

**Fantasy Football for Smart People: A Guide to Winning at Daily Fantasy Sports**

Jonathan Bales

Copyright Jonathan Bales 2014

Published at Smashwords

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All Rights Reserved ©2014

Jonathan Bales. First Printing: 2014. The editorial arrangement, analysis, and professional commentary are subject to this copyright notice. No portion of this book may be copied, retransmitted, reposted, duplicated, or otherwise used without the express written approval of the author, except by reviewers who may quote brief excerpts in connection with a review.

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Table of Contents

Fantasy Football for Smart People: A Guide to Winning at Daily Fantasy Sports

**Section I: Chapters from** _Daily Fantasy Pros Reveal Their Money-Making Secrets_

The Vegas Lines with Mirage88

Projecting Players with MrTuttle05 and Dinkpiece

**Section II: Chapters from** _Fantasy Football (and Baseball) for Smart People_

One-on-One: How to Win Heads-Up Leagues (and 50/50s)

The Final Piece of the Puzzle: Creating Projections and Lineups

An Appendix of Extra Data

**Section III: Chapters from** _The Ultimate In-Season Weekly Guide_

How much do matchups matter?

The Fourth Quarter, Vegas Lines, and Passing Stats

How to Treat Backup Running Backs Who Become Starters

Does playing in a dome help an offense?

Demaryius Thomas, Andre Johnson, and Opportunities to Score

A Bunch of Fantasy Football Data

## Preface

A couple years ago, I was playing daily fantasy football semi-seriously (but still more as a hobby than a full-time job) and I was in position for a major cash. With one game left in the NFL slate, I was the favorite to win a huge tournament on DraftKings with a prize of $100,000 to first place.

And I blew it.

About 15 minutes before kickoff, I swapped out Eric Decker and put in Keenan Allen. Knowing that I had a serious shot at some big-time cash, I was doing as much research as possible to project these two players. Right before the game began, I second-guessed my swap to Allen and pivoted back to Decker. The pretty boy went on to catch two of his five targets for 32 yards, dropping a touchdown and literally falling down in the open-field while running for another. Allen went for 6/142/2.

I won $1,500—roughly $98,500 less than I thought I was going to win—and I didn't sleep for days.

At that point, I realized that the difference between the elite daily fantasy players—the guys making literally hundreds of thousands and millions of dollars per year playing fantasy sports—and average players is the sum of a bunch of small edges. Had I known more about the progression of rookie wide receivers (something I studied literally a week later), I would have stuck with Allen (which I now believe was the right move). I set out to take daily fantasy sports more seriously, doing as much research as possible and talking to as many pros as I could to see if I could do this for a living.

Fast-forward to the present day and, well, I'm getting there. I scored my first six-figure cash in a DraftKings fantasy baseball tournament (through a combination of getting lucky and swapping equity with a few players who performed rather well) and I'm grinding it out every day to continue my success.

The way that I got started in the fantasy sports industry, though, was through education—teaching players how to become better and even profit from their hobby, and that's something I'll continue to do as long as I can. My Fantasy Football for Smart People book series has seen multiple books become the #1 football book in the world at one point or another.

I'm also now a sponsored DraftKings Pro. I produce content on the site and help spread the word that DraftKings is the best daily fantasy sports site in the world (it really is).

As a full-time fantasy sports player and writer, I've had a ton of time to dick around, research weird topics, and write a whole bunch about stuff I love. I expanded my book series quite a bit in the last 12 months—so much so that I wanted to create a "Best Of" book. That's what this is: the best of my daily fantasy football content from the past year. My hope is that you'll find some of the advice interesting and actionable as you play on DraftKings this season.

Note that I also sell a season-long draft guide and in-season package (complete with weekly projections and values for daily fantasy sports sites). The goal is to help you become a profitable fantasy sports player over the long run. All of my packages and books are available at FantasyFootballDrafting.com.

Thanks so much for your continued support of this series. Best of luck this year.

  * Jonathan Bales

## Some Free Fantasy Football Stuff for You

I like giving things away, so here's some stuff for you. The first freebie is 10 percent off anything you purchase on my site—all books, all rankings, all draft packages, and even past issues of RotoAcademy—my fantasy football school. Just go to FantasyFootballDrafting.com and use the code **"Smart10"** at checkout to get the savings.

The second freebie is a download of my book _Fantasy Football for Smart People: Lessons from RotoAcademy (Volume 2.0)_. Yes, the entire book for free.  Here you go.

Finally, I've partnered with DraftKings to give you a 100 percent deposit bonus when you sign up there. Deposit $500 and then bam! you got $1,000. DraftKings is the main site where I play daily fantasy football. Deposit there through one of my links (or use <https://www.draftkings.com/r/Bales>) to get the bonus, use the "Smart10" code to buy my in-season package at FantasyFootballDrafting.com (complete with DraftKings values all year long), and start cashing in on your hobby.

A whole lot of readers profited since I set this up, and we now have _five_ six-figure weekend profits by subscribers since purchasing my in-season package. Yes, over $100,000 in one weekend of football—on five occasions! There's an outstanding investment opportunity in daily fantasy sports right now, and there's really no reason for you _not_ to get involved.

# Section I: Sample from  Daily Fantasy Pros Reveal Their Money-Making Secrets

 Fantasy Football for Smart People: Daily Fantasy Pros Reveal Their Money-Making Secrets **is the first book to truly dig deep inside the minds of daily fantasy football's most lucrative players—the ones raking in full-time salaries playing the game you love. With interviews from headchopper, Al_Smizzle, PrimeTime420, dinkpiece, naapstermaan, MrTuttle05, and others, you'll learn exactly how the experts go about researching, projecting players, and creating their daily fantasy sports lineups each week.**

**In addition,** **Daily Fantasy Pros Reveal Their Money-Making Secrets** **contains chapter-by-chapter commentary and analysis from author Jonathan Bales and Top-10-ranked daily fantasy pro Peter Jennings, a.k.a. CSURAM88. With unprecedented access to the strategies used by the world's top players, you'll learn how professional daily fantasy footballers are** **really** **cashing in...and how you can too.**

## The Vegas Lines with Mirage88

I'm a huge proponent of "stealing" research from Vegas by looking at their game lines, spreads, totals, prop bets, and so on. These are people who have millions of dollars on the line with each game, so creating an accurate line is important to them. It's not that we can't know that the Broncos are going to score a lot of points without looking to Vegas, but rather that the lines allow us to 1) quantify the effect and 2) do it in a really efficient way so we can spend precious research time elsewhere.

I spoke with Mirage88—one of daily fantasy's up-and-coming players—about his use of Vegas. Mirage88 is one of the smartest people I've spoken to about daily fantasy sports. He's also my favorite daily fantasy success story.

Within two weeks of learning about daily fantasy, Mirage88 qualified for a daily fantasy football championship and then won $25,000 just one month after that. A few months later, he went on a two-week heater that included a six-figure profit.

Currently ranked in the top 30 in NFL and top 12 in TPOY, I'd argue that Mirage88 is one of the top 10 daily fantasy players in the world.

### First, talk to me about the Vegas lines and how they're created.

I think it's important to understand how the Vegas lines are created, which then aids us in figuring out how useful they are. There's a perception that Vegas sets lines solely to get 50/50 action on each side of the bet. And to some degree they probably want that in many situations since they'll guarantee themselves profit just from the juice (the commission they charge to play). But what happens is people will sometimes use that as a reason that Vegas shouldn't be used in projections, saying something like "Oh, they just care about whatever popular opinion might be and just getting in the middle of that."

The problem with that is that there are a lot of sharp bettors out there with a lot of money, so if Vegas indeed produces a line to equalize bets but it's weak, those sharps are just going to pound that bet and Vegas will be in a really poor situation in terms of expected value.

So the way I like to think about Vegas is that it's really where the most risk is in terms of projecting any player results—at least the most financial risk from one entity making projections, anyway. So if Vegas posts a poor line—let's say they post a total that's way too low—then all of a sudden anyone who can bet on that who is relatively sharp will just start hitting the over, and Vegas will realize that the bet isn't really balanced.

Vegas will compensate for that by moving the total up to get more action on the under. That's fine, but then there's this area in the middle which was over the initial line but under the new line movement bet that's now a really bad place for Vegas. If the game ends up in that spot, they could theoretically lose a whole lot of bets to sharps who bet the original over, but at the same time lose late bets that came in on the under when the total was higher.

Vegas doesn't want to put themselves in that position where they can be arbitraged, so it's really important for them to create an accurate line from the start. Even if they don't guarantee a profit by getting equal money on each side, they can limit their downside—their risk of ruin—by making the line accurate. They really don't want to be in a situation where they set a bad line that moves a whole lot and they could potentially lose their share on both sides of a bet.

So to be clear, Vegas needs to set accurate lines to not only ensure that they get equal money on both sides, which they will, but so that _actual results_ fall on both sides of the bet 50 percent of the time over the long run as well. It's okay if they get 70 percent of action on one side of a bet and 30 percent on the other if, over time, the actual results are falling half over and half under—meaning Vegas is setting accurate lines.

Ultimately, making accurate lines is just a safer way for Vegas to make money than trying to predict public opinion, especially when there are sharks out there who might not agree with public opinion. Vegas has a very clear financial incentive to make accurate lines, and they do. So that's my little rant on why we can trust the lines and why the idea that all Vegas wants is to balance bets is false.

### How do you personally use the lines in your daily fantasy projections?

I personally use the lines whenever there aren't time constraints. So if the lines come out a couple hours before a game, that's a little difficult to fit into a model to make projections and still be able to create lineups and all that. But any Vegas line that comes out early enough that I think relates to something that I'm trying to project gets put into my model.

In football, I'm usually looking at projected totals the most. The easiest way to use over/unders in football is to look at the total and the spread and calculate the projected total for each team. You can do that pretty easily on your own, but I go to RotoGrinders for that info to get it really quickly.

Once I have the total for each team, I look at some historical scoring rates—what percentage of scoring has typically gone to each position for certain teams. So let's say we're looking at the Packers and the Giants, who Vegas has projected at 24 points each. By looking at historical scoring, we'd see that we should project Aaron Rodgers with more touchdowns than Eli Manning just because Green Bay scores a higher percentage of touchdowns to field goals, and Rodgers also accounts for a much higher percentage of the Packers' scores than Manning for the Giants, even with the same projected points.

You need to be careful there, too, because there can be a lot of turnover in the NFL, so things change. For example, the Giants have a new offensive coordinator and an entirely new offensive philosophy, so that data on how their touchdowns are usually allocated might change. For the Packers, on the other hand, we can pretty much assume the same scoring rates since not much has changed for them in terms of coaching or personnel.

I think that's also a good example of projections sometimes being really scientific and other times being more of an art. With Green Bay, I'd be more likely to rely on the numbers; I can look back at however many years the same sort of scheme was in place and say, "Okay, 20 percent of touchdowns go to Jordy Nelson, 35 percent go to running backs, and so on."

You can actually do the same sort of thing when projecting kickers, looking at a combination of the line and then what percentage of points the kicker has produced, assuming there haven't been giant shifts in offensive philosophy or personnel.

After that, you still need to adjust for other factors, specifically the opponent. Maybe the Packers' wide receivers account for 50 percent of all touchdowns, but they're facing a defense that has really short cornerbacks who get picked on in the red zone, so they allow 65 percent of touchdowns to opposing receivers. Then you'd expect an even greater rate of the scoring to come from Nelson & Co.

But that's the general idea behind what I do to at least get a baseline projection.

### Do you study only totals? How about player props?

I don't use props, but not because they aren't useful. They have the same predictive power of any other number put out by Vegas. The main reason I don't use the props, though, is that they tend to come out pretty late in the week, so that doesn't leave much time to get them into my projection model. I can pretty much calculate projected touchdowns, especially, from using the total and past scoring rates alone, so I can do that earlier in the week when the lines are posted. That's a way to basically get most of the way to creating the props without the props actually being released.

The other thing is that I think there's something to be said for simplicity in a model. It's really important to understand everything that goes into your model and how it affects the projection, and sometimes it doesn't make sense to have all these little minute details coming in from 50 different sources.

So I don't know if I'd make player props a major component of my model even if they did come out earlier in the week, just because it's important to understand what's driving your model in order to improve it. If you have a bunch of different components in your model and it doesn't work, it's going to be really difficult for you to figure out why and make a change. A huge part of being a profitable daily fantasy player is about improvement, so you need to know where your projections are coming from and how they can be enhanced.

### What percentage of players do you think look at the lines? How does that affect their worth?

I'd say that almost all high-volume players are looking at the lines in some form or another. Some put more weight into them than others, but if you're a successful daily fantasy player, I'd be surprised if you aren't looking at the lines at all.

In terms of the overall player pool, though, I think it's probably still a tiny percentage, which adds to their worth. I think there's a ton of value in using the lines, especially in cash games, because in head-to-head or 50/50 matchups, for example, you're just looking to figure out the most likely thing that's going to happen and use that to beat the average player. Using the Vegas lines is a really accurate way to accomplish that.

In cash games, I think Vegas can really help with your own projections. In tournaments, I think the biggest value from the lines comes in using them as a prediction market for ownership. So the higher the over/under on a game, the more player utilization there will be in those games. Even if the general public isn't using the Vegas lines, they still have a sense of which games are going to be high-scoring, so Vegas can act as confirmation of where there's going to be heavy player usage.

That's important because, unlike in cash games, it's important to have a unique lineup in tournaments. So if there's a game that's an outlier in terms of the projected total, just way ahead of everything else, it's kind of hard to recommend players from that game because they're going to be so popular. That doesn't mean I never use players from the highest-projected game in tournaments, but if I do, I need to create some elements within my roster that I think won't be as common elsewhere. It's not that you can't win by using all highly utilized players, but just that it can improve your tournament odds by adding at least some contrarian elements into your lineup when you otherwise go with the chalk.

### How do you know when to go against Vegas and when to play the chalk?

I think this is one of the aspects of daily fantasy that's still more art than science. My general philosophy, at least in tournaments, is to take the best possible players who I think won't be really popular choices for the rest of the daily fantasy community.

So let's say that the Broncos are projected way ahead of everyone else in a given week and also have some attractive salaries, to the point that we pretty much know Denver players are going to see extremely high usage. In that situation, I tend to look at the next few highest-projected teams and then try to think about which ones won't be very popular—maybe they aren't getting a lot of buzz or they play in a small market—and try to target players on those teams. Even if they aren't projected quite as high as the Broncos, you make up for that by creating a lineup that doesn't resemble many others, whereas maybe 30 percent of the field is creating very similar Broncos-centric lineups.

So in a way, I'm still playing chalk in that I want them to be projected to score a lot of points, but just that I care about how popular I think a team's players will be, too. It's also an experience sort of thing where you'll get better as you play in more tournaments.

Another time when I like to go against the top-projected team is when I see certain players on poor teams come out as really good values. Maybe there will be a case where a team isn't projected to score that many points, but their quarterback and top receiver are likely to account for a huge percentage of their overall yards and touchdowns. I love to target those situations because I can still get value with the projections, but I know the public won't be on them because they aren't projected that high as a team. So again, I'm just using the Vegas totals as a proxy for daily fantasy ownership.

### What percentage of your model is composed of Vegas-based data?

It depends on the sport. I think Vegas is a really powerful tool, but it's just one of many projections I use. One of my goals is to find as many projections from as many smart people as I can find and just aggregate them. So I make projections myself, I use Vegas, I look at sites that I trust, and I factor all of those into my model.

As long as I think the projections are coming via a quality process, I love to just aggregate a whole bunch of information to factor out as much bias as I can, whether it's my own personal biases or individual site bias. So Vegas might be just one-fourth or one-fifth of my model, although I do change the weight I place on each source as I see fit.

The bigger picture is that I really am a huge proponent of aggregating data from a few trusted sources. I think it's the easiest way to build an accurate projection—the "wisdom of the crowd"—and Vegas is a big component of that, although certainly not the only source I rely on.

### Which aspects of daily fantasy projections aren't priced into the Vegas lines?

For the most part, I think they're going to do a really good job of capturing most relevant statistics. Again, there's so much money on the line that it's hard to believe there's a really powerful predictive measure that they aren't considering.

But if something isn't priced in, it's factors that they aren't aware of when they set the line. The biggest example of that is weather. Vegas posts lines early in the week without full knowledge of weather conditions days down the line, so that's something that certainly affects player production but won't be a major component of the early lines. The classic example is a 2013 game between the Eagles and Lions that had a huge over/under that was posted on Monday—a total that needed to be adjusted considerably because forecasts were calling for a massive blizzard during the game.

It's not just Vegas that doesn't initially account for weather, though. Most projections aren't going to have weather as much of a component to start just because we can't really know how the weather is going to look during a game days in advance. So weather is a really, really important aspect of projections to monitor.

Injuries are another aspect of daily fantasy production that might not be priced into the Vegas lines when they first come out. Let's say Aaron Rodgers is questionable but expected to play in a given game, but just before kickoff he's ruled out. Vegas might have set the total as if Rodgers were going to play, but that's going to be altered significantly if he can't go, which will of course extend to projections for his teammates as well.

### Do you ever study line movements? How might those be useful?

I don't think the lines move all that much in most cases because, like I said, Vegas generally posts strong lines. When it does move a lot, I think that's really good evidence of strong public perception in one way or the other.

That has the most use in tournaments because if you see the public strongly moving in one direction, that should probably be a reason for you to move in the other. I think you want to avoid following the public when a line moves because basically that's a case where the general populace likes a team more than Vegas does. We can usually equate the general betting populace to the average daily fantasy player, so when a line moves a lot in a particular direction, it's a decent sign that a lot of users are going to be higher on certain players than Vegas would be—and thus their ownership will be higher than it should be.

Whether or not I target players in a game that moves a lot depends on which direction it moves. If a game moves up, it means that there's probably going to be heavy usage on players that Vegas doesn't like as much as the public, and that's a situation to fade. If a line moves down, though, it means Vegas is higher on a team that the public, and that's often a situation to target because you can get value on players who are unlikely to be heavily utilized. That's not a hard-and-fast rule that I use—again, it's still more of an art than science at this point—but it's a good rule-of-thumb regarding value and ownership percentages.

### Tell us about a time when you successfully leveraged Vegas.

I have an example from MLB. After completing my baseball projections for the day, I ranked my top teams to stack simply by sorting average fantasy points per batter on each team. I saw that the Cubs were the sixth-highest total out of 20 teams in my model, but had the third-lowest projected total in Vegas. The downside of that over/under was already included as an input in my projection, so I felt good going with the Cubs despite the low projected total.

I also got a sense that the opposing pitcher, Tyler Lyons, was going to be highly owned based on industry articles and podcasts recommending him. Given that Lyons isn't an ace pitcher and my model had batters with a decent projection against him, it was a perfect opportunity. I got to stack a low-ownership team while getting double the value, as the poor performance of the opposing pitcher would sink a lot of my opponents' teams. Regardless of outcome, that's the type of shot I love taking in big tournaments, because when they work they win big. In that case, I had three Cubs stacks finish 1-2-3 due to the contrarian nature of the picks which almost no one else had.

## CSURAM88's Analysis

I'm a huge proponent of using the Vegas lines for information because they've proven to be so accurate, and Mirage88 did a great job of explaining why that's the case. One thing the lines allow me to do which is different from some other players is target teams over players.

That is, I look at the Vegas lines and use those to help me figure out which teams are going to be able to score a lot in a game. Then, I try to predict which players on that team are going to be the main beneficiaries of that. That's in opposition to some other daily fantasy players who start by looking at individual players.

Overall, though, I am 100 percent a Vegas-based daily fantasy player; I use the lines as a very strong foundation for my projections and lineups, and I don't think there's a more efficient and useful way to go about playing daily fantasy. In addition to game totals, I look at team totals, line movement, player props—anything I can get my hands on.

## Jonathan's Analysis

I've done a decent amount of research on the effect of Vegas in daily fantasy. My personal belief is that the lines are particularly useful for running backs. If we look at the relationship between projected over/unders and quarterback fantasy points, there's a weak relationship.

It strengthens a little if we look solely at projected team totals, which is obviously recommended, but it's still not nearly as strong as the relationship between projected points and rushing yards. Check this out.

Teams projected to score 30 or more points in a game rush for around 14 percent more yards than those projected at 21 points and nearly 33 percent more than those at 14 or less.

So why are the Vegas lines a better proxy for rushing totals than passing? Think about game scripts. When NFL teams win, they normally do it because they pass the ball efficiently. It's not always the case, but frequently a team will pass the ball well, gain a lead, and then run out the clock. Meanwhile, the opponent probably didn't pass the ball very efficiently, faced a deficit, then made up for the poor passing efficiency with more late attempts.

Thus, passing stats often converge, with winners racking up fantasy points via efficiency and losers doing it with more attempts. On the flip side, rushing the ball is more correlated with winning than passing it. Note that I'm not saying rushing the ball often is a _cause_ of winning; NFL coaches have made this mistake in the past (and still do), running the ball way too often because "hey, that's what teams that win do." Yeah, no shit, because they're _already winning_.

You can of course use Vegas props to project quarterbacks and receivers, too, and I also think the game location is important. Specifically, I like to target quarterbacks who are playing at home. Take a look at their passing efficiency over the years.

There are probably a lot of reasons for this effect, but ultimately there's a lot of value in targeting quarterbacks on home underdogs. First of all, they're likely to have greater efficiency than on the road. Second, they're probably going to throw the ball often as an underdog. Third, they're far less likely to be conservative early—something that coaches who are pussies still do on the road way too much—so you have the perfect storm of throwing the ball often _and_ well.

## Projecting Players with MrTuttle05 and Dinkpiece

There's no area where daily fantasy players want to improve more than in creating projections; if you can create accurate projections, you can make money playing daily fantasy sports. I spoke with two top-tier players in MrTuttle05 and Dinkpiece regarding their projection methodologies.

MrTuttle05 is the co-founder of FantasyInsiders.com and FantasyPros' 2013 Daily Fantasy Accuracy champ. He has qualified for the DFBC (three times), FFFC, DFFC, PFBC, and finished second in the PFFC. He's also currently ranked by RotoGrinders as one of the top 10 NFL players in the world.

Dinkpiece founded MyFantasyFix.com and joined RotoExperts in 2013. That same year, he left his job as an investment analyst to play daily fantasy sports full-time, and his transition was featured by  The Wall Street Journal. Dinkpiece was the 2012 Super Joust champ, along with a finalist in the DSFC, DSBBC, DSBC, PBC, and 2014 DraftKings Showcase.

Dinkpiece's ability to win across a variety of sports is amazing.

Currently ranked No. 7 in the RotoGrinders Tournament Player of the Year rankings and No. 4 overall, Dinkpiece is a top 12 NFL player.

### What are the general steps you take in projecting players? Is it more subjective or objective?

**MrTuttle05:** I'm probably a bit more subjective in regards to football research. I don't use a model or anything like that to project players. I look at some other data-driven projections from various sites because I think that can be helpful, but I don't usually do that until after I've performed my own research and figured out who I like on my own. I want my thoughts to be my own, so I tend to steer clear of that stuff until after I've picked a general pool of players that I'm going to target.

After I've decided who I like, I'll look at some others' stuff and then see where we agree and disagree. When I'm in agreement with others' work, that's kind of reassuring to me and lets me know that there are probably reasons to like a certain player. That doesn't mean I won't play someone who other people dislike, but just that there's value in a consensus opinion.

**Dinkpiece:** My research for NFL really starts with injuries. I'm looking at news throughout the week and waiting for the injury reports, so from that standpoint, it starts out qualitatively. I've run some numbers on players who are listed as 'questionable' or worse and still play. The numbers aren't overwhelming overall, but for certain positions, there are drops in production and so you really need to pay attention to the injury status. Even if a guy plays, you might want to fade him depending on his position and the injury.

The next step for me is far more data-driven. I start by looking at consensus rankings. FantasyPros is one of my favorite sites because you can find aggregate expert rankings, which is really useful. So that's just a great place to start to get an idea of how players should be ranked and projected.

The next thing I do is start to get a sense of opportunities. I look at various team-driven stats to see what type of run/pass balance I can expect from each team in every game. That includes not only their own run/pass balance, but also the run/pass balance allowed by the defense. Sometimes you have situations where a team runs the ball a lot, but mostly late in games, so if they're expected to be trailing, they probably won't be running as much as normal. But the opportunities are so important for fantasy football, so that's really where the majority of my research is directed—looking at how defenses are attacked, how teams like to beat them in the red zone, and so on.

Another thing to understand is how one particular aspect of a defense affects play-calling against them. Sometimes, certain defenses can be so good against the run, for example, that opposing offenses throw the ball a lot more than normal. That defense's actual passing defense might still be good, but they'll frequently give up decent passing numbers just because they get thrown on so much. If that's a team that gets passed on a lot in the red zone, too, I'll target quarterbacks/receivers against them, assuming I'm projecting a specific type of game script that will allow for favorable passing situations.

Ultimately, what I'm trying to do is use consensus rankings as a foundation and then alter those based on opportunities, which I think most players weight too lightly. That helps me create a projection for wide receiver targets, running back touches, and so on. For running backs, I look specifically at red zone carries; touchdowns are so important for backs and they're easier to project based on offensive philosophies and defensive weaknesses.

### Dinkpiece, you mentioned FantasyPros as a source for aggregate projections. Do you think certain people can beat a consensus of experts long-term?

**Dinkpiece:** Yeah, I use the aggregate projections, but I also think certain people can indeed beat the consensus long-term. The reason is that there can sometimes be a herd mentality in expert projections and rankings that creates inefficiencies. So if 95 percent of experts aren't considering a factor that's actually quite important, that's a chance to beat the consensus.

However, I think the number of people who can actually beat the consensus is very low, and I also think you should approach projections as if you _can't_ beat the consensus. I personally don't act as though I can beat FantasyPros' rankings on my own because that could actually put me at a disadvantage in terms of placing too much confidence in my projections. I use the aggregate to find areas where I might be overlooking a player, or just to see where the majority of people are headed in a given week in terms of player utilization.

### Which stats do you consider most when projecting for NFL?

**MrTuttle05:** I think this is probably where I differ a lot from the typical daily fantasy football player. Basically, when I'm projecting NFL, I'm concerned less with finding a bunch of different stats and more with projecting opportunities. Obviously matchups matter in the NFL, so that's something to consider, but I really do that stuff secondary to figuring out how many chances players will have to make plays. That's really where fantasy points are scored; the matchups don't matter if a player doesn't get enough opportunities.

So the foundation of my research is looking at stats that are going to help me predict if a player will at least have the opportunity to produce. That means targets, red zone work, and stuff like that. For wide receivers, I'll look at both projected targets and a recent history of targets, as well as the percentage of his team's targets that he's going to see.

Then I can start to deduce a workload. If I think a team will pass the ball X times and a particular receiver will normally see 20 percent of those throws, I'd project him around X * 0.2 targets for that game. It's the same sort of deal with the other positions.

One of the reasons that I start with opportunity, in addition to it just being really important, is that it's also really predictable. I can try to project touchdowns, but those are really volatile from week to week. If I'm continually finding players with lots of opportunities to score touchdowns or otherwise make plays, then I'll eventually hit on those.

So basically I use opportunity stats to weed out players and find a core group who I think will have lots of chances to score in a given week. From there, I then move onto matchup stats—defense vs. position—to figure out which of those options is in the best spot to really do something with his touches.

**Dinkpiece:** Again, it all comes down to opportunity stats—targets, attempts, carries. There are a lot of really great rate stats out there, and while I consider those, they don't get too much weight in my projections. In the NFL, the samples are so small, so it's difficult to always trust those rate stats. What happens is that you often see a game or two really influencing the overall data too much. I'm more concerned with figuring out how many chances a player will have to score a lot of points and then using an adjusted baseline efficiency rate to finish the projection.

One rate stat that I do like to use is anything related to cornerback strength. I look at how well cornerbacks have played both historically and in the recent past. I get most of that data from Pro Football Focus, and it's just a good way to see who is struggling and which cornerbacks are being tested.

Another rate stat that I'll look at is the percentage of a team's workload a player receives, which is related back to opportunities. I look at the percentage of red zone targets each player receives, for example, as a way to determine who's most likely to get chances to score.

A final rate stat—again one related to opportunities—is the overall success rate of an offense. How often do they convert first downs and sustain long drives? I want efficient offenses just because they're more likely to stay on the field and run a lot of plays.

The game script is always important in predicting workload, too, so that's one area where I really focus. I look at the Vegas lines and just think about different ways a game could unfold; the score obviously has a huge effect on play-calling, so understanding and accurately predicting that is important. A lot of times, I'll target quarterbacks who are either going to be in a close game or even losing in the game (preferably a high-scoring one).

The final thing I want to say with opportunities, and this is kind of related to game scripts, is that I want to project not only the workload, but also the consistency of the workload. Some players have games where they'll see a huge workload and others when they barely see the ball. C.J. Spiller is a player like that who I might use in a tournament, but I'd never use him in a cash game just because you can't rely on him seeing a significant number of touches.

I know I'm harping on opportunities a lot, but it really is a huge thing. If you can do the research to add just a couple targets per receiver or a few carries per running back, that's going to do wonders for your daily fantasy success.

### How about projections kickers and defenses?

**Dinkpiece:** At kicker, I'm generally looking among the cheapest options because they're so volatile, but there's still some nuance to it. A lot of people say they look for the cheapest kicker on a high-scoring offense, but I don't want the offense to be too efficient or else they'll steal field goal opportunities.

I like to target kickers on teams with decent offenses but maybe those that aren't that efficient in the running game, specifically in short-yardage situations. Those teams tend to pass the ball more in the red zone, and passing produces binary outcomes down there; it's usually either a touchdown or not, which sets up longer third downs and can result in more field goal tries. If you can identify an offense that can move the ball up the field but might struggle punching it into the end zone, that's a good opportunity for points from short field goals.

For defenses, the main thing I look at is the opposing offense. I want defenses facing offenses that give up a lot of sacks, which usually results in a lot of turnovers. The defenses playing on heavy favorites are usually good plays because when they're playing from ahead, they get way more opportunities for sacks and takeaways against an offense that's dropping back to pass on every snap. The perfect storm for a defense is facing an offense that either wants to or must pass the ball a lot, but is inefficient in doing so.

The other way to pick a defense that I see more often in cash games is to go super-conservative with one in a game that's projected to be really slow-paced. I wouldn't recommend that strategy in tournaments because you want upside from sacks and takeaways, but targeting defenses in games projected to be slow-paced without a ton of plays—or a lot of running plays—is a safer option to limit downside. I tend to be more aggressive with my defense selection, however.

I tend to go pretty cheap with my defense, but that's not always the case. There are situations when I'll pay for a defense that's in a really good situation against a turnover-prone quarterback, but in general, I allocate a low percentage of the salary cap to my defense.

### How much variance do you think there is week to week? Do you place more importance on the player or the matchup?

**MrTuttle05:** There's a good amount of variance each week in the NFL. In general, basketball is the most consistent sport on a nightly basis, so you can predict production there a lot easier. The players have lots of chances to get rewarded for their performance. It's not like that in football, so like I said, I'm just concerned with figuring out opportunities.

In doing that, I'm more concerned about the player than the matchup. The matchup matters, but it starts with making sure you're in a position to expose your lineup to lots of potential scoring. A player like Darren Sproles is an example of someone I don't normally roster. Even when he has a good matchup, his ceiling is limited, even in PPR leagues, because he rarely sees a heavy workload. So even against defenses with poor coverage linebackers who won't be able to keep up with Sproles, I'd be more likely to avoid him than the average daily fantasy player due to the lack of projected touches.

The only time I might go away from that a little is in tournaments just to go against the grain. Sometimes a player like C.J. Spiller can hit without a lot of touches, even if it isn't an optimal situation from an opportunity standpoint. In terms of my cash games, though, I wouldn't take a chance on a player like that.

**Dinkpiece:** This is a great question. I've done some research on how much the matchup matters at each position and, in general, the matchups matter _a lot_. When a player faces a bottom 10 defense, he has a 75 percent chance or more to outperform his normal production. That's a really high rate of success and suggests that the quality of the defense is probably just as important as the player in question.

The problem is really knowing how strong an actual matchup might be. Since there are such small samples in the NFL, you can get fooled into thinking that a defense is much better or worse than what's actually the case. For that reason, I think a lot of the defense vs. position stats aren't very useful until once we approach midseason. Before that, you're better off using a combination of this year's stats and last season's data. I personally just weight the defense less early in the season and focus more on player skill and opportunities. That's especially true if a defense has a lot of turnover in terms of personnel from one year to the next.

With respect to variance, there's a ton of it in football. It's very much like baseball in that a single play can make a player's day. A long fly-ball that gets held up in the wind and gets caught at the warning track can be the difference between winning a tournament or not, just as a single long touchdown can dramatically alter results.

The other thing that causes excess variance in the NFL is the injury risk. Throughout the course of the year, you'll have times when you have players leave the game because of injuries, and that's just not something you can predict. That adds a level of variance that the other sports don't have as much. It's important to try to account for that variance in your picks, especially knowing that it might not wear out over the course of such a short season.

### Do you do median projections or ceiling/floor?

**MrTuttle05:** I typically do a mental ceiling/floor projection. I think that has a lot more use than a median projection because you're typically concerned with either maximizing your floor or ceiling in a given week, depending on the league. So in cash games I want safe players and in tournaments I want guys with huge upside.

Lots of times the high-upside players also have high median projections, but that's not always the case; sometimes, you have players who are normally mediocre, but when they have a big game, it's _really_ big. A median projection would probably lead you in the wrong direction in that case.

**Dinkpiece:** I know a lot of other good players approach it differently, but I tend to focus mostly on median projections because I play in a lot of cash games, so my concern is mostly on median value. When I enter large-field leagues, I obviously look more at ceiling projections, but the majority of my research gets placed into median projections.

### How much weight do you place on early-season results?

**MrTuttle05:** It really depends on the situation, but normally I don't place too much emphasis on the early data because it can just be misleading. You might have a player who has started the year on fire, but the stars aligned for him to do that. Or maybe you have a struggling stud who just hasn't gotten going because of matchups. Those matchups even out for the most part by the end of the year, but over the course of a few games, they can play a huge role.

I don't really care about the statistics, but I do look at other things like the roles guys are playing, how offenses/defenses are being run, how new coaches are calling plays, and so on. The beginning portion of the year is especially useful for players in new situations so you can get a feel for how they'll be used. That won't necessarily be reflected in bulk stats, but it's important to look at that stuff.

**Dinkpiece:** Like I said in regards to defenses, I don't place too much emphasis on early-season results. Over the course of just a few games, you'll see a lot of variance; maybe a defense looks good but just had three really easy matchups. I'll use some scouting services like PFF to help early in the year to try to decipher which teams/players are really playing well, but I mostly wait until around Week 7 or so to really start focusing on that data more heavily.

### How much do you use Vegas in your projections?

**MrTuttle05:** I don't use Vegas as much as some other players, but I use it a lot in accordance with weather. I'm not a meteorologist and I can't really predict how certain weather is going to affect scoring with much accuracy. I know which teams and players are the most likely to score the most in a given week, but I can't tell how much the wind is going to affect passing, for example.

So I look at the Vegas lines, especially right before the games when we have a really good sense of the weather, to see how the experts are projecting the conditions to affect scoring. If there's a lot of movement in one direction or the other before the game, that's important to me. There was a game in Chicago a year or two ago when the wind was just out of control, and both the line and passing player props dropped considerably. That's an example of a situation when I'll be likely to lean on Vegas heavily.

### How much of a factor does weather play for you, Dinkpiece?

**Dinkpiece:** I actually don't care that much about weather; I look at wind the most, and really just extreme wind conditions. I think people get scared away by rain or snow, but as long as there aren't heavy winds, I don't think an offense is really at a disadvantage. It could actually give them an advantage since they know where they're going on each play and the defense needs to react.

One time that precipitation might play a bigger role for me is if I think the offensive coordinator is going to change play-calling because of it. So if I like a receiver in a snow game but I think the team is going to run the ball way more and cut down on his opportunities, then I might stay away from him because of that.

I also consider weather for kickers. I don't want them in very cold or windy weather because the ball doesn't carry very far and teams are less likely to attempt field goals.

### What do you think are the advantages/disadvantages of a value-based projection system, such as $/point?

**MrTuttle05:** Because of the volatility in NFL, I don't create a value threshold for each player that I'm looking to reach. That's something that's useful in basketball where the production is pretty consistent from night to night. In football or baseball, that's not the case and the values aren't as important.

It's not that the values are meaningless or can't help, but there's just so much variance that it isn't a great use of time. You can use up a lot of time creating values and then you might move down your value list quite a bit to select the players you like.

The other thing is that most $/point or value systems are based on median projections. Like I said, I'm more concerned with high/low projections for players—pure risk and reward—so the median values aren't that useful for me.

**Dinkpiece:** I don't follow $/point or other value systems religiously. I look at $/point just as I look at aggregate projections to create a baseline, but I think there's a problem with following values in a strict way because it's fragile.

Like I said, there's so much value in touchdowns or even long offensive plays in the NFL that they can totally change a player's game. You end up needing to project players very accurately—with fractional touchdowns, for example—to such a degree that you can't really be confident that the projections are right.

It's not that they have no value, but you can't blindly follow them because there is a really large margin for error. One defender falling down can totally change results, for example, and there's no way to accurately predict that.

I think the biggest value with $/point systems comes in identifying players who are obvious poor value. So I'll generally stick to players who come out near the top of the value systems or in the consensus projections, but I don't place so much emphasis on it that I'm always taking the top guys.

I prefer ranking systems. I sort players into tiers and then try to get as many high-tier players at each position as I can. I think that's the best course of action just because very specific projections place too much emphasis on fractional big events (touchdowns), and it's hard to evaluate those properly.

### Once you finish, how do you optimize your projections or rankings to create lineups?

**MrTuttle05:** The first thing I do is go onto the sites and look up player salaries. I already know who I think will do well in a given week, but I also want to know how much they cost on each site. That helps me not only identify undervalued players, but also know where they're the cheapest and where I should play them most in a given week. So if I like Dez Bryant and he costs 90 percent as much on one site as another, I'll of course try to get more exposure to him on the site where he's cheapest.

I cross-reference the names that stand out to me from a salary standpoint on each site with the players I have identified as guys to target from an on-field standpoint, and then I can get a sense of which players are going to offer value and where they're the most valuable.

**Dinkpiece:** "Optimize" is a buzz word for me; I don't really like it because I think it implies a level of precision that just doesn't exist. If you're going to be able to create a _true_ optimal lineup, the projections need to be totally accurate, and again, I don't think that's possible.

I look through my tiers and compare the rankings with pricing to create the best lineups. I think that when you start to rely too heavily on $/point, you aren't putting your own stamp on your lineups; you're valuing the system over your own thoughts, which isn't always the best move.

I personally like to have a combination of both methods, using my own rankings alongside $/point and consensus projections. I think that's the best way to get the best of both worlds and create lineups that aren't so fragile as to blindly follow a projection system, but not totally subjective, either.

## CSURAM88's Analysis

Great thoughts here from two of the top players in the game. I think projections tie in with the previous two topics—variance and the lines. Like I said, Vegas should be part of any projection model, but really understanding week-to-week variance can help you make better projections; people are so quick to overreact to results, especially in football because there's not much else to analyze. I think that's really the best way to get value in players, really—zigging when everyone else zags, jumping on players who people are down on (or those who have cheap salaries) because of factors that are just due to variance.

I also agree that daily fantasy players need to be careful when trying to translate their projections into lineups. Projections can be valuable, even if it's only because of the research you put in to complete them, but there's still a lot of subjectivity when trying to pick players from a list of values. There are so many players that there are naturally going to be a handful ranked very closely to one another, so you have to be able to pair players in an optimal way without just selecting the top values all the time.

## Jonathan's Analysis

Dinkpiece noted that he likes to look for pressure and sacks from his defenses, and that's a smart move. I've done a lot of research on this in the past, and it's amazing how strongly correlated pressure is with takeaways.

Over the last three years, teams that have ranked in the top 10 in quarterback pressures (via Pro Football Focus) have secured 39 percent more interceptions than those that ranked in the bottom 10 and forced 48 percent more fumbles. It's pretty incredible how well past pressure can predict future takeaways—much more so than even past takeaways.

By the way, I study pressures instead of sacks because the former is far more predictive. Defenses have historically sacked the quarterback on around one-fourth of all pressures. When you see a defense that starts the season with 20 sacks but only 30 pressures, they're almost assuredly going to see a major drop in their future sack rate. If you want to predict a defense's sacks per game, look at their pressures per game and divide that number by four.

Also, I prefer to analyze forced fumbles over fumble recoveries because the latter stat can be very volatile; once the ball hits the ground, it's very random as to who will recover it. When a defense recovers, say, 80 percent of the fumbles they force in a year, that's a really good sign they're going to recover fewer fumbles in the following season, for example.

Both players also brought up the wind. I looked into the effect of wind in my last book "Fantasy Football (and Baseball) for Smart People." Here's a look at how it alters passing efficiency.

As Dinkpiece presumed, there's a big drop in passing success with strong winds; once you get above around 15mph, you're getting into the territory where it's going to have an impact on the passing game (and kicking game). In the typical game with winds in the 16-20mph range, a quarterback will be around 11 percent less efficient than he is inside of a dome with the conditions otherwise the same. That's going to have an effect on your projections.

Note that there's also an effect on play-calling; coaches adjust for the conditions and call fewer passes when it's windy.

Combine this with the efficiency and you're looking at a dip of at least 15 percent in total expected production for the typical quarterback in winds of 16+ mph.

And in case you're interested in the effect of temperature on passing efficiency. . .

# **Section II: Sample from**  Fantasy Football (and Baseball) for Smart People

Some people play fantasy sports with family and friends. Some do it for entertainment on a Sunday afternoon. And with the advent of daily fantasy sports, some people are attacking the game from an entirely new angle: to make a living.

 Fantasy Football (and Baseball) for Smart People: How to Turn Your Hobby into a Fortune provides in-depth analysis on how to truly profit from fantasy sports. Working with the game's top players who are already raking in tens of thousands of dollars per month playing fantasy sports, the book is a step-by-step guide to making money from fantasy football and baseball on DraftKings.

Using actual game data from DraftKings to analyze which strategies are winners, _Fantasy Football (and Baseball) for Smart People_ takes a scientific approach to playing fantasy sports. No more guessing. No more dogma. Just bottom-line analysis to help you become one of the growing number of fantasy sports' profitable players.

## One-on-One: How to Win Heads-Up Leagues (and 50/50s)

" _Success is steady progress toward one's personal goals."_

  * Jim Rohn

You know what I like to discuss with my friends who play daily fantasy? Tournaments. GPPs. Large-field leagues. Whatever you want to call it, we like to talk about how awesome it would be to win DraftKings' Millionaire Maker.

You know what most pros like to discuss? Head-to-heads and 50/50s. With the exception of Al_Smizzle and naapstermaan—the kings of tournaments—and a few other players, many of daily fantasy's top players build their bankroll by kicking ass in head-to-heads, 50/50s, and the like. They take advantage of tournaments, too, but they utilize smaller leagues for steadier growth.

Head-to-heads and other small leagues that pay out a high percentage of entrants are foundational pieces of the daily fantasy pie. The upside isn't as great as in a tournament, but neither is the risk. And if you're really trying to see a quality ROI in daily fantasy, you need to minimize risk in some form or another.

There are two primary ways in which head-to-head leagues in particular minimize risk. First, the obvious: there are only two freakin' players. Even if you're new to daily fantasy, you'll still probably win at least 40 percent of your heads-up matches.

Second, head-to-head leagues allow for a linear return. By that, I mean that if you consistently finish in the top 25 percent of all scores, you'll get paid at that rate (winning in three-quarters of your heads-up leagues). If you're a totally average player near the 50th percentile, you'll probably win right around as many as you lose.

That's in opposition to tournaments in which you need to cross a certain tipping point to get paid. A 50th percentile score isn't going to do you any good; it will never win.

## 50/50s

Like a heads-up league, the top half of entrants in a 50/50 get paid. That can create safety if you're entering just one lineup. No matter the quality of your lineup in a heads-up match, there's always a chance that it gets beat by a higher score. That won't happen in a 50/50, however, since you won't see an outlier take you down.

However, here's why head-to-head games are safer when we look at the broad picture: the more you play, the less risky they become. With head-to-heads, you can enter the same lineup again and again and actually _increase_ the safety of that lineup; the larger the sample size, the more likely that you'll get paid "as you should," i.e. there will be a linear relationship between your score and your return.

If you have a top 10 percent score, for example, and enter it into just one league, you'll have a 90 percent chance to win. Enter it into 10 leagues, and the odds of not getting paid are basically zero. Enter it into 1,000 leagues, and you'll be nearly guaranteed to win close to 90 percent and lose close to 10 percent.

Now consider a single lineup in 50/50s. One 50/50? Lots of safety? Two 50/50s. A little less safety. Five-hundred 50/50s? You better be on your game, bro. Because if that team flops and you entered it into nothing but 50/50s, you just lost all of your money.

Heads-up leagues are like schools of fish: they have safety in numbers. Meanwhile, 50/50s are similar to pancakes. As Mitch Hedberg would have said, they're all exciting at first, but after a while you're fu**ing sick of 'em.

## Diversifying Based on Player Pool

Theoretically, 50/50s would be invaluable if you were playing completely different lineups. You could enter various lineups into different 50/50s and just use those to grow your bankroll.

The problem with that strategy is that, as you move down your list of lineups, they become less and less optimized. The value of submitting one lineup into lots of leagues is that you can play the best of the best. Over the long-run, that will provide you with the best return.

So it's really a balancing act between diversifying lineups and increasing upside. That means your potential player pool—the number of players you like in a given night in MLB or a given weekend in the NFL—should dictate your strategy.

Namely, if you like relatively few players, you'll have fewer lineups and would be smart to play in more head-to-head matchups. Meanwhile, a larger player pool would allow for greater lineup diversity, and thus more opportunity to enter 50/50s.

## **Raise the** Roof **Floor**

One of the most overrated stats in all of football is yards per carry (YPC). The stat is pretty much useless because it's so affected by outliers. A running back can be having a poor game of 15 carries for 45 yards (3.0 YPC), then break off a 70-yard run that will catapult his average to 7.19 YPC. All of a sudden, he "ran all over the defense"—a conclusion that might result from one broken tackle or a defender falling down.

Is that 115 yards on 16 carries really the same as a running back who continually gashes the defense for seven yards? Of course not, and it's vital to understand that difference when selecting your fantasy lineups.

In his book  Antifragile, Nassim Nicholas Taleb makes a distinction between the resilient and the antifragile. The resilient can withstand shock; it remains the same in the face of outliers. The antifragile, on the other hand, not only withstands variance, but it prospers from it. The antifragile _improves_ with chaos.

In many ways, your daily fantasy lineup selection should be built upon a similar foundation. Outlying poor performances will hurt you regardless of the league type, obviously, but you want your head-to-head lineups to be as resilient as possible; you want consistent play from every position. When a 40th percentile lineup is a winner in 50/50s and most head-to-heads, you want low-variance, not volatility.

## Understanding Long-Term Trends

In any daily fantasy sport, you have a lot of decisions on your hands, the most overlooked of which might be salary cap allocation. The individual matchups are of course important and every lineup should be built upon specific information relative to that day's games, but I think some players overlook the importance of long-term trends.

While the value of an individual player is fleeting, the importance and consistency of specific positions is more everlasting. Are quarterbacks generally safer options than running backs? Is it ever okay to pay up for a kicker? Which types of wide receivers provide consistent production?

## NFL Head-to-Head and 50/50 Strategy

Head-to-head or 50/50, what you're seeking is consistency. If you average 150 points with half of your lineups scoring 100 points and the other half scoring 200 points, you're actually not going to be a profitable heads-up player. If you can find a way to score around 150 points each time, however, you'll be nearly unbeatable over the long-run.

To back up that idea, let's take a look at some more DraftKings data, this time on the average scores in different league types. Of all the charts you'll see in this book, this one will probably end up being the most popular because of all the information it contains.

This is awesome stuff. You can see the average top score (197) and average score that finishes in the money (157) dwarf the numbers in head-to-heads and 50/50s.

Looking at those head-to-head and 50/50 leagues, the average top score in the latter is much higher than that in the former, which is to be expected since there are just more lineups (sometimes many more so) in 50/50s. Nothing strange there.

But here's what's most interesting to me. The average "in the money" score in head-to-heads (143) is three points lower than that in 50/50s (146). Since the top half of entrants get paid in both league types and we're dealing with a huge sample size, you'd expect the numbers to be equal. You'll have more outliers in a 50/50 since there are more lineups, but if you took the same sample of heads-up lineups, you'd think that the score distribution and average would be the same.

But it's not. Further, despite a higher average "in the money" score in 50/50s, the average overall score is one point _lower_ than in head-to-head leagues. That means the deviation between the average score and the average winning score in 50/50s (17 points) is a lot higher than the same deviation in heads-up matches (only 13 points).

Here's my explanation for the difference that, if true, could really alter the way you enter both league types: _people are approaching 50/50 leagues with the wrong strategy_. It initially seems like 50/50s might be more difficult since the average "in the money" score is three points higher than in heads-up matches, but I don't think that's the case.

Instead, I think many daily fantasy players are approaching 50/50s with a high-variance strategy much like what you might seek in a GPP. So there's a wide gap between the best scores and the average scores that increases the overall average, but _the outlying top lineups might be throwing off the mean_.

Here's a visualization. First, this is how scores might be distributed in a 10-man 50/50 league in which players submit optimal lineups.

This sample distribution matches the DraftKings data—an average "in the money" score of 146 and an average overall score of 129. The dotted line represents that average. You can see that half of the 10 lineups finish above that 129 mean, which is what we'd expect if players submit lineups as they should with a low-variance strategy in mind.

However, this is an example of what we actually see with 50/50 lineups.

Again, the average of the "in the money" scores is still 146 and the overall mean is still 129. But there's a larger deviation in scores, so the top lineups make it seem as though players are better when they're really just submitting high-risk/high-reward lineups.

Now, here's the important part. Take a look at the average lineup dotted line. It's still at 129, but now there are only four lineups that fall above it. The fifth-best lineup—one that would cash in this 50/50 league—has just 119 points, which is 10 below the overall average.

So despite the higher average score in 50/50s over head-to-heads, the deviation in points suggests _you might be able to cash in them more easily_. Daily fantasy players might see the large number of entrants in a 50/50 (which can be huge at times) and automatically think they need a high-variance lineup with lots of upside. That creates a phenomenon through which below-average lineups can sneak into the money.

In reality, you should approach a 50/50 just like a head-to-head league. In both, you want a high floor. There are three primary areas on which to focus in order to increase it as much as possible: position consistency, player types, and player combinations.

## NFL Position Consistency

For the most part, daily fantasy players don't pay much for kickers. Amateurs and pros alike understand that it's usually senseless to pay top-dollar for a position that's not consistent from week to week. It doesn't matter how many points a player scores and it doesn't matter how scarce those points are if you can't predict his performance.

We all seem to intuitively know that we shouldn't pay for kickers, but few people extend this argument to the other positions. In leagues in which safety is the name of the game, there should be a strong positive correlation between the percentage of cap space you're willing to spend on a player and your ability to accurately project his performance.

It's not like any of the skill positions are unpredictable in the same way as kickers, but there's still varying degrees of predictability. Those should undoubtedly have an influence on your decision-making. All other things equal, you could maximize your team's long-term floor by allocating a higher percentage of the cap to the safest players.

In my  first book on daily fantasy, I calculated the consistency of each position. I'm going to use the same methodology here, but with updated results. To obtain the numbers, I looked at the top fantasy scorers over the past four years. They are the players who would typically cost the most money on daily fantasy sites.

I charted the number of "startable" weeks for the players at each position. A "startable" week was defined as finishing in the top 33 percent at the position (among the top 30 quarterbacks, tight ends, defenses, and kickers and the top 75 running backs and wide receivers).

You can see that running backs have been by far the most consistent position, with the best of the bunch giving you a top 10 performance 67.0 percent of the time. Quarterbacks aren't far behind at 61.1 percent, but no other position is close.

When you think about it, that shouldn't be a surprise. Consider the number of opportunities each position has per game. For quarterbacks, it might be 35 attempts. For top running backs, it's in the range of 15 to 25 touches.

Meanwhile, wide receivers and tight ends might be lucky to see 10 targets in a game, and it's often much fewer. Just based on those numbers alone, you'd expect quarterbacks and running backs to be more consistent, and thus more predictable. It's like asking if a baseball player will come closer to hitting at his career average after five games or after 20 games. . .there's just no contest.

Taking it a step further, I analyzed the percentage of "top-tier" weeks turned in by each position. I defined "top-tier" as a top two finish for quarterbacks, tight ends, kickers, and defenses or a top five finish for running backs and wide receivers (the top 6.7 percent for each position).

Again, no contest. Quarterbacks and running backs are just far more consistent on a week-to-week basis than all other positions. When you're paying for reliability, you should start with the quarterback and running back positions.

## NFL Player Types

The position consistency data is certainly useful in all league types, but we can cut up the data a little more to obtain even better accuracy. Specifically, we can look at subsections of each position to see which _types_ of players are the most consistent, and thus worthy of the majority of our cap space in head-to-head leagues.

Before diving into that, I think it's important to once again rehash the fact that much of what we view as individual consistency and volatility in the NFL is illusory. Would we ever create enduring narratives for baseball players through eight games? Never, so why do it for NFL players?

I explained this idea in  Fantasy Football for Smart People: How to Cash in on the Future of the Game:

In any set of random (or near-random) data, you'll see lots of "abnormal" results. If you assign Calvin Johnson a 50 percent chance of going for 100 yards and a touchdown in any given game, he'll probably wind up with somewhere around eight games with such numbers over the course of a season.

But there's also a solid chance that he'll appear to have either an unusually outstanding or a very poor year. With a 50 percent chance of 100 yards and a score in any game, Megatron is around as likely to have either five or 12 stellar games as he is to have exactly eight.

Because the number of games in an NFL season is so low, it's really easy to see patterns in data that aren't really there. Over the course of even a few NFL seasons, we'd expect some players to appear to have a huge degree of weekly consistency, even if consistency were completely random.

Similarly, even with total randomness, a handful of players would appear to be "all-or-nothing" fantasy options without much consistency, when in reality they possess just as much consistency as the most reliable performers.

I really believe daily fantasy players as a whole place too much stock in past game-to-game consistency on the individual level, especially in the NFL. It's not that game-to-game consistency doesn't exist, but just that it's going to be really, really difficult for us to separate it from randomness.

It's the same reasoning behind my typically bullish stance on injury-prone players. Are some players more likely to get injured than others? Probably, but that doesn't mean we can turn that idea into actionable information.

Injuries are relatively low-frequency events controlled heavily by randomness, and humans aren't built to properly deal with randomness. We perceive all sorts of signals that aren't really there because it's not all that evolutionarily beneficial to say "I don't know."

But in daily fantasy sports, saying "I don't know" is a great thing; by factoring your own fallibility into your decisions—a choice that's reflected in your stance on week-to-week consistency and injury-proneness alike—you'll be able to acquire value where others are overlooking it.

The bottom line is that the majority of what most think they see as consistent play is noise. It would take years of NFL data to establish individual player consistency to the point that we can trust what we're seeing isn't just randomness. By that time, it's too late.

The crux of my individual-player-consistency-is-kind-of-overhyped-but-maybe-not-completely argument is that a small sample size hinders our ability to obtain meaningful results.

The solution? Once again, it's player comps. By broadening the potential player pool to include players who resemble the one in question, we can actually acquire more significant results. Ultimately, it just comes down to figuring out which sorts of players—and which aspects of their games—are consistent and repeatable.

### Quarterbacks

Many NFL teams covet versatility, particularly on the offensive side of the ball, because it can create matchup problems. As a daily fantasy football player, your search for versatility should be more league-specific.

Namely, versatility is a wonderful thing in head-to-head matchups or leagues with a high percentage of players cashing (50/50s and even three-team leagues, too). Versatility increases the number of ways a player can beat a defense, raising his floor.

At the quarterback position, mobile quarterbacks like Cam Newton and Michael Vick have proven to be more consistent than the average passer. That flies in the face of conventional wisdom, which suggests that quarterbacks who rely on their legs are actually big risks.

I looked at the number of quality starts from mobile passers—those who have rushed for over 400 yards in a season—with "quality start" being defined as any game in which the quarterback posted at least six percent of his total fantasy points. That way, I could automatically adjust for differences in total production to see which passers have a flatter distribution of scores, i.e. more consistency.

It turns out that the mobile passers have been just under 10 percent more consistent than pocket passers. The idea that a player like Newton is volatile, which probably stems from the perception that he might not be an elite passer, is just wrong.

### Running Backs

As I mentioned earlier, I was very bearish on Marshawn Lynch heading into the 2013 season. I ended up looking like an idiot, but part of my reasoning was that Lynch was becoming somewhat situation-dependent in Seattle. He had thrived because the Seahawks were a winning team, which is why he saw 338 touches in 2012.

Prior to 2013, though, Lynch hadn't caught more than 28 passes in a season since 2008. It's not like he was a Michael Turner-esque receiver out of the backfield, but Lynch didn't generate a large percentage of his points as a pass-catcher. And with young pass-catching backs behind him on the Seahawks' depth chart, it stood to reason that Lynch might see a dramatic decline in usage if Seattle were to lose more games than expected.

Lynch silenced his 2013 doubters, but that doesn't mean there wasn't a certain level of volatility inherent to his game. As it turns out, pass-catching running backs have proven to be far more consistent than running backs who don't see heavy work as receivers.

Looking at all backs with at least 750 rushing yards, the top 25 percent in catches have been 14.4 percent more consistent than the bottom quarter in receptions. The typical pass-catching running back has generated 10.3 quality starts—defined the same way as with quarterbacks—per season.

Whenever you're analyzing a player, you need to envision how the course of the game could affect his production. In head-to-head leagues in which consistency is king, you want players who can produce almost regardless of the path of the contest. Someone like Reggie Bush can give you numbers even when his team is down 21 points—perhaps to an even greater degree than when they're winning—whereas a back like DeAngelo Williams can be rendered useless.

Further, because running backs who don't catch passes have limited ways to score fantasy points, they're more touchdown-dependent than pass-catching backs. Touchdowns are relatively volatile, so it makes sense to fade backs who lack versatility when you're seeking consistency.

### Wide Receivers and Tight Ends

When I first started studying the week-to-week consistency of pass-catchers, I thought that receivers who are relatively dependent on big plays—think Josh Gordon or Torrey Smith—might be slightly less consistent than receivers and tight ends with shorter targets.

That's true to an extent, but what seems to matter most is the actual alignment. Receivers who play primarily in the slot have recorded over 15 percent more quality starts than those who play out wide.

Why? It's tough to say for sure. Shorter targets (and thus a higher catch rate) probably have something to do with it, but it's also more difficult to double-team a slot receiver (or a tight end). Whereas cornerbacks can use the sideline to their advantage against an X or Z receiver with safety help over the top, nickel cornerbacks need to cover slot receivers all over the field, typically without help.

Ultimately, it seems like slot receivers possess more consistency than outside receivers. That could make them quality options in the flex position for heads-up leagues, especially on full PPR sites like DraftKings.

## NFL Player Combinations

In our search for the ultimate "safe" lineup, we've covered the consistency of each position and subsets of those positions. Yet another way to increase your team's floor is to strategically pair players who are playing in the same game.

Namely, you're searching for players whose production is tied via an inverse relationship, i.e. as one player goes up, the other goes down. That doesn't necessitate one being good and the other bad, of course—they should both offer value—but the relationship between them would increase the floor of your team.

One such pairing is two starting running backs from teams facing one another. That's particularly useful in a game that could be close, as both teams would be likely to continue to run the ball throughout the game. If one of the teams gets down big and is forced to throw, however, the drop in workload you'd see from one of your backs would be offset by the increased workload from the other back.

Another pairing to consider is starting two pass-catchers in the same game. It's the same idea as with the running backs. However, you could actually start two pass-catchers on the same team, too, assuming the contest figures to be high-scoring. If you look at the Vegas line and see the Broncos-Chargers matchup with an over/under at 59 points, for example, you can be pretty sure there's going to be a whole lot of points scored. Pairing a wide receiver and tight end from the same team might not be such a poor play in a head-to-head league because, due to the relative safety of each offense as a whole, there's a solid chance that at least one (or possibly) both of the pass-catchers will go off.

You never want to combine teammates just for the sake of doing it—they always need to provide value—but creating those relationships based on your league type could increase your win probability by a few percentage points.

## MLB Head-to-Head Strategy

One of the reasons I'm able to do this NFL-MLB hybrid book is because there's so much overlap between the two sports when it comes to daily fantasy. It doesn't matter if you're deciding between Drew Brees and Aaron Rodgers or Clayton Kershaw and Matt Harvey; the general principles surrounding risk and reward are the same.

So it follows that your head-to-head (or 50/50) MLB strategy is basically the same as it is in daily fantasy football: maximize your floor. Unlike in football, though, position consistency and "player types" don't matter as much because players are put in the exact same situation in baseball: 60 feet and six inches from the pitcher's mound.

You still want to seek consistency in MLB heads-up matches, but it's more about the consistency of certain stats rather than that of positions. For all practical purposes, there are two positions—hitter and pitcher—and you want to have exposure to the players who generally post more of the stats that are consistent from day to day.

## MLB Scoring Data

I showed you the NFL head-to-head and 50/50 data from DraftKings. Now it's time for the MLB stats.

Again, the upside needed to win a GPP is obvious. Meanwhile, the average score to cash in a DraftKings MLB head-to-head is 109, compared to 115 in a 50/50. That's a significant difference and suggests that perhaps 50/50s aren't the way to go in daily fantasy baseball.

That's in contrast to what the numbers suggest in football, but why? My guess is that there are many, many daily fantasy football players who are new to daily fantasy sports. They might know football, but they don't know how to efficiently allocate cap space in the world of daily fantasy.

Daily fantasy baseball probably has more experienced players who understand how to play 50/50s, however. There's evidence of that in the average 50/50 score (98) surpassing the average head-to-head score (96); we don't see that effect in the NFL.

So it's not that there's no money to be made in daily fantasy baseball. On the contrary, the sheer number of games creates an outstanding money-making opportunity. But the new players are likely involved more with head-to-heads and GPPs than 50/50s.

## Recent Play vs. Long-Term Numbers

I've talked a lot about how there's all kinds of variance in the NFL, even over the course of an entire season, because they play so few games. That variance doesn't exist over a 162-game MLB season, but it's very much in play from night to night.

On a per-game basis, there's probably even more variance in baseball stats than football stats, particularly for hitters. Whereas pitchers frequently have 100-plus opportunities to assert their dominance within the course of a single game, hitters have a rather small sample of chances to score fantasy points in a single game. A batter might swing the bat fewer than 10 times per game, and he'll rarely see more than six plate appearances to generate points. Meanwhile, quarterbacks sometimes throw the ball 50 times in a single contest.

In any random environment, we'd expect results to eventually regress toward the mean. But which mean? Are baseball players likely to regress toward their long-term mean in various statistical categories, or more probable to regress toward a recent mean, i.e. streaky play?

It appears as though  there is indeed streaky play in baseball, although perhaps not to an extreme degree. We're going to see long runs of greatness and seemingly never-ending slumps because there are so many players that those events will occur just from chance alone.

However, baseball is a unique sport in that, because of the sheer volume of games, you can leverage small advantages into large ones over the course of a season. In that way, baseball is paradoxically both random and determined; it's filled with variance on the day-to-day level, but the little blips in the data even out long-term. And that's kind of what you can expect as a daily fantasy baseball player, too—ups-and-downs in the short-term, but steady long-term results.

Matt Holliday will go through all kinds of streaks and slumps within a single season, for example, but the probability of him hitting between, say, .285 and .325 at the end of the year is rather high. If we were to simulate 10,000 games for Holliday instead of 162, he'd be extremely likely to fall near his career average of .311.

When it comes down to it, we need to combine recent play with matchup data (such as the pitcher, the ballpark, the weather, and so on) _and_ long-term trends. The extent to which daily fantasy players value each factor varies, but they should all be included in your analysis.

As a quick aside, I think it's worth mentioning that many daily fantasy players disagree on the value of short-term trends in MLB data, i.e. do players really get hot and cold? Again, I think they do to some degree (even if it's just an unknown injury affecting their play), but I definitely believe you should at least consider recent numbers.

The reason has to do with player salaries. Because NFL teams play just once a week, player salaries are altered a whole lot between games. Thus, players coming off of big games can be overvalued since their salary will rise, but they aren't really "hot" in any meaningful way.

That's not the case in daily fantasy baseball, though; since the teams play nearly every day, daily sites don't update the salaries so much. If streaky play exists, you should be able to obtain an advantage by considering recent stats. If it doesn't, there shouldn't be much of a disadvantage, though, since you don't need to pay more for those guys. You always want the players with optimal numbers over the long-run, but there are enough players that you can value both long-term and short-term stats.

## Consistency in Head-to-Heads

My official position on "streaky" MLB play is that it probably exists, but even if it doesn't (or not to an extreme degree), it probably doesn't hurt you to value short-term stats, assuming long-term stats are still the backbone of your research. Almost all successful daily fantasy baseball players will tell you to emphasize large samples of data and then adjust for recent trends, not the other way around.

Mike5754—one of the premiere MLB players in daily fantasy—is one of those players.

"I tend to use numbers from huge samples. I don't even look at BvP (batter vs. pitcher) data, for example. I just care about a guy's numbers against lefties or righties, for the most part, because the BvP stuff is so dependent on the situation.

Also, I tend to value a player's career numbers over recent stats. You can get fooled by a single game or even a stretch of games, but the numbers mean something after a few seasons. All of those subjective factors even out.

So over the first month of the season, for example, I use only stats from the past three years. After that, I slowly include the numbers from the current season, but I still value primarily the long-term data."

This is important information when searching for consistency because we need to know which stats are the most reliable. Regardless of the importance you place on streaky play versus data from larger samples, it's important to know the consistency of each stat.

## Studying Year-to-Year Trends

I collected data on the season-to-season consistency of various individual player stats. Why not game-to-game consistency? Remember, baseball's consistency is easily apparent over large stretches of games, but not so obvious over the course of a single game, week, or even month.

You might be saying "Uh, yeah, idiot, so if there's no consistency from game to game, why even look at the numbers?"

First of all, watch your language. Second, it's not that there's _zero_ consistent from game to game, but just that it's minute enough that we can't really detect it or separate it from randomness. But we know it must be there since there's all kinds of consistency from year to year.

And in daily fantasy baseball, small advantages can equal big profit down the line. By continually placing yourself at the right end of the consistency spectrum, you can turn a minor positive into an enormous advantage over the course of a season.

## Batter Consistency Data

So here it is—the year-to-year consistency of various hitting stats.

The two most reliable hitting stats are strikeout percentage and home run percentage. The strikeout percentage is understandable given the number of Ks in each game, but the home run percentage is less expected, at least to me.

There were 18,336 total strikeouts during the 2013 baseball season, but only 2,504 home runs—over seven strikeouts per long ball. Even as a relatively low-frequency event, though, home runs have been about as consistent as strikeouts—the power of a large sample size.

CSURAM88 agrees with this approach. "I pay for home runs for batters. It doesn't matter what league type, I like to look at 3-4-5-6 hitters who can hit for power in any game."

Meanwhile, walks carry over from one season to the next at a higher rate than average. There's more evidence of that in the slugging percentage (SLG) and on-base percentage (OBP) correlations, which are in many ways a hybrid of the other stats on here.

## Pitcher Consistency Data

And now for the pitchers. . .

As is the case with hitters, strikeouts are the most consistent stats for pitchers. The coefficient of determination for strikeouts per nine innings is 0.68. Groundballs per fly ball is the next most consistent major stat for pitchers—even more so than walks or hits per nine innings—which is mildly surprising.

Not shockingly, ERA is the least consistent major stat for pitchers with an R-Squared of only 0.25—lower than the value for batting average.

## How to Use the Numbers in Heads-Up Games

The reason that consistency stats matter is because your projections are only as useful as the ability of the numbers to carry over from one game, one week, or one year to the next. Even if you knew beyond a shadow of a doubt that one pitcher would throw a perfect game in a given night, the information would be useless to you if there were no predictability in pitcher stats from night to night. The more comfortable you can be in your predictions—the more consistency inherent to certain stats—the more weight you can place on your projections.

When trying to increase the floor of your daily fantasy baseball team in a head-to-head matchup, you want to pay for the stats that 1) give you the most points and 2) are the most likely to remain consistent.

To give you an idea of that process, let's take a look at part of DraftKings' MLB scoring. DraftKings awards 10 points for a home run and two points for a walk. If we want to project, say, Albert Pujols in both categories on a given night, we might start with his career baseline averages of 0.25 home runs per night and 0.54 walks.

Right off the bat, you're looking at 2.5 projected points from home runs and 1.08 points from walks. But the disparity between the two stats is even greater because home runs have been a bit more consistent than walks from year to year (a coefficient of determination of 0.75 versus 0.68).

You could simply multiply the projected points by the consistency correlations to obtain an adjusted projection of 1.88 for home runs (2.5 * 0.75) and 0.73 for walks (1.08 * 0.68).

Now, I don't think the math is necessary when projecting every player each day, but the broader picture is more valuable: in head-to-head matchups, you want to pay for the players and stats that are most likely to get you points, thus increasing your floor projection.

For hitters, you might want to consider home runs as a more consistent source of production than previously thought. Any single player is probably unlikely to hit a homer in a given night, but the odds of your entire lineup knocking a few out of the park can be decent. And as the season progresses, that little advantage will accumulate into a bigger one.

Meanwhile, you're probably better off not paying up for guys who 1) don't hit for power and/or 2) don't walk a lot. Drawing walks is a pretty consistent skill, so it holds more value in heads-up matches than tournaments. Hitting for power is even more important.

Contrary to popular belief, players like Salvador Perez who hit for average but don't have an exorbitant number of walks or home runs probably aren't as consistent (relative to their salary) as some other batters. Even a player like Joe Mauer, despite a decent number of walks, is probably overvalued on most occasions.

For pitchers, it's all about the Ks. On DraftKings, pitchers get just 0.5 points less for one strikeout (2 points) than they do for a complete game (2.5 points). Just two strikeouts equals one win (4 points). Because strikeouts are 1) a big contributor to total points, 2) consistent, and 3) scarce (meaning some pitchers have a lot more than others), they're very valuable in head-to-head leagues.

## MLB Salary Cap Allocation

As was the case with their NFL data, DraftKings hooked me up with the salary cap allocation of winning MLB teams. Here you go.

With two starting pitcher spots, winning head-to-head lineups spend an average of 17.3 percent of their cap space on each pitcher. You can see that they're typically saving at catcher and shortstop, which is to be expected. The typical winning head-to-head lineup pays an average of 8.5 percent of his cap on each outfielder.

Now, let's compare that to the typical 50/50 distribution.

As expected, the allocation is similar to that for winning head-to-head lineups. The average 50/50 winner is actually spending slightly more on starting pitchers, though, and slightly less on outfielders. Many pros will tell you that paying for starting pitching is the safe move in head-to-head and 50/50s.

## MLB Player Combinations

Just as in football, there are some player relationships in baseball that are connected via an inverse relationship (such as batter vs. pitcher). If you start a bunch of batters and the pitcher they're facing, chances are your hitters will stink if your pitcher has a decent outing.

But here's the difference between football and baseball, and it's an important one: baseball stats are zero-sum and football stats aren't. By that, I mean that if your pitcher gets points, the hitter loses points, and vice versa.

In football, on the other hand, you can comfortably start wide receivers facing one another. Their play is connected, of course, but when one scores, it doesn't cut into the other's fantasy points. You never want to create a situation in which playing one guy will actually have a negative impact on another's performance within your lineup. That relationship reduces both your ceiling and floor.

## The 10 Laws of Head-to-Head (and 50/50) Strategy

Head-to-head and 50/50 leagues are so important because they're low-risk propositions that can provide a steady return. Let's run through the 10 most important points from this chapter.

Law No. 1: Head-to-head and 50/50s should be the foundation of your league structure.

Any league that pays out a high percentage of entrants will offer you a more consistent return on your money. So even if tournaments provide the best long-term ROI, it can take a long time to see that money make its way back into your account (depending on the type of GPPs you enter). That makes head-to-head leagues and 50/50s perhaps the best practical investment choice.

Law No. 2: Enter as many NFL 50/50s and MLB head-to-heads as your bankroll will allow.

While entering a single lineup into 50/50s is risky, the data suggests that, because daily fantasy football players are approaching them with the wrong strategy, they're perhaps better investments than head-to-head leagues. As long as you aren't compromising the integrity of your bankroll, 50/50s are outstanding investments in daily fantasy football.

That might not be the case in daily fantasy baseball, and it probably has something to do with the type of people who are playing the game. There are still lots of novices, i.e. "bad money," but they don't seem to be playing as many 50/50s.

Law No. 3: You should vary head-to-head and 50/50s based on your player pool.

The primary factor that should dictate how much of your bankroll you place into 50/50s versus heads-up leagues is the number of potential players you're willing to use in a given night or weekend. If you like relatively few players, you should stick with head-to-heads since you won't be able to vary your lineups too much. If you've assigned a bunch of players with near the same values, though, you can enter more 50/50s with a "diversified portfolio."

Law No. 4: Your goal in both league types is increase your floor.

Your goal in a head-to-head is the same as in a 50/50—play it safe. Do everything in your power to increase the floor of your team. You don't need a 190-point night in a 50/50; 150 points will usually do the trick.

Law No. 5: In NFL, you can play it safe by paying for quarterbacks and running backs.

Quarterbacks and running backs are far more consistent than every other position on a weekly basis. That's likely due to the number of plays in which they're primary contributors: 30-plus for quarterbacks and 15-plus for starting running backs, as compared to far fewer for wide receivers and tight ends.

When you're valuing safety, you should allocate a higher percentage of your cap to the least volatile positions. The numbers show that the best NFL head-to-head and 50/50 lineups typically pay a healthy amount for quarterbacks and running backs.

Law No. 6: In MLB, you should pay for consistent stats like home runs and strikeouts.

In the same way that you pay for consistent positions in daily fantasy football, you want to emphasize safe stats in baseball. Paying for home runs (yes, home runs!) for hitters and strikeouts for pitchers is a smart way to maximize your team's floor over the long run.

Law No. 7: Target certain types of NFL players who display week-to-week consistency.

In addition to specific positions, there are certain types of players who have proven to be the most consistent from week to week: mobile quarterbacks, pass-catching running backs, and slot receivers. When possible, favor those position archetypes in leagues that require safety.

Law No. 8: Target quality starting pitching.

Most pros will tell you that they pay for starting pitchers in head-to-head leagues. That's not a coincidence, because the data suggests that pitchers are far more reliable than hitters from game to game.

Law No. 9: Always pay attention to long-term MLB trends.

While you should certainly take note of recent trends—baseball players have a higher probability of "getting hot" than football players, after all—you also need to be aware of long-term trends. If you're projecting a player in a given night, chances are the best projection will be the one that most effectively combines his historic stats with those in recent nights or weeks.

Law No. 10: Avoid volatile player combinations.

In the NFL, playing a quarterback with his receiver is a good way to increase volatility—something you don't want in a head-to-head league. Instead, search for players whose production is connected via an inverse relationship, such as two running backs in the same game but on different teams. In baseball, filling every hitter slot with players on the same MLB team can also increase risk since they need to hit against the same pitcher.

" _My theory is to strive for consistency, not to worry about the numbers. If you dwell on statistics, you get shortsighted. If you aim for consistency, the numbers will be there at the end."_

  * Tom Seaver

## The Final Piece of the Puzzle: Creating Projections and Lineups

" _Most people ask what we would do if we had the answer. The first thing that we would do is we would begin to solve it. For example, picture a_ _chess_ _board and think about the rules that govern the game play. Just because you know how the pawns, bishops, and knights move around the board doesn't mean that you're a grand master."_

  * Michio Kaku

This book is set up in such a way that the majority of what you need to get started (and hopefully profit) playing daily fantasy football and baseball is located in the first four chapters. But as the great physicist Michio Kaku has pointed out, there's a difference between understanding all of the rules and truly mastering something.

Thus, this chapter is going to be filled with the opinions of daily fantasy's pros—the game's top players who are already highly profitable. Thanks to DraftKings, we know the rules of the game. Now it's time to hear from the grand masters.

## Starting the Journey Toward Value

When it's all said and done, we're all seeking value; we want to uncover players whose production will exceed their cost. I've already explained how to build your research foundation, so how in the hell do we translate that data into player projections?

One way is to use the aggregate approach I detailed earlier. If you're creating daily fantasy football projections, for example, you can easily import data from numberFire, 4for4, FantasyPros, RotoGrinders, and similar sites into Excel.

By calculating the average of those projections, the hope is that you can factor out flaws in each set of projections to generate a "truer" mean projection. This "wisdom of the crowd" approach is the simplest and quickest way to create accurate projections.

But it's really only the start of your journey, because those projections must be modified as new information rolls in. The way that the projections are adjusted depends on the sport.

"Projecting baseball is a lot different than football because you're pretty limited with time in MLB," says CSURAM88. "So for baseball, I upload projections into Excel and then quickly modify them based on factors that aren't already part of the projections."

Another of daily fantasy baseball's best players—Mike5754—does the same. "After I have every player listed, I immediately delete anyone playing in a game that could rain out. If there's more than a 70 percent chance of rain, I won't start anyone in that game because it could kill my lineups if they don't play."

Talking with naapstermaan and other pros, there's a general consensus that studying the weather in baseball is vital to getting a sense of who's going to play and what they might do. "Even if there won't be rain during a game," says naapstermaan, "I might use heavy winds as a tiebreaker to target or fade hitters and pitchers based on the direction and speed of the wind."

For football, the pros seem to favor the Vegas lines more than the average player. Says CSURAM88, "Vegas is so accurate that it would be foolish to not consider their thoughts. I use Vegas as a foundation for my projections, so I'll usually bump up guys who are going to be in high-scoring games. Even if I don't change the projections all that much, it makes sense to target those players, especially when you can stack them in tournaments."

Mike5754 does the same in baseball. "I build around my pitchers. So I look at the Vegas lines to see who's projected to have a quality game, then I'll see what matches up with my initial projections. Then I'll consider other late news, like the weather, the opposition's batting order, the home plate umpire, and stuff like that."

Daily fantasy baseball players don't have time on their side, so the value of quick aggregate projections can be monumental. Daily fantasy football players have more time to study data (which ironically might be far less useful than the stats in baseball).

"Things change so much in the NFL that you really have to adjust on the fly," says CSURAM88. "The long-term numbers don't mean as much as they do in baseball because coaches get fired, schemes change, matchups mean a lot, and so on. So just slightly adjusting a player's past per-game averages doesn't work as well as it might in baseball."

Because of the time that can go into football projections and the fact that positions are so different from one another (as opposed to just batter vs. pitcher in baseball), let's quickly hit on the traits to seek at each position.

## Building the NFL Prototype

There's lots of variance from week to week in the NFL, but ultimately, it's the same sorts of players who return value to daily fantasy owners. Their defining traits are the ones you want to emphasize in your projections.

### Quarterback

I've already discussed mobility as a characteristic of high-floor signal-callers, but accounting for a high percentage of their team's touchdowns is also important.

CSURAM88 studies touchdown rates more than anyone. "Percentage of touchdowns is a really consistent stat for quarterbacks, so you always want to find players who are going to be major parts of their team's game plans. Quarterbacks who see heavy workloads are obviously important, but you also want guys who throw the ball (or run it themselves) near the goal line. Peyton Manning has historically thrown the ball a lot inside the five-yard line, for example, and that's really important."

RotoGrinders is a great source for workload data, including important advanced red zone stats

.

### Running Back

I've personally done a whole lot of research on running backs, so it's my most accurate position to project. Like quarterbacks, the workload matters a whole lot for backs. Check out the relationship between carries and year-end rank for backs over the past four seasons.

Pretty clear relationship—one that's much more linear than that for YPC and rank:

But everyone kind of knows that backs usually need to get the ball a lot to produce. What you might not have known is how much straight-line speed matters at the position. Check out the correlation for various pre-draft measurables and eventual NFL success for running backs. Note that the length of each bar matters most, not whether it's positive or negative.

The most important trait, by far, is the round in which a back gets drafted. That doesn't mean NFL teams have efficiently drafted running backs, though; since 2000, those drafted in the mid and late rounds have actually recorded a higher YPC than those in the first two rounds. But early-round backs see heavy workloads, and that's what matters.

After that, though, you can see the two metrics that best capture explosiveness—the 40-yard dash and the broad jump—are most strongly correlated with running back success. And take a look at what happens if we break up the running backs into subcategories of 40 times.

I used Pro Football Reference's approximate value—a metric that accounts for yards, touchdowns, receptions, and so on—to judge running backs. The numbers are revealing; if a running back doesn't run faster than a 4.50 at the NFL Scouting Combine, his odds of NFL success are miniscule.

These numbers are very applicable to daily fantasy football, too. First of all, you always want to have the most exposure to the types of players who find long-term success. It's not like season-long production comes completely independently of weekly production; the former is just the accumulation of the latter.

Second, stats like these can help to predict breakouts. When a rookie backup running back steps into the starting lineup for the first time, which happens all across the league every single season, it might be difficult to project him without a body of NFL work. His potential workload and his straight-line speed are the two most important factors in predicting his success, even on the level of a single game.

### Wide Receiver/Tight End

At the wide receiver and tight end positions, I'm really interested in the same trait: size. Receivers who are big and heavy often find red zone success—a characteristic that's really consistent from year to year. Take a look at the red zone touchdown rates of DeSean Jackson (a horribly inefficient red zone receiver) and Dez Bryant (one of the game's premiere red zone receivers) since Bryant came into the NFL.

This difference is remarkable. And while I knew Bryant is efficient in the red zone, I actually had no idea what Jackson's numbers would be before I decided to use him as an example. I chose a small, big-play receiver who came to mind. There's such an incredibly strong correlation between size and touchdowns for receivers that these numbers remain very consistent from year to year.

Overall, Bryant's career red zone touchdown rate is 41.7 percent and Jackson's is 12.8 percent. No matter how you slice it, touchdowns are incredibly important to daily fantasy owners. Even though they're relatively low-frequency, you still need to find a way to maximize their count in your lineups. It's a similar situation to targeting home runs in baseball; even though they're relatively fluky over the short-term, it's all about maximizing your exposure to the players who score touchdowns and hit home runs in an effort to increase your long-term win probability.

By the way, I personally find career red zone touchdown rates using PFR's Game Play Finder.

## Building the MLB Prototype

As mentioned, baseball is very much a binary sport—it's always batter vs. pitcher with the conditions nearly exactly the same—so analyzing positions doesn't make as much sense as analyzing stats. Here's an overview of the most predictive baseball stats not used by the masses to help you adjust your initial aggregate projections. You can find all of them at FanGraphs.

### Batting Average on Balls in Play (BABIP)

BABIP is one of my favorite stats in baseball because it can quickly give you an estimate of a hitter's luck in getting on base. For the most part, a player's BABIP is due to random factors, such as defensive strength, an exorbitant number of bloop hits, and so on. That means that BABIP typically regresses toward each player's career average (which is between .290 and .310 for most players).

When a player has been getting on base quite a bit when he shouldn't be, he'll have a higher-than-normal BABIP. When you see a BABIP in the .400 range over an extended period, it's a sign to steer clear of that hitter.

### Weighted On-Base Average (wOBA)

wOBA is perhaps my favorite stat in all of baseball because it captures so many aspects of offensive play. In short, wOBA is a metric that accounts for the actual value of certain types of hits. Whereas batting average doesn't differentiate between the types of hits and on-base percentage inaccurately weighs the value of each type of hit, wOBA measures hits in proportion with their ability to create runs. That makes it a predictive stat.

One of daily fantasy baseball's top players—Mike5754—places serious emphasis on wOBA. "I look at wOBA more than any other stat. I'm always searching for guys with at least a 0.375 wOBA against either lefties or righties—whichever pitcher they're facing that day. So I immediately eliminate a bunch of players right out of the gate each day based on wOBA."

I spent some time charting wOBA for each position over the past few seasons. Here are the results.

Unsurprisingly, first basemen and DHs have been the best hitters of the bunch. The average wOBA is around .320. It will be hard for anyone to sustain a wOBA in the 0.375 range on all at-bats over the long haul, but some can do it against just lefties or righties, especially over small sample sizes.

The point is that wOBA is highly predictive and an outstanding way to determine which hitters are likely to remain hot or break out of a slump in a given day.

### Batter v L/R

One of the most popular ways to analyze matchups is with BvP (batter vs. pitcher) data, which you can find in a number of places around the Interwebs. However, there can be some big, big problems with BvP stats. Namely, they aren't big at all, i.e. a small sample size.

Even if a hitter has faced a particular pitcher, say, 100 times, his numbers will still be rather volatile. Over a stretch of 100 at-bats, he could hit .250 with two home runs, or he could hit .375 with 10 dingers. The at-bats might not be an incredibly small sample, but the other stats you want to study—hits, extra-base hits, homers, wOBA, whatever...that stuff is far flukier.

Mike5754 doesn't even look at BvP stats. "I just don't care because it can't be trusted. It's a small sample, but you also don't know the nature of the at-bats. Maybe a bunch came when the pitcher was throwing on short rest, for example. The only stats I consider when examining a batter versus a particular pitcher are those against lefties and righties. That's it."

And while some top daily fantasy players examine BvP in limited situations, most also tend to favor simple lefty/righty splits.

### Batted Ball Data

It's really an amazing time to be a nerd. With the cumulative power of other geeks like me, we now have all sorts of data on not only the types of hits that pitchers allow, but also how their batted balls leave the bat (namely if they're ground balls, fly balls, or line drives).

That's important because it can give you a really strong indication of a pitcher's future success. The more ground balls, the better. The league average for ground balls is around 44 percent of all balls in play.

Stats like batting average allowed are flimsy, influenced heavily by BABIP, while batted ball rates show true pitcher quality. Historically, line drives have been worth 1.26 runs per out, fly balls worth 0.13 runs per out, and ground balls worth 0.05 runs per out, according to FanGraphs.

Even if you like a pitcher with a high ground ball rate, you might want to get away if he's also giving up line drives on more than 20 percent of batted balls. Since there are a huge number of runs created per out on line drives, minimizing it should be your top priority when analyzing pitchers.

### Expected Fielding Independent Pitching (xFIP)

xFIP is another pitching stat that basically acts as an adjusted ERA, showing what a pitcher's ERA "should be," assuming 1) a league average BABIP and 2) a league average home run to fly ball rate (around 10 percent). Since we know both BABIP and home run to fly ball rate regress near a common league mean for pitchers, xFIP can sort out the noise to give us a really accurate idea of how pitchers are performing. It's an ERA you can trust.

### Stats to Avoid: Batting Average, ERA, WHIP, Wins, BvP

Your typical daily fantasy player is going to look at some pretty generic stats to pick players: batting average, ERA, WHIP, wins, and maybe even batter vs. pitcher splits. None of those stats are nearly as predictive as those I listed, though. Batting average and WHIP, for example, don't control for changes in BABIP. All we really should care about is "can this stat help me make better projections?"

Ultimately, the reason that advanced stats, player prototypes, and the Vegas lines can be useful in your projections is because most players aren't considering that stuff in their work. The same is true of the daily fantasy sites that set the player salaries.

If you're creating initial projections based on aggregate data, you already have the basic predictors of success in there. If you try to adjust the numbers based on, say, recent touchdowns or ERA, your projections will just become repetitive. You need to search for important, predictive stats that _aren't already used by the masses_ to obtain a true competitive advantage.

## Projecting Players

The reason that I ran through some advanced stats is because I don't think there's necessarily one set way to project players. The process of doing research isn't just the means to an end; rather, it's often the most valuable piece of the puzzle. Analyzing objective values can help better inform your subjective decisions.

That's especially true in a sport like football that's not standardized in the same way as baseball. NFL teams run different schemes and approach the game as a whole differently from one another. That doesn't mean you should just guess on your player choices, though. Well, you _can_ guess, but those guesses need to be calculated ones that are directed by your research.

One way that you can use advanced stats to help with your projections is to eliminate certain players right out of the gate. As mentioned, Mike5754 scans his favorite stat—Weighted On-Base Average—and considers only those players above a certain threshold over a given period of time. By choosing only those players who rank highly in a predictive advanced stat, you can greatly reduce the pool of players you need to project.

## Salary Data and $/Point

If you're projecting players in Excel, you'll need to import salary data to create player values. You can typically download the salary data right from the daily fantasy sites, but you can also get it at RotoGrinders.

I want to quickly note that most daily fantasy players import salary data after projecting relevant players, but a few first look at the salaries. Mike5754 takes such an approach, importing MLB salaries into Excel, removing players he immediately deems overpriced (and then doing the same with Weighted On-Base Average) to generate a much more concentrated list of possible players.

Once you have a mean projection for each player (or the players you've chosen to project) and you've imported site salary data, you'll need to combine the two in order to create player values.

Most players create $/point values, simply dividing a player's salary by his projected points. If Jamaal Charles costs $9,000 and you have him projected to score 20.0 points, his $/point value would be $450. The lower the $/point, the better. All other things equal, you obviously want to pay as little as possible for each point that you can be expected to score.

Note that $/point values are especially relevant in head-to-head and 50/50 leagues, but not so much in tournaments. Remember, when you need upside, you really want to focus on obtaining players with high ceilings. The value matters and you still want to stick to primarily the top values in terms of $/point, but it's okay to work your way down your value list just a bit to make sure you acquire as high of a ceiling as possible.

## Lineup Optimization

Some daily fantasy players use lineup optimization tools to figure out which lineup is "optimal" based on their projections. If you use Excel, "Solver" is a simple add-in that can help you optimize your lineup.

However, I find myself on the opposite end of the lineup creation spectrum, generally creating lineups by hand. The primary reason for that is that I don't believe Solver or other programs truly optimize your lineup since your projections aren't flawless.

Remember, projecting players (and $/point values) is a fragile process because little deviations in stats can throw off the results. Your projections can never perfectly capture a player's future from game to game, so there's lots of subjectivity in the lineup generation process.

Further, there are so many different players to project that you can create literally thousands of optimized lineups that are within a point or two of one another. That's a minute difference—one that's basically meaningless once you account for your fallibility as a prognosticator—so blindly choosing the true optimized lineup would be a mistake.

Use $/point values as a tool to find the lineup you consider to be the best for your goals in a particular league. With so many different lineup combinations, your aim should really be to put together a group of players who meet certain criteria (such as those related to the player prototypes and advanced stats).

## Problems with $/Point

Before I dig into the problems with most value systems in even greater detail, let me preface this by saying that I think $/point has value (no pun intended) to daily fantasy players. It's a quick way to narrow down the list of potential players you can utilize in your lineups.

The main problem with $/point is the fragility on which I've already touched. The other chief issue is that it doesn't properly weight the importance of bulk points. That is, it's way more valuable for a high-priced (and thus highly projected) player to live up to his $/point than it is for a low-priced option to fulfill his $/point duties.

To show you why, I'm going to give you a very extreme and very awesome example. Suppose that the Dallas Cowboys sign me to play running back for their next game. Admittedly, this is very unlikely to happen (although many physicists would argue it necessarily must happen in some parallel universe somewhere).

So there I am, the starting tailback for the Cowboys, and DraftKings & Co. need to give me a salary. We'll just say for a second that DraftKings doesn't know I PLAYED HIGH SCHOOL FOOTBALL and that I still do 50 pushups (not in a row) every weekend. It's whatever.

Anyway, unaware of my natural ability, they price me at $0. Nothing. Zilch. Nada.

But you know I'm starting and you even project me for one yard. It might be one yard on 20 carries, but it's one yard. Guess what my value is with a $0 salary? It's $0/point, i.e. it's infinite. I have infinite value.

So while all those other sucker NFL players are sitting there with $/point values higher than $300, you can secure my one yard for a fraction of the cost, and that fraction is nothing.

But we all know that I'm not valuable at all. Putting me into your lineup would just be wasting a position; the $/point calculation has broken down. While situations obviously aren't so extreme in this universe, the idea is the same; lower-priced players will naturally have greater $/point values, but their actual worth to your team isn't represented by that number.

In short, you should be searching for the best combination of $/point and pure points.

## Thinking in Probabilities

At the crux of this semi-negation of $/point as a flawless value system is that we should be thinking about players in a more probabilistic manner. To give you an idea of why, I'm going to jump right into an example.

Suppose you're deciding between a "high-low" running back combination of Adrian Peterson ($9,000) and Bernard Pierce ($5,000) versus a mid-tier combination of C.J. Spiller ($7,000) and Le'Veon Bell ($7,000). You have both duos projected to score 28.0 total points, so the $/point for both tandems is the same at $500.

Same projection, same total salary, same value. Even though they have identical $/point values, the second duo of Spiller and Bell is almost certainly the superior option. The reason is that the probability of both of them reaching a certain threshold of points is far greater than both Peterson and Pierce doing it (since Pierce's low probability kills their chances).

Let's assume that both Spiller and Bell have a 50 percent chance to score 14.0 fantasy points. The odds of both of them scoring at least 14.0 points would be 25 percent. Meanwhile, we'll say Pierce has a 30 percent chance to meet the mark.

So where would Peterson need to fall for he and Pierce to be the better running back pair? The answer is actually above 83 percent. Even if Peterson could be counted on to reach 14.0 points on four of five occasions, the chances of both he and Pierce doing it together would be only 24 percent.

And if we drop Pierce's probability just slightly to 25 percent—not unrealistic for a low-priced running back option who offers good $/point value—AP would need to score 14.0 points 100 percent of the time to make the Pierce/Peterson combo equal to the Spiller/Bell duo, and that obviously ain't happening.

Regardless of how you structure the numbers, the point is that players can have drastically different outlooks, even if they have the same $/point values, when you begin to assess them in terms of probabilities.

## Points > Value

The reason that thinking probabilistically is valuable is because it places the emphasis back on projected points. Sometimes, daily fantasy players seem to get so caught up in value that they lose sight of the bigger picture: scoring a buttload of points.

Always remember that your goal isn't to maximize value at all costs. You could do that with a team of players who fill up only 60 percent of your cap. Instead, the ultimate goal is to maximize projected points (or the probability of your lineup reaching a certain threshold), and $/point or any other value system is just a tool to help you accomplish that.

## Total Player Exposure

When you create your lineups, you'll need to decide how much you want to diversify. Some of the game's top pros don't do much lineup diversification at all.

CSURAM88 is one of them. "I play just one head-to-head lineup on each site. And even then, I'm using a lot of the same players across sites. But I don't put too much of my bankroll down on each one, so it's not really that risky."

For pros, diversifying lineups just means choosing sub-optimal players. Most will draw from a fairly large player pool in tournaments, but few play more than one (or at most, two) lineups in other leagues.

However, most pros don't have more than 20 percent of their total cash in play at any given time, and many fall below that mark. If you have a $100,000 bankroll, 20 percent is a whole lot of dough. If you have a $100 bankroll, not so much.

If you have limited funds and you want to put more than 20 bucks into play in a given night or weekend, though, you'll need to diversify just a bit more. What you should focus on most is the total exposure to each player.

In some cases, you need to be careful about using certain players together (a quarterback and his receiver in a heads-up league, for example, or two opposing running backs in a tournament). Otherwise, assuming the potential players you could place in your lineup aren't dependent on each other for production, the combination of players is less important than how often each individual is in your lineups.

If you're playing three DraftKings lineups and placing five percent of your bankroll into each one, the odds of you cashing depend on how the lineups differ from one another. If you don't have any of the same players, the leagues will basically be isolated events. If you use the same group of core players and switch around, say, a couple pitchers, the likelihood of winning or losing all three leagues will be much greater.

Thus, the lineups themselves matter less than the exposure to each player. Speaking with daily fantasy pros, it seems like most hover somewhere around the 10 percent mark for the ceiling on player exposure. That means if you're playing in three lineups with five percent of your bankroll in each one, you'd want to make sure no player is in more than two of those lineups. If you're putting only three percent down on each lineup, on the other hand, you can have your top guys in every lineup.

## Hitting the Cap

If you sift through any daily fantasy sports forum or chat, you'll see that one of the most discussed topics is whether or not it's okay to leave cap space on the table. While there are a handful of players who trust their ability enough to go with their true optimal lineup and leave significant cap space out there, almost all of the pros at least come close to utilizing the entire cap.

In my opinion, you should almost always use the entire cap, for three reasons.

### Fallibility

I must sound like such a pessimistic asshole by now. "You're going to be wrong. You're not as good as you think. Prepare for the worst." SHUT UP ALREADY.

No really though, once you start to account for your fallibility, you won't be so set on a particular lineup, increasing the value of filling up your cap.

### Fragility

Remember, projections and player values are fragile—susceptible to big fluctuations via rather small changes in information. Because of that, it makes sense to hedge with the final reason to utilize all of your cap space...

### Diversification

Since I advocate selecting from a player pool just slightly larger than your typical pro, it becomes easier to use all of your cap space. If you like a lineup that costs $59,000 out of a possible $60,000 on DraftKings, the number of viable options means you'll be able find somewhere useful to spend that extra $1,000.

All told, most pros usually come extremely close to using all of their cap space. "I tinker with my teams until I at least come close to filling the cap," Notorious told me. "I never leave more than one percent on the table. The only time that becomes an option is on a night in MLB when there isn't a full slate of games, and thus fewer players, or on a site that has weak pricing."

## Walking You Through a Week of NFL

Because of the access I have to the game's top players, I thought it would be cool to walk you through their processes in both NFL and MLB. I spoke with four of the best daily fantasy players in the world—all ranked in the top 10 in at least one sport—to give you an idea of how they approach football and baseball.

### CSURAM88's Approach to NFL

CSURAM88—Peter Jennings—is in this book quite a bit because, since working on  my first daily fantasy book, we've become good friends. Peter is basically a full-time player at this point (how cool is that?), and I can say that he's hands-down the brightest NFL mind I've met.

We talk each week about upcoming values, and the amount of time he spends on research is always apparent. I really couldn't recommend a better player to teach anyone about daily fantasy football (or basketball—his favorite sport). Let's let him walk you through his NFL process.

"I start by looking at each site's salaries on Tuesday just to get a sense of who might be valuable. Sometimes people get so involved with the numbers that they don't even think about who just pops out as an obvious value.

Throughout the week, I watch a lot of film. That's important in the NFL because the stats aren't necessarily standardized in the same way as other sports. And there might be different aspects of each contest—the game flow or just how a player looks—that you can't see in the box score.

There's a lot that can go into my projections, but I really take advantage of the Vegas lines. They're so helpful and if I'm bullish on a player in a game that Vegas thinks will be high-scoring, that's just more confirmation that he's probably a great value.

One of the ways I use the lines is to project player touchdowns. Let's say the Broncos are projected to score X points. I can look at their past scoring distribution to get an idea of how many touchdowns that will be. Then I start to distribute those scores to players based on their past likelihood of scoring.

So if the Broncos are projected to score four touchdowns and I know that Peyton Manning accounts for 75 percent of Denver's touchdowns, then my projection for him will be right around three touchdowns. Even though the total touchdowns can change versus different opponents, a player's "market share" of touchdowns on his own team tends to remain the same over time.

I also use Vegas player props to get a sense of projected yards, but I'll tweak those based on different information we learn throughout the week. I look at stats on Football Outsiders, Pro Football Focus, and RotoGrinders.

I specifically look at target data for receivers and snap data for all players, especially running backs. Running backs need lots of carries to give you value, so the snaps are important. When a running back is seeing a lot of snaps and is playing on a team that Vegas projects to win easily, he'll probably have a heavy workload. Red zone and goal line snaps are really important, too. I target players who get the ball near the goal line because they usually account for a high percentage of their team's touchdowns.

In addition to snaps, I also care about players' "percentage of workload stats," which are at RotoGrinders. So not only how often is he on the field, but how frequently does he see the ball when he's playing? What percentage of his team's touches, yards, and touchdowns does he account for?

Another stat I examine is how each defense performs versus particular positions. I think that's a step or two ahead of most players. If a novice is projecting a No. 1 wide receiver, for example, he might look at how many yards the defense has allowed. A little bit better player might look at how the defense performs against only wide receivers. But I'd look at how that defense performs solely against the other team's top receiver. Maybe they're poor against the pass overall but have one really good cornerback who shuts down No. 1 receivers.

I also use Pro Football Focus for a lot of that data because they have individual defensive player stats. So if I see news that a particular cornerback will shadow a receiver or a linebacker will play most snaps against a pass-catching running back, I can examine their coverage stats to see if it's smart to target or fade the offensive player.

There are so many injuries and personnel changes in football each week that value can shift really quickly. If a backup running back is thrust into the starting lineup, for example, that's obviously going to drastically change his projection. But even small news, like a coach saying he's going to get a wide receiver more targets, can be useful. So I just stay updated with player news throughout the week and alter my projections accordingly.

I use Excel Solver to give me optimal lineups based on my projections, but I don't just pick the top one no matter what. There's all sorts of considerations that go into the lineup, like the player combinations, their upside and risk, and the league type.

I make one optimal lineup for head-to-head games on each site, and I start to enter those on Saturday. I usually stay up all night submitting lineups and don't stop until right before kickoff on Sunday. So I'm pretty tired by the time the game's start. But you have to monitor your players, obviously, in case a questionable player is a late scratch or something.

Also, I usually do heads-up lineups first and then tournament lineups later because it gives me extra time to see where there might be overlay in some tournaments. When there's lots of overlay, I'll usually enter a lot of lineups because that's clearly a +EV situation for me."

And there you have it.

### Headchopper's Approach to NFL

In Week 9 of the 2013 season, a man who goes by the name of headchopper turned in what many consider to be the  greatest week in the history of daily fantasy football. Winning tickets to all of the industry's major qualifiers, headchopper took down the DraftKings Sunday 200 Grand (for a $25,000 grand prize) and won a ridiculous 25 tickets into the Millionaire Grand Final. You could buy into that tournament for $1,500, meaning the value of those tickets was just under $40,000. Not a bad weekend.

Headchopper posted nearly 300 points on DraftKings that week (yes, 300 points in the NFL), which is outrageous. It wasn't just a lucky week, though, because headchopper is consistently near the top of the GPP leaderboards. At the time of this writing, he's the sixth-ranked tournament player in the world.

I spoke with the man, the myth, the legend about his NFL strategy.

"My approach to daily fantasy football is a little bit different than most because I don't put as much stock in all of the projections. I think it's more valuable to spend time researching players, looking at matchups, and reading analysis throughout the week.

The first thing I do each week is look at the player salaries. From there, I can create an initial player pool of guys I like or would potentially use. That changes each week, but it's usually around 12 quarterbacks, 18 running backs, 24 receivers, and 12 tight ends.

Then I cut down that list based on matchups, injuries, and stuff like that. I look around the internet at all sorts of analysis and I study others' rankings. I don't use anyone else's rankings in isolation, but if a lot of guys I respect are high on someone, then I'll take a closer look at him too.

Once I have a smaller player pool, I can start making lineups on Saturday. I usually enter cheap tournaments—like $1 and $2—as a way to practice making lineups. I just mess around with different player combinations and just try to figure out what I like. I'll usually fall in love with a couple of those, so those are the lineups I enter into larger tournaments.

A lot of daily fantasy players use lineup optimizers and other tools like that, but I think there's so much subjectivity in it that I'm better off just making lineups on my own by hand and figuring out what works. I play a lot of tournaments, so I'm always looking for upside. I don't necessarily force stacks, though; it just depends on the situation. For example, some quarterbacks are really likely to throw to their No. 1 receiver—Matthew Stafford to Calvin Johnson, for example—so they pair better together than a quarterback who spreads the ball around more.

Another thing that I don't do is intentionally fade high-value players. I know some people like to do that in big tournaments to create a unique lineup, but if a guy is a great value, I'll play him. I also don't play guys who aren't great values just to force them into my lineups. I just go with who I like.

I usually have a handful of players I'll target, and I use them pretty much everywhere. So in Week 9 when I scored all those points, I really liked Andre Johnson and T.Y. Hilton. I put them in pretty much every lineup and then diversified around them. Sometimes I like even more guys as my core and I'll just mix and match my other values around those guys. So I don't necessarily hedge as much as the average player because I want to stick to the core group of optimal values.

On DraftKings, I place a lot of emphasis on receivers. I usually pay for receivers pretty heavily since it's full PPR and you can also use one in the flex. I almost always have a wide receiver in the flex in that format."

## Walking You Through a Day of MLB

To walk you through a typical day of projections and lineup creation for daily fantasy baseball, I spoke with one of the game's premiere cash players (Mike5754) and the current No. 1 ranked tournament player in the world (naapstermaan) to give you two different perspectives.

### Mike5754's Approach to MLB

Sticking primarily with head-to-heads and 50/50s, Mike5754's MLB strategy is about risk-minimization.

"After I download salary data from the sites and eliminate players I recognize as immediately overvalued, I look at the Vegas lines to see which pitchers are projected to do well. I always build my lineups around my pitchers, no matter the league, because they should be your source of consistent points. That usually means paying up for the more expensive ones.

One thing I do differently than a lot of players is pick hitters by targeting poor pitchers. I use the lines to see which pitchers are struggling, and then select from a group of potential batters who are facing those pitchers. I think the pitcher is so important and so consistent that it's really valuable to study pitcher stats for batters.

I also look at past stats for hitters, of course. I don't consider BvP stats much because they're usually fluky and small samples, but I do really care about batter vs. lefty/righty. Since I play mostly head-to-heads, I typically prefer batters with balanced stats versus lefties and righties. The reason for that is because starting pitchers can come out of the game pretty quickly sometimes, so you want hitters who can hit well against anyone. That raises their floor, which is obviously important in heads-up games.

If you pick a player who hits extraordinarily against righties but horrible against lefties, he could be in trouble if he starts slow and the opponent brings in a lefty within the first few innings. I'm more likely to target hitters with unbalanced lefty/righty splits in tournaments when I want upside. Otherwise, I'd prefer a hitter who hits .290 against both lefties and righties over someone who hits .310 against lefties but .200 against righties, even if they're both facing a lefty starter.

I also favor long-term stats more than recent play. I think hitters can get hot and cold in MLB, but for the most part, I prefer to play the law of averages. So I'd say I build projections based on long-term data and then adjust for recent play.

I don't necessarily mind small stacks in head-to-head leagues. Since I'm targeting batters against a handful of struggling pitchers, it usually ends up happening anyway. You just need to trust your research. If I'm playing teammates, though, I prefer to pick guys hitting near one another in the order because you can increase your upside with RBI situations, but it's not really risky.

After I've done projections, I'll create one or two optimal lineups for the night. I hedge a little bit just to limit my downside, but not too much because I want to stick to the top values. I also have very strict bankroll management, so that allows me to use just a single lineup if I'd like because I'm not going overboard with entries. I usually put around five percent of my bankroll into play in a given night in MLB, and never more than 10 percent.

I also enter more 50/50 leagues than I do in football. The reason is that with the consistency of pitchers, I think you can guarantee a pretty high floor, which you can't do as easily in football. So that makes my MLB lineups naturally better-suited for leagues in which a high floor is valuable."

### Naapstermaan's Approach to MLB

Naapstermaan is widely considered the top tournament player in the world. As of the time of this writing, here's a list of his biggest cashes.

The dollar amounts are just the buy-ins, so you can see how profitable he's been in his career. He won the largest single payout in daily fantasy baseball history--$125,000 in DraftKings' Midsummer Classic baseball championship. There's no better person to break down tournament strategy than naapstermaan.

"I start all of my baseball research by checking the weather. If it looks like it might rain in a game, I'll fade those players. I'll also fade hitters in stadiums with heavy wind blowing in or pitchers with heavy wind blowing out. So I immediately eliminate certain guys right off the bat.

Then I look at the Vegas lines, which gives me an idea of which pitchers to target. Because I play mainly tournaments, I use a pretty diverse group of players. I roll out different stacks and then fill in the pitchers based on the remaining salary cap. I do the opposite when I play heads-up, but I think pairing the right groups of hitters is important in tournaments so you can create synergy and increase upside.

The exact stacks I use are based on the payout structure. In qualifiers that pay out just the top entrant or other tournaments that have a top-heavy payout structure, I use a one-team stack. I think that gives me the most possible upside (Note from Jonathan: That fits with the DraftKings data). If it's a flatter payout structure, I might use a two-team stack because I don't necessarily need unlimited upside and it can help me cash it a little more often.

Same idea with the size of the tournament. In smaller tourneys, I might be more inclined to use a two-team stack than in a huge GPP that requires a massive ceiling.

In terms of stats, I actually look at BvP stats quite a bit, but not because I care that much about them for my own projections. I look at them because I know a lot of other players are doing the same, so it helps me figure out who might be really popular in a given night. If I'm deciding between two stacks and one has some hitters with quality stats versus that night's starting pitcher, I'd be more likely to fade that team so that I can have a unique lineup. That's a little contrarian strategy that's useful in tournaments.

I also like hitters who hit early in the order because they're obviously more likely to get more at-bats and more points. When I try to save money on a player who is really cheap, I almost always try to make sure he hits in one of the top four spots in the order.

I still think there's a lot of subjectivity that goes into daily fantasy, even in baseball, so the most helpful tactic for me is discussing potential plays with other good players. I like to chat with other pros throughout the day to get a sense of who they like and we can bounce ideas off of each other. So for anyone who is new to daily fantasy, I think finding experienced players you can trust and then picking their brains is the best thing you can do."

## Playing NFL on DraftKings

If you're playing daily fantasy sports, the general principles remain the same no matter where you play. But it's important to keep in mind that the implementation of those philosophies should change based on the site on which you're playing.

There are a few differences between daily fantasy sites, the most obvious of which are deviations in scoring and starting lineup requirements. So I want to give you an idea of what you should be examining when approaching either daily fantasy football or baseball at a particular site. And since we have all of this data from DraftKings at our disposal, they're the obvious choice to analyze.

So here are some of the subtle nuances of DraftKings' scoring and lineup requirements that mastering can turn a good player into a great one.

### PPR

DraftKings rewards a full point per reception, which changes the value of some positions relative to one another. The most obvious effect is that wide receivers and tight ends hold more value. PPR scoring increases the scarcity of the top receivers; it creates another category through which they can differentiate themselves.

It also decreases the importance of quarterbacks. Passers are already of limited value on DraftKings because they receive only four points per passing touchdown and one point per 25 yards. There's not as much scarcity at the position as if the quarterbacks got six points per score and one point per 20 yards, so it can be advantageous to go a little lower at the position, especially in tournaments when you don't need consistency. That also fits with the data, which shows that winning GPP lineups spend $350 less at quarterback than winning 50/50s.

### Bonuses

DraftKings gives you three points for 300 passing yards, 100 rushing yards, or 100 receiving yards. I think one of the areas daily fantasy players can go wrong is trying to predict bonus points. On the individual level, they're volatile enough to just ignore them. Sure, Drew Brees is more likely than a rookie quarterback to reach 300 yards, but that effect is the same across positions.

On the positional level, though, the bonuses mean something. If you're trying to figure out how to efficiently allocate cap space among positions, it's obviously valuable to know which ones are the most important. So I charted the occurrences of the bonuses from 2008 to 2012.

You can see that 100-yard receiving performances have been the most common in every season. Part of that is probably due to a larger potential player pool since teams could theoretically have three receivers go for 100 yards in a game, whereas typically just one running back has a shot to crack that barrier.

However, the difference in 100-yard games is large enough to suggest it's at least as likely that your receiver will go for 100 yards as your running back. Second, quarterback and wide receiver bonuses are increasing, whereas running back bonuses have remained steady, suggesting that quarterbacks and pass-catchers are becomingly increasingly more important in relation to running backs.

Again, bonuses are relatively fluky, so it's not like they should be an enormous factor in your projections; you should still consider paying for running backs in head-to-head leagues, for example, since they're more consistent than wide receivers on a weekly basis.

The main aspect of lineup creation in which bonus points are meaningful is the flex...

### The Flex

If you're searching for the biggest possible advantage you can uncover in the world of DraftKings daily fantasy football, you just found it. The flex spot allows you the opportunity to gain a major advantage over other players because most approach it inefficiently.

When I discussed the flex position with the top daily fantasy players, the majority said they almost always play a wide receiver in the flex in PPR leagues. On a $/point basis, wide receivers consistently offer the most value on DraftKings. Now, that's always subject to change if the site alters the way they price their players (so do your research), but it's unlikely to vary too much since pass-catchers naturally offer more value in PPR leagues.

So when I got this data from DraftKings on the win rates for head-to-head lineups with different positions in the flex, I was a little surprised.

I say "a little surprised" because you have to remember that running backs offer far more safety than wide receivers and tight ends. If you can find cheap running backs expected to see a surge in workload, it should be a fairly consistent source of points.

Further, my hunch is that most players have been using receivers pretty much interchangeably. By targeting solely slot receivers who have proven to be more consistent, the wide receiver-in-the-flex lineups might win a little more frequently.

The real surprise here comes at tight end, as only 44.1 percent of lineups that used a tight end in the flex won their head-to-head matchups. With 10,300 total leagues in this data set, the results are stunning and undeniably significant. Since tight ends usually don't offer value comparable to that of receivers in terms of $/point _and_ their week-to-week production is relatively volatile, it seems like you should avoid them as flex plays except in really extreme situations.

Meanwhile, this is evidence that running backs are indeed in play as head-to-head (and 50/50) flex options. They're predictable, and there's value in that. You can and should still consider wide receivers, too, especially those who see shorter targets from the slot.

Also note that this is perhaps substantial evidence that we need to concern ourselves with more than $/point calculations. The reason is that daily fantasy players are unanimous in agreement that running backs offer the worst $/point values on DraftKings. Yet due to their consistency, they make perhaps the best head-to-head flex options. When you begin to view players in terms of probabilities, traditional conceptions of value get turned upside-down.

In addition to head-to-head leagues, I also have data on how different flex strategies affect GPP success.

Again, this is shocking—the reverse of what we see in head-to-head leagues. These results suggest it is indeed smart to pay for volatility in large leagues that require upside. Yes, running backs are a more consistent source of points, but they might not have the same sort of home run ability as wide receivers (or even tight ends, relative to their salaries) in PPR leagues.

I wouldn't just go throwing tight ends in the flex, however. The reason is that the numbers are distorted due to the success of the NFL's elite tight ends (namely Jimmy Graham and Rob Gronkowski). How do I know that? This...

The average salary of a flex tight end in a GPP lineup was $5,329. The average salary of a flex tight end in a GPP lineup _that won_ , however, was $6,420. That's a huge jump and one that we don't see at the other positions. Actually, winning GPP lineups typically spend less on the flex at positions other than tight end—a position that truly seems distorted because of a couple outliers.

Thus, unless you're going to use an elite tight end in both the tight end slot and the flex position, it's probably smartest to stick with wide receivers as GPP flex plays on DraftKings.

## Playing MLB on DraftKings

Your approach to daily fantasy baseball can be even more site-specific than daily fantasy football since MLB scoring can vary so much. To give you an idea of how DraftKings' MLB scoring compares to that on other daily sites, I charted the difference between DraftKings and the aggregate of the other major sites as a function of points for a home run.

On DraftKings, for example, a home run is 10 points and a triple is eight (80 percent). Comparing values in this way allows us to gauge the relative importance of each stat in a more accurate way.

### Batter Scoring

Check out the relative importance of offensive statistics on DraftKings.

There aren't any major deviations here. DraftKings values singles, triples, and stolen bases just slightly more than the typical site, where they reward fewer points for RBIs, runs, and walks.

Note that hitters cannot go negative on DraftKings because they don't lose points for strikeouts. That's important because it increases the worth of batters who hit early in the order. Those players have a little extra value on DraftKings since every at-bat has a positive expectation; nothing negative can result from more plate appearances. Some daily fantasy pros like to stack the 1-2-3-4 hitters from two teams, for example.

The fact that you don't lose points for strikeouts means you can also do what the consistency stats from the previous chapters suggest is correct: pay for home runs. A lot of power hitters also strike out more than normal, but that won't hurt you on DraftKings. You'll need to balance between hitters early in the lineup and later guys since it's typically the 3-4-5-6 batters with the most power, but when you come across, say, a player who hits from the 2 spot and he's a home run guy, that's really valuable.

### Pitcher Scoring

I also charted pitcher scoring as a function of points for a win.

DraftKings doesn't take away points for a loss, but you lose more points for earned runs, walks, and hits. However, the high value of strikeouts and innings pitched more than compensates for the potential negative points. The site also weighs complete games a lot, although those are pretty rare.

Nonetheless, your goal on DraftKings should be searching for strikeout pitchers who typically go deep in games. To give you an idea of the value of a strikeout on DraftKings, consider that two of them equate to an entire win. That means you don't need to worry all that much about if your pitcher is on a great team that's expected to win the game; a guy who gives you eight innings and allows two runs in a loss is still plenty valuable.

### Two pitchers

You must start two pitchers on DraftKings, which of course increases their importance. The site's scoring increases the potential variance at the position; poor pitchers can have really bad games, and elite ones can give you something special. The matchups matter, but in almost every scenario, you want to pay for the consistency of top-tier pitchers _at both starting spots_.

### The Late Lineup Switch

As mentioned, DraftKings is unique in allowing late lineup switches, i.e. you can substitute out any player for another any time prior to their games starting. That's valuable for NFL leagues that start on Thursday night, but also for MLB weekend games.

On the weekend, the games are typically spread out throughout the day. On most sites, your lineup is locked once the first game begins. But what if there's a late scratch or a pitcher change? Well, you're screwed. Not so on DraftKings, where you can edit your lineup to ensure you never start someone who isn't playing.

## The 10 Laws of Projections and Lineups

Peter Jennings once told me "If you can create accurate projections, you can be a profitably daily fantasy player. If you can't, you won't." Here are the take-home points on making projections and constructing lineups.

Law No. 1: Use Vegas as a foundation.

They say what happens in Vegas, stays in Vegas. But if you're a daily fantasy player and what happens in Vegas stays there, you're leaving money on the table. This was a law from a previous chapter, too, because it's so important.

The Vegas lines are especially useful in NFL tournaments, when you're favoring massive upside, and in all types of baseball leagues. Because there's not a whole lot of time to perform in-depth projections on every MLB player since they play daily, many pros use the Vegas lines to target strong pitchers in low-scoring games and teams hitting against weak pitchers.

Law No. 2: Import salary data.

At least one of daily fantasy baseball's premiere players—Mike5754—imports salary data to immediately eliminate players he recognizes as overvalued. That cuts down on the pool of players he needs to analyze, thus reducing research time.

Either way, you'll need to import salary data to create values for each player. A $/point calculation is the most popular for most players—how many dollars you must spend per point you can be expected to score. While $/point shouldn't be used as a standalone tool in creating lineups, it's still important to identify the best values.

Law No. 3: Think about which types of NFL players will help you reach your goals.

In football, there are specific player types that continually perform the best—fast running backs with heavy workloads and big, red zone-relevant receivers, for example. If you want consistency, consider mobile quarterbacks, pass-catching running backs, and slot receivers. If you want a higher ceiling, emphasize speed at the running back position and a heavy workload, especially in the red zone, for all positions.

Law No. 4: Consider which stats are most important for MLB players.

Since baseball is a binary sport, the positions matter less than they do in football. A "prototypical" second baseman, for the purposes of daily fantasy, is the same as a "prototypical" shortstop.

The most consistent stats are home runs and strikeouts. That means you should generally load up on power hitters, typically in the 3-4-5 spots, and pitchers with a lot of Ks. Because of the deviation in those stats, you should favor them in all league types.

Law No. 5: Think about players in terms of probabilities.

Strict $/point calculations are important, but they also overvalue low-salary players. There's value in, well, value, but there's also value in pure points. When Jamaal Charles matches his projection, that's more valuable to you than if a running back who costs half as much does the same.

In addition to $/point, consider how likely each player might be to match certain thresholds. One player projected to score 20 points might have a significantly wider distribution of possible outcomes than another player projected at the same mean score. Because you should be targeting different sorts of players in different leagues, a probabilistic manner of thinking is just as valuable—perhaps more so—than the deterministic approach that stems from traditional value calculations.

Law No. 6: Don't leave more than one percent (and preferably less) of your cap space.

The main reason failing to utilize cap space is a problem is that it assumes your projections are flawless. Again, if you're thinking in terms of probabilities, you'll realize you're going to be wrong at times. Once you account for your imperfections, the pool of potential players you're willing to utilize increases, thus making it highly unlikely that the true "optimal" lineup doesn't come close to maxing out the salary cap.

Law No. 7: Monitor the weather, especially in baseball.

Wind can wreak havoc on passing games in the NFL, but football games almost never get cancelled. Meanwhile, baseball games get rained out all the time. Starting a player or two in a game that rains out can be disastrous. Further, you should monitor wind speeds to help you select batters and pitchers.

Law No. 8: Utilize late-game swap when possible.

The primary advantage that a late-game swap feature offers is that it allows you to play your true best lineup right out of the gate, regardless of when leagues begin. If you're in an NFL league that starts on Thursday night, for example, you can use a player who is questionable for a Sunday game, knowing that if he doesn't go, you aren't in major trouble. When that feature is taken away, it decreases the aggressiveness with which you can approach early lineup choices.

Law No. 9: Understand site scoring and lineup requirements.

It's absolutely critical to understand site scoring and lineup requirements. There's a massive difference between standard scoring and PPR scoring in the NFL, for example, and failing to account for that could ruin your chances for success.

For football, DraftKings is a full PPR site that gives bonuses for 100-yard rushing/receiving games and 300-yard passing games, so that benefits wide receivers. Since DraftKings requires you to start someone in the flex, it really increases the importance of wide receivers.

For baseball, DraftKings has no negative points for hitters, increasing the value of those hitting at the top of the order who will see the most plate appearances. Pitcher scoring is volatile, but it increases the deviation between elite and low-end guys. In almost every case, you should be play a high-priced pitcher in both starting spots.

Law No. 10: Don't overlook subjective factors.

Although most daily fantasy players project players, the best ones don't let the numbers control their actions. There are all sorts of subjective factors that should influence your decisions, so don't feel like you need to follow the data 100 percent of the time.

The main reason for that is because the process of researching, doing projections, and creating lineups is valuable in and of itself. If you approach daily fantasy with the sort of "I have a lot to learn" mentality that makes the greatest players as good as they are, your subjective thoughts will be influenced by objective factors anyway, so it's okay to "trust your gut" when that's the case.

(Bonus) Law No. 11: Consider running backs in the flex in heads-up leagues, but not in tournaments.

I think the flex data from DraftKings is so interesting that I gave it its own bonus law. It's particularly noteworthy that running backs, despite generally poor $/point value relative to the other positions, make for the best flex plays because of their predictability.

In large GPPs, though, you should typically fade running backs as flex plays and target either a big-play wide receiver or an elite tight end—both of whom can be excellent sources of scarce upside.

" _The creation of a thousand forests is in one acorn."_

  * Ralph Waldo Emerson

## An Appendix of Extra Data

" _It is a critical mistake to theorize before one has data."_

  * Arthur Conan Doyle

This book has obviously been extremely analytical, so my sincerest apologies go out to those of you who thought you were getting a vague, subjective book that would propose no actionable advice (but I assure you there are plenty of those on the market for you). So here we are, stuck with all these numbers that can help us make lots of money.

Seriously though, I hope I've been able to break down the math and data in such a way that it makes sense. Einstein (I think it was Albert, but it might have been his little-known brother, Jimmy) once said "if you can't explain it to a six-year old, you don't understand it," and I find that to be true in many ways. The data itself might get complex at times, but the bottom line should be simple.

I've spent the last few years performing all sorts of fantasy sports-related analyses in an effort to become a better player. When combined with all of the incredible data provided to me by DraftKings, I'm sitting on countless Excel spreadsheets that I feel hold information that could lead to actionable advice for daily fantasy players. I collected all of the data on prototypical NFL players from the last chapter, for example, and it's shaped the way I approached daily fantasy football.

So this chapter will be a collection of just a few pieces of data for which I couldn't necessarily find a spot in the main portion of the book. If any of it helps you become a better daily fantasy player, that's great, and if not, well, uh, just pretend I didn't even write it.

Note that you won't find "The 10 Laws" at the end of this appendix. Those sections were meant to provide "bottom line" analysis, but I hope to interpret each piece of data and provide meaningful advice within the text of this chapter.

## Pitching vs. Hitting

For much of this book, I've talked about the importance of paying for pitchers. Here's a little more evidence that's the way to go.

The typical DraftKings winning lineup has spent 0.3 percent more on pitching than the average team. Again, with the thousands and thousands of lineups analyzed, that's a significant number. It's sort of like the difference between a 4.39 40-yard dash and a 4.49 40-yard dash or the difference between mercury levels of 0.30 parts per million and 0.50 parts per million in your fish—small differences, large effect.

*Note: I quickly Googled that mercury thing and I have no idea if that really constitutes a big difference in mercury, but the first site I visited suggested that's the case, sooooooo, yeah.

## NFL Defensive Strength

A little while back, I did some research on defensive strength from season to season, analyzing how specific defensive ranks (such as run defense) carried over from year to year.

There seemed to be no correlation between pass defense from one year to the next, a moderate correlation for team interceptions, and a strong correlation for run defense.

To test that further, I looked back at the strength of the run defense correlation over a five-year sample.

While pass defense is relatively fluky from season to season, run defense remains very consistent. But why?

I think the answer is that run defense more accurately reflects actual team strength. The best teams are typically winning late in games, so their final run defense rank is usually pretty high since they don't see as many attempts. It's just the opposite for the worst teams, who see a lot of rushes late in games.

You might say that we should see the same sort of effect with pass defense; if the best teams are winning and get thrown on a lot, they should give up more yards. That's true to an extent, but it's also important to remember that pass defense is more vital to team success than run defense. Many times, teams acquire leads by throwing the ball effectively and stopping the pass, then milk it away with the run. So the winning teams that get passed on a lot late in games probably didn't give up many passing yards earlier, meaning they wouldn't rank as low overall, despite the extra attempts.

I think this data has obvious uses early in the season. Namely, we don't necessarily need to be too concerned about team pass defense early in the year; chances are it won't resemble what we saw in the prior season. The opposite is true for run defense.

However, there's another important point here; perhaps we should care more about the opponent for running backs than any other position. Remember, early passing success typically creates a lead, which results in fewer passing attempts late in the game. That means the numbers for quarterbacks, wide receivers, and tight ends might "even out" a bit as games progress; if they're efficient early, they'll get their numbers then. If they're not, they'll probably be down in the game and make up for it with more attempts late.

The same effect doesn't exist for running backs; most of them are more dependent on game situations for touches. When you consider the consistency of run defense, it means we might want to place more weight on favorable matchups for backs than other positions. The easiest way to do that is to search for running backs with projected heavy workloads on teams that are the favorites to win the game.

## Best Player Owned Frequency

One of the strategies proposed in the chapter on tournament play is using a contrarian approach—purposely going against the grain in order to create a unique lineup. Up until now, it was just assumed that bypassing a few "obvious" values was optimal in tournaments. Now, the evidence is in.

This graph shows the percentage of winning GPP lineups with top-scoring players in specific usage brackets (represented with the percentages at the bottom). For example, the two bars all the way to the left show the percentage of winning GPP lineups in both NFL (12.9 percent) and MLB (23.3 percent) with a high-scoring player who was owned on anywhere between one and five percent of all lineups.

The data on this graph is extremely interesting and should make us think about how we structure our tournament lineups. Namely, look at how important it is to have a low-usage player who erupts for a huge game. Of all winning GPP lineups, 45.2 percent of daily fantasy football teams and 41.4 percent of daily fantasy baseball teams hit on the top-scoring player who was in 10 percent or fewer lineups. That's pretty remarkable.

At the other end of the spectrum, you can see there's a jump in the frequency of lineups with the best player who was 51-plus percent owned. That might be due to a larger player pool, but since not many players are ever in more than half of lineups, it could also be evidence to not forgo elite values. When a player stands out as the clear-cut top value, use him, regardless of the league type. It's the second-tier values you might want to fade in favor of less-utilized players.

" _If you stop at general math, you're only going to make general math money."_

Snoop Dogg

# **Section III: Sample from**  The Ultimate In-Season Weekly Guide

 Fantasy Football for Smart People: The Ultimate In-Season Weekly Guide is filled with data-driven fantasy football analysis designed to improve your in-season decision-making, from projecting players to trade strategies to daily-fantasy-specific advice. With _The Ultimate In-Season Weekly Guide_ , you'll learn:

  * How to handle the flex position on a weekly basis

  * Why using the Vegas lines can make you a better in-season decision-maker

  * How to handle "questionable" players

  * Which player types you should target for either consistency or upside

  * How to use game theory in season-long and daily fantasy football

  * How to win on DraftKings

_Fantasy Football for Smart People: The Ultimate In-Season Weekly Guide_ is ideal for daily fantasy players who want to make better lineup decisions or season-long owners who struggle to get the most out of their teams. Using hard numbers to either confirm or debunk popular fantasy football narratives, the guide is a scientific, analytical look at which in-season moves are _really_ the best.

Whether you play traditional season-long fantasy football or want to kick ass on daily fantasy sites like DraftKings _, The Ultimate In-Season Weekly Guide_ contains the tips and advice to give you the edge you need to become a profitable player and long-term winner.

## How much do matchups matter?

As I pen this article (I like to think of myself as an old-school writer who uses a quill pen), I have Peyton Manning ranked seventh among quarterbacks in projected fantasy points for the week. Vegas has the Broncos, who broke pretty much every meaningful passing record in 2013, projected to score just 23 points.

There's nothing wrong with the Broncos' offense—no one is hurt, they aren't struggling—but they play the Seattle Seahawks and their world-class defense this weekend. If you need any evidence that matchups matter a ton in the NFL, you don't need to look any further than Vegas.

But the Vegas lines don't tell the whole story, especially as it relates to specific positions and their fantasy prospects. I recently created a really awesome database of matchup data, broken down by position, and I plan to do a whole lot of different analyses off of it. That should help with making better lineup decisions in season-long fantasy football, as well as kick ass on a weekly basis on DraftKings.

## The Matchup Numbers

For each position, I sorted fantasy production into buckets based on the quality of the defense (at the time of the game): top 10, middle 12, and bottom 10. I didn't look at total defense, but rather pass defense for quarterbacks, wide receivers, and tight ends and run defense for running backs (I actually looked at total defense, too, and the numbers were unsurprisingly stronger for each position versus a specific aspect of the defense).

Here are the overall numbers, which include fantasy starter-caliber players over the past five years.

What we should be looking for here is how much of a jump there is from production versus top 10 defenses to production versus bottom 10 defenses. To help with that, I charted the percentage improvement in fantasy production.

Running backs improve the most here, suggesting that matchups might matter more for running backs than for the other positions. That is, a running back facing a top 10 run defense is in a worse spot than a tight end facing a top 10 pass defense, for example.

I think that there are two reasons for this. The first is that the matchup does indeed matter more for backs since run defense rank (in terms of total yards) is a better indicator of team strength than pass defense rank.

That might seem backwards, but it's something I showed in the past. Basically, passing yards tend to "even out" a bit because of game flow; teams that are winning usually passed the ball effectively to gain a lead, but then they stop throwing the ball so much. Meanwhile, the losing team probably wasn't effective through the air early, but they make up for it some by throwing the ball a lot late. There's no such relationship with run defense since running the ball effectively isn't very strongly correlated with winning games.

The second reason that the running backs come out ahead of the receivers and tight ends is perhaps because run defense is directly applicable to a running back's production, while pass defense isn't so straightforward; a pass defense might be really good overall but horrible against tight ends, for example, which would make the correlation a little weaker.

Another way we can look at the data, though, is just in terms of expected fantasy points. That's all that really matters at the end of the day, right?

Looking at pure fantasy points, we see that quarterbacks jump running backs in terms of expected drop-off. That's due primarily to quarterbacks just scoring more points in general, but it's still useful to know that a quarterback facing a top 10 pass defense will likely see a bigger dip in bulk fantasy points than any other position versus a top 10 defense.

## Confidence in Ranking Defenses

The last thing I want to say is that we need to be careful when ranking defenses. Sometimes the numbers matter, and sometimes they don't. When your wide receiver is facing a top 10 pass defense in Week 2, that's basically useless; with just one week of data, we have no idea who is good and who isn't.

Some of my other research suggests that defensive rank starts to become meaningful around the middle of the season. After around Week 8 or 9, you can probably start to trust the numbers a bit more. Before that time, I'd use a combination of this year's numbers, last year's stats, and of course cat-like intuition.

Also, there's clearly a relationship within each of the defensive buckets into which I sorted data such that we can't just treat all top 10 defenses the same. This experiment was more just to show that matchups do indeed matter (a lot) and to give you a general idea of how much.

## The Fourth Quarter, Vegas Lines, and Passing Stats

There are a lot of ways that fantasy football can be different from NFL football; we don't always need to know which players are good in real life to succeed in the fantasy realm. However, I think there's value in understanding players and the NFL game in general, as exploiting inefficiencies in the NFL (which are present all over the place) can lead to fantasy success.

I used to cover the Dallas Cowboys, and I feel like I had a pretty good grasp on what the team did well and poorly such that it gave me a pretty big advantage in regards to their players in fantasy football. I know that Jason Garrett has historically come out very conservatively on the road, for example, which aids in weekly player projections.

I also like to look at head coaches' aggressiveness on fourth downs; those coaches who are risk-averse end up costing their offenses valuable fantasy points. Like I said, the inefficiencies are plentiful, but one of the biggest is an ultra-conservative play-calling philosophy after gaining a lead. How many times do we see an offense that has a three-point lead in the fourth quarter just completely "sit on their lead" by running the ball way too much, then lose the game because they were forced to punt it away?

Hint: It's all the fucking time.

When a team has a late lead, they should indeed be more conservative with their play-calling. The degree of such a conservative philosophy should depend on the score, and unless you're up by multiple touchdowns, the correct move isn't a run-the-ball-on-first-and-second-down-and-then-attempt-a-short-pass-on-third-down-short-of-the-first-down strategy that we see so often. For the most part, unless time is ticking away near the end of the game, offenses with very small leads should run very close to their normal offense.

But outside of a few exceptions, they don't. And it has fantasy football ramifications.

## Fourth-Quarter Play-Calling and Stats

I knew we'd see some dramatic differences in fourth-quarter play-calling based on the score, but what I found is pretty shocking. Here's a look at the difference in passing attempts and YPA for offenses that are either leading by 14 or trailing by 14 in the fourth quarter.

Note that, although there's a difference in efficiency, it's not that extreme. This is par for the course with quarterbacks; the difference between the best and worst in terms of efficiency isn't monumental. Meanwhile, the difference in passing attempts can be substantial, especially on the level of an individual game or quarter.

Overall, offenses down by 14 in the fourth quarter rack up around nine more passing attempts than their winning counterparts. That leads to an average of 73 fourth-quarter passing yards for the quarterback of a team down by two touchdowns or more in the final frame, compared to just 21 passing yards for quarterbacks on the team with the lead. That's a substantial difference—around 3-3.5 fantasy points, depending on your scoring system.

And that's just from yards alone. Here's a peek at touchdowns/interceptions in the fourth quarter of 14-point games.

Teams that are trailing by 14 or more points throw way more interceptions, but they also toss a lot more touchdowns, too, and the scores more than make up for the picks. In leagues that award four points for passing touchdowns and deduct two points for interceptions, quarterbacks on teams that are trailing by 14 points score an average of 1.30 fantasy points in the fourth quarter from their TD/INT totals. Quarterbacks on winning teams total just 0.66 fantasy points.

If we add it together, we're looking at an additional four fantasy points for quarterback who are trailing big in the fourth quarter. That's just from the passing stats, too; if you have a mobile quarterback, he's more likely to rack up rushing yards on scrambles.

Having said all that, these results are somewhat surprising, but not a total shock since a 14-point lead is a big one that _should_ dramatically alter play-calling. But what about in close games that are within one touchdown? Offenses with a small lead aren't just dicking around by keeping the ball on the ground, right?

Even though it's a smaller gap, teams down by less than a touchdown are throwing the ball more than twice as often as teams up by seven or less in the fourth quarter! With basically no difference in YPA, that equates to twice as many fourth-quarter passing yards, too.

And what about touchdowns/interceptions?

These gaps are smaller than in 14-point games, but still pretty large. The fact that offenses down by less than one score are throwing twice as many fourth-quarter touchdowns as those with the lead is pretty incredible. These numbers display why, given the choice between down by four points or up by four points in the fourth quarter, I'd actually choose trailing for my team in many situations (assuming they have the ball). NFL offenses are simply way, way too conservative when they have small leads, even late in games.

## How to Benefit

It's one thing to know that a team is going to post worse fourth-quarter passing stats _if_ they have a fourth-quarter lead, but is it actionable? I think it's at least worth a try with the Vegas lines. In most cases, Vegas is going to be correct more than us in their prognostication of game outcomes. Their lines can give us a really accurate representation of expected game flow.

Take this line, for example: PIT (-3) vs BAL – Over/Under 31 points

In this case, we can deduce a projected final score of 17-14—close and low-scoring. This is a situation we'd want to avoid with passers. One reason is of course the low over/under; at 31 total points, there's not much meat on the fantasy bone. The other reason is the close spread; at just three points, we can't be very confident that Pittsburgh will indeed be leading in the fourth quarter. There's nothing actionable here.

Compare that to this game line: DEN (-10) @ DAL – Over/Under 64 points

In this case, the projected final score is 37-27—lots of potential scoring for both quarterbacks. Further, because the spread is high, we can be fairly confident that Denver will be leading in the fourth quarter, forcing Tony Romo to rack up more passing attempts. In this case, Romo is probably a good start because 1) the Cowboys would still be projected to score a lot of points and 2) Romo is likely to maximize his output with this sort of game script.

Note that this doesn't at all make Romo a better play than Peyton Manning, clearly. But in fantasy football, everything comes at a price. While this information can help you make lineup decisions in season-long leagues—generally side with the quarterback on the team projected to score the most, especially if they're a close favorite or underdog—it has even more use in daily leagues.

On sites like DraftKings, you need to work within the confines of a salary cap. Each player has a cost, so lineup decisions aren't nearly as straightforward as in season-long leagues. Manning will almost always be a better play than Romo straight up, but that might not be the case when you consider their salaries. And if we have good reason to believe that Romo will be down in a game in which Dallas will still score a good number of points, that's probably a situation we want to target; relative to his cost, the fact that Romo will be throwing the ball a lot in a high-scoring game could make him a better value than Manning.

## How to Treat Backup Running Backs Who Become Starters

I wrote an article a few months ago that examined how backup running backs perform when they're thrust into the starting lineup. I focused on how it should affect your draft strategy, primarily whether or not you should handcuff your best running backs. For the record, the answer was yes with three contingencies: the cost of the backup is low, he'll get the majority of the workload if the starter goes down, and he has sufficient NFL-level talent.

The crux of that argument was based on this simple graph.

From 2009 to 2013, starting running backs averaged 4.24 YPC, while backups averaged 4.17 YPC. There are probably lots of reasons that the figures are so close, including "better" carries for backups and the fact that running back is such a dependent position.

Whatever the reason, running efficiency is arguably (much) more dependent on the offensive line than the running back. We see very mediocre running backs finish as high as RB1s in fantasy football quite a bit. Knowshon Moreno was the No. 4 overall running back in 2013 and I'd argue that there are dozens of _backups_ in the NFL who are better than him. If you let me play with Peyton Manning and I see a heavy enough workload, I don't see any reason why I personally couldn't rush for 2,500 yards in the NFL. Easily.

## Don't Be Afraid

Once a backup becomes a starter...he's no longer a backup. That's the type of insight you're paying for, right?

Once a backup becomes a starter, there's typically a value opportunity to be exploited. In season-long leagues, I think it makes sense to buy low on newly anointed starting running backs, assuming the original starter is out for an extended period of time.

You might argue that you're not buying low on someone whose stock just soared, but I disagree. If you can pay a 'B' price for an 'A' fantasy player, it doesn't matter if he was a 'D' player yesterday. If he's an 'A' running back now and you think he'll continue to be that moving forward, you're buying low.

Typically, most owners are going to factor in some uncertainty with backups who haven't been starters, reducing their cost. There's really not much uncertainty, though; running back is such a plug-and-play position, and outside of the few truly elite talents like AP and Jamaal Charles, the position is really commoditized. Pick a running back, any running back, and he can probably be productive if given opportunities.

That's evident when you consider how RB2s have performed when they take the lead.

Despite rushing for slightly fewer YPC, backups have scored more fantasy points per game than the original starters since 2009. Are the backups really better? No, but 1) it's close, 2) teams aren't going to just stop running the ball, and 3) a lot of backups are groomed to be pass-catchers who specialize in third downs, which helps their fantasy value.

If you own a backup who has even a decent level of talent and he's going to see the same workload that the starter did, don't trade him (unless the starter is expected to be out like a week or something); normally, market value won't approach actual value. If you don't own such a player, consider trading for him.

In daily fantasy leagues on sites like DraftKings, there's no prettier sight than when a starting running back tears his ACL. Wait what!? Just kidding (but not at all). When a running back gets injured, his backup will normally be priced too low on the daily sites. That's particularly true if the back gets injured during the week, because the site will have already priced the players, so the backup will normally be much too cheap. You can make a lot of money by targeting low-priced backups who start because the normal starter is a last-minute scratch, for example.

## Emphasize the Same Traits

The numbers are obviously an average and you still need to consider every case individually. Though most of the time you don't need to be scared off by backup running backs, you should still emphasize the same traits you want in any back: a heavy workload, first and foremost, followed by explosiveness (as measured by the 40-yard dash and broad jump), and pass-catching ability.

The first of those is ultimately the most important. Running back production is 90 percent about workload. Fantasy football strategies can get pretty complex at times, but it doesn't get much simpler than "target running backs who are going to touch the ball a lot, dumbass."

## Does playing in a dome help an offense?

I was perusing some daily fantasy football salaries and matchups for this week and I couldn't help but be drawn to Aaron Rodgers. Even with an expensive price tag, Rodgers' matchup with the Lions is drool-worthy. The Detroit defense is much-improved and can get to the passer, but who doesn't want to see Rodgers & Co. playing on the turf at Ford Field?

But then I thought about it for a bit, and I'm not actually sure whether or not passing games or offenses as a whole are actually better when playing indoors. We've been taught that offenses are more high-powered when playing inside, but I personally didn't have any data to back up that idea.

So I got some. Using the Pro Football Reference Game Play Finder, I looked up passing and rushing stats since 2010 for all teams that either play in a dome or a stadium with a roof (thus including a team like the Cowboys).

I initially wanted to simply compare home versus away stats, but I realized that would be problematic since teams are naturally worse on the road. That meant I needed to look at stats for all teams, broken down into home versus road, to establish some sort of baseline.

These numbers look pretty close, huh? Well, they are. Since 2010, home teams have totaled 7.31 YPA through the air, compared to 7.09 YPA for teams on the road. Meanwhile, home teams have rushed for 4.27 YPC, compared to 4.19 YPC for away squads.

Using those numbers, I calculated the expected drop in efficiency for a team playing on the road versus when they're at home.

On average, home teams are 3.02 percent more efficient through the air and 1.87 percent more efficient on the ground as compared to when they're on the road.

Why are those numbers the way they are and not closer? Probably because home teams win more often than road teams, and winning teams often call a lot of low-efficiency runs late in games to milk the clock. The rushing efficiency for home teams goes down because they're running against an eight or nine-man box late in the game.

## The Stats on Dome Teams

So now we have a baseline with which to work. We should expect dome teams to be more efficient at home than on the road, as all teams are, but the numbers would need to exceed the baseline we've established for us to conclude that playing indoors (or on turf, or out of the elements, or whatever) is really advantageous.

For passing efficiency, there is indeed a larger observed drop (home vs. away) for dome teams than we'd expect. Actually, it's about 57 percent higher than the expected drop, which is pretty sizeable. On average, dome teams are 4.77 percent worse through the air on the road as compared to at home.

But it's a different story with rushing efficiency. In that area, dome teams are actually worse at home than we'd expect, with the numbers being close to the overall league rate.

## Conclusions

I've thought about this one for a little while, and I think what we're seeing is the effect of weather. Inclement weather can really wreak havoc on the passing game. Whether it's cold temperatures, precipitation, or strong winds, it's difficult to pass in the elements.

The same isn't true of running the ball, or at least not to the same degree. I'm pretty sure that's why we see that dome teams aren't much better at home than on the road when rushing the ball, but they are a lot better when airing it out at home. Being able to toss the ball around the field in perfect conditions is a huge advantage.

I don't think there's anything overwhelming here, but there's still evidence to suggest that, if you have reason to believe a quarterback playing indoors is going to see a heavy workload, you can probably bet on him recording better-than-average efficiency to go along with it, all other things being equal.

## Demaryius Thomas, Andre Johnson, and Opportunities to Score

In 2013, pretty much every skill player on the Denver Broncos was start-able because the team had plenty of opportunities to go around. The Broncos were obviously an efficient offensive football team, but the historic pace at which they scored is what gave their fantasy weapons so much upside.

Fantasy football players typically get the majority of their points from yards, but it's the scoring that really matters. The reason is because there's a larger potential deviation in touchdowns than yards. Of the top 10 wide receivers in fantasy points in 2013, for example, Josh Gordon had the most yards (1,646) and Dez Bryant had the fewest (1,233). That difference of 413 yards represents just 25.1 percent of Gordon's total.

Meanwhile, of those same top 10 receivers, Demaryius Thomas had the most touchdown receptions with 14, while Andre Johnson had the fewest with five—just over one-third the total and a difference of 63 fantasy points. Here's a look at the average number of touchdowns for top wide receivers.

The true top-tier players—the Josh Gordon and Demaryius Thomas-esque receivers who win fantasy championships—score at an elite rate.

Meanwhile, take a look at receiving yards.

Not as much of a drop-off, especially from the top five to the top 10. Scoring is obviously critical to obtaining elite production from players, and most scoring occurs in the red zone. Even more so, most scoring takes place near the goal line. All other things equal, we should be targeting players who 1) see lots of goal line opportunities and 2) have the ability to make the most of them.

To see a lot of chances to score, players obviously need to be on teams that afford them those opportunities. Even for players who eat up a huge share of their team's red zone activity, they still need to get into position to score at a high rate to reach their ceiling for fantasy owners.

To give you an idea of how much offenses can differ in terms of nearing the end zone, consider that the Broncos had 109 snaps inside the opponent's 10-yard line in 2013, leading the NFL, while the Tampa Bay Bucs ranked last with only 47 plays. Also note that only one other team had more than 86 snaps inside the opponent's 10-yard line, which shows just how good the Broncos were.

If you're deciding between two players during your draft, those opportunities near the goal line should play a pretty big role in your decision, right? I mean, for a wide receiver on the Bucs to "make up for" the lack of opportunities as compared to one on the Broncos, he'd need 2.3 times as high of a market share of Tampa Bay's passing targets. That's crazy. Efficiency is great, but bulk opportunities win fantasy championships.

With that said, I wanted to take a look at the exact correlation between trips inside the opponent's 10-yard line and fantasy success for each position. Do certain positions rely on being near the goal line more than others? Can top players overcome playing on an offense that doesn't near the goal line at an elite rate? Let's find out.

## Quarterbacks and Plays Inside the 10-Yard Line

If there's one position where I'd expect trips inside the 10-yard line to correlate nicely with fantasy points, it would be quarterback. Even on teams with a dominant short-yardage rushing attack, you'd expect plays inside the opponent's 10-yard line to be a nice proxy for quarterback fantasy points since quarterback play is such a big factor in overall offensive efficiency. In short, you typically need a good quarterback to get into the red zone and near the goal line at an elite rate.

Here's a look at how snaps inside the opponent's 10-yard line correspond to fantasy production for the top 25 signal-callers.

There's an obvious upward trend. Here's how things shake out if we sort the data into buckets.

This is the money chart. The difference between the top five and top 10 (which includes the top five) is pretty large. Overall, a top-five fantasy quarterback takes his team inside the opponent's 10-yard line around 9.3 percent more often than the average quarterback.

These numbers aren't all that surprising because, like I said, reaching the opponent's goal line often necessitates a great quarterback. But what about the other positions?

## Running Backs and Plays Inside the 10-Yard Line

Looking at running backs and plays inside the 10-yard line, the relationship is almost exactly the same as with quarterbacks.

Again, I sorted the data into buckets—top 10 running backs, top 25 running backs, and all backs.

The big jump again comes in the top tier, with top 10 running backs typically seeing around 9.5 percent more plays inside the 10-yard line as compared to the average back. That equates to around seven extra chances to score per year.

## Wide Receivers/Tight Ends and Plays Inside the 10-Yard Line

Let me save you the suspense: the numbers are the same for pass-catchers, too.

It's actually strongest at the wide receiver position, with the typical top 10 wide receiver playing 12.7 percent more snaps inside the opponent's 10-yard line than the average wide receiver in general.

At tight end, the effect really picks up for the best of the best—the top five tight ends.

The typical top five tight end receives greater than 15 percent more snaps inside the opponent's 10-yard line as compared to a normal tight end.

## A Comprehensive Look

Sorting the data for all positions into equal-sized buckets, here's how quarterback, running back, wide receiver, and tight end compare to one another in terms of how frequently their teams near the goal line.

The biggest drops are at running back and tight end. There, the players on teams that near the goal line at an elite rate seem to benefit the most. That makes a ton of sense because those players are typically utilized quite a bit near the end zone—running backs on short-yardage carries and tight ends over the middle because of their size.

Meanwhile, although there's still a correlation at wide receiver, the truly elite top five options haven't reached the opponent's 10-yard line any more often than top 10 receivers. Could this be evidence that, although scoring opportunities obviously matter for all positions, they're more important for backs and tight ends than wide receivers?

I'm not sure what the answer is to that question, but the ability of wide receivers (and quarterbacks) to score from deep is much greater than for running backs and especially tight ends. Even though we still want to target red-zone relevant receivers, it stands to reason that the fantasy value of running backs and tight ends is more tied up in their ability to score touchdowns, which of course is easiest near the goal line.

## How to Use the Data

I don't think there's any doubt that a huge part of fantasy football success is finding touchdowns, which is easier to do when you can predict red zone opportunities (and specifically plays inside the 10-yard line). No matter the position, every player benefits from more chances to score.

There are all sorts of ways to project touchdowns, but one that might be worth looking into more is considering only plays inside the red zone or even inside the 10-yard line, particularly for running backs and tight ends, and then using market share numbers to decipher individual touchdowns.

Let me explain with an example. Let's assume you think the Vikings are going to run 75 plays inside the opponent's 10-yard line this year and score at a league average rate (29.9 percent in that range)—right around 22 touchdowns.

We can then look at the percentage of such touchdowns for which Peterson has accounted in the past using  PFR's Game Play Finder. Over the last five years, he's scored 39.2 percent of Minnesota's touchdowns from inside the 10-yard line. Thus, if we're projecting 22 such plays this year, we'd predict somewhere around 8.6 rushing touchdowns for Peterson when inside the opponent's 10-yard line. We could also go on to add longer touchdowns, but this is going to be a really accurate foundation because 1) most touchdowns occur near the goal line and 2) they're far more predictable than longer scores.

The take home point: near-the-goal-line plays matter for all positions, and they matter a lot, but they're most vital to backs and tight ends.

## A Bunch of Fantasy Football Data

I do a lot of fantasy football research, but I'm a complete loser who has nothing better to do, so that's to be expected. I put much of the research into my books or use it to develop my draft/in-season packages, but sometimes I find stuff that's interesting, except I don't really know what to do with it. Sometimes it could be that it's just useless, or perhaps there are conclusions to be drawn that I'm just missing.

Either way, I'm going to propose some of that data here, and I'll attempt to explain what I think is going on. Mostly, though, this is just a chance to publish some cool data that is interesting (or not) and could be actionable (or not).

## Vegas and QB Fantasy Points

I'm very intrigued with the possibility of using Vegas in both season-long and daily fantasy leagues. I think looking at projected team totals is the most obvious action, but you can also examine player props and even line movements (most useful in daily fantasy to get a sense of where the crowd is headed).

One mistake I've noticed being made by most people who use the lines is examining the projected total for an entire game as opposed to for just a single team. While I think there's something to be said for targeting players who are supposed to be in shootouts, it's not inherently valuable to examine the total over/under because it doesn't tell you anything about each individual team.

I've showed how you can calculate the individual team totals using the over/under and the spread and it's semi-complicated, so I won't rehash that here. However, I wanted to look at the relationship between quarterback fantasy points and the projected game total. The numbers shown are since 2012.

The strength of the correlation is .10, which is nearly nothing. Again, looking at the projected game total isn't very useful. I've shown in the past that examining the individual team totals is useful, but I actually think it's way more effective when projecting running backs than quarterbacks. Below, I charted average rushing yards versus the projected team total.

This is pretty obvious, and I think it helps prove the idea that rushing stats are more representative of game outcomes than passing stats. Notice that I didn't say that rushing helps teams wins, but just that we so frequently see winning teams with the best rushing numbers because they keep it on the ground late in games. Compare that to teams that are down and forced to rack up passing attempts late, making up for what likely got them down early in reduced passing efficiency. Basically, passing stats "even out" in most game scripts, whereas rushing stats don't. For that reason, I personally find more uses for the Vegas lines when it comes to projecting backs.

## RB Receiving Yards

One of the most important traits for running backs is the ability to catch passes. It increases their ability to score points, obviously, but it also gives them a high floor from week to week because they aren't so reliant on particular game scripts; basically, pass-catching running backs are robust, whereas one-dimensional backs are fragile.

Below, I charted the average total passing yards and running back receiving yards for every team since 2009, along with the percentage of the total passing yards accounted for by the backs.

Notice that some of the teams that have a high percentage of receiving yards go to the running back are also generally just poor passing teams—the Ravens, Bills, Raiders, etc, but you also have teams like the Bears, Lions, and Saints in there.

At the other end of the spectrum you have the Cardinals, Bengals, Colts, Giants, Steelers, and Bucs who have rarely thrown to running backs. I looked at the average correlation between teams' running back receiving yards from one year to the next, and it's 0.42, which is moderately strong.

Here's a look at team passing yards versus running back receiving yards for each team.

I think what's interesting here is something that would seem obvious—that the teams that have the most running back receiving yards are the ones with the backs who are the best receivers. That is, you can transform an offense with a quality receiving running back, but you can't turn a running back who can't catch passes into one who does because of the system.

That doesn't bode well for Alfred Morris, for example; the Redskins have said they're going to get him much more involved in the passing game, but he's not a natural pass-catcher. It seems more like running backs who are considered good pass-catchers remain that way no matter where they go, as opposed to them being labeled that way because of a particular offense that just gets them the ball.

## WR1, WR2, and TE Scoring

I've written a lot about the pros and cons of pairing teammates on your fantasy team. When it comes to pairing a quarterback with one or more of his wide receivers, I think it's generally a good idea in season-long leagues, assuming the value is there, because you give yourself upside if you need it.

I'm particularly bullish on pairing a backup quarterback with one of your top receivers; if your starting quarterback gets injured, you'll need a lot of upside, which that pairing will give you.

But what about pairing multiple receivers from the same team? My first thought is that this would increase risk without necessarily improving upside that much, since it's unlikely that you're going to get two huge performances from teammates. The exception would be on a team like the Denver Broncos, where there's plenty of receiving yards and scores to go around.

Before tackling that question, though, we need to look at how performances are normally distributed among different types of receivers. I charted the percentage of games with less than 10 points, 10-14 points, 15-19 points, and 20+ points in standard scoring leagues for WR1s, WR2s, and TEs. Here's a look at No. 1 wide receivers.

In a standard scoring league, 20 points is a top-tier performance—140 yards and a touchdown or 80 yards and two scores, for example. Since 2011, No. 1 wide receivers have scored at least 20 fantasy points in eight percent of their games—between one and two per year.

Meanwhile, 15+ point performances are much more common, occurring around once every four games for No. 1 NFL receivers. The most likely outcome in any given game, however, is that the average No. 1 will score fewer than 15 points, which they do just under three-fourths of the time.

Here's a look at the breakdown for No. 2 wide receivers.

As expected, there's less upside with a big jump in poor games (fewer than 10 points). The average No. 2 receiver fails to score 10 points in standard fantasy leagues in just under half of his games.

And now tight ends...

There's a lot less upside than both WR1s and WR2s, with the average tight end scoring 15 points in just 15 percent of his games. The normal tight end will fail to eclipse 10 points in about three of five contests.

Knowing these percentages, we can calculate how often we'd expect the different receiver types to have quality games together from chance alone. That is, if the receivers' performances were randomly distributed, how often would they score X points in the same game?

Looking at the 15-point mark, we see that WR2s are far less likely than tight ends to score 15 fantasy points when the WR1 does the same.

These results are pretty extreme, and I don't really know why. Why is it that when a WR1 had a good game, the tight end on the team is much more likely than the No. 2 wide receiver to do the same? Is it something with coverage? Is it the way offenses with dominant No. 1 receivers are structured? I have no idea, but there's something here.

In either season-long or daily leagues, the evidence seems to suggest that it's smarter to pair a tight end with your wide receiver than to use two wide receivers on the same team. Personally, I don't know if I'll use the data because I can't really draw any logical conclusions from it. This could be an aberration, I presume, so the fact that it doesn't necessarily make sense to me makes me less likely to act upon it. It's not that I don't think it could be useful, but just that I'm not really sure how I'd implement this data right now without knowing the cause.

## RB Size and Big Plays

One of the biggest hurdles for any statistician (or any total hack who messes around with stats like me) is trying to identify causal relationships in data. The majority of the time, we're going to see relationships that indicate a correlation between two variables, but not causation.

We see this all the time with the relationship between rushing attempts and team wins. To this day, almost every NFL team runs too much because everyone knows the "If you run X times, you win Y games" stats that FOX and CBS show us every Sunday.

Mike Lombardi—someone who has held numerous high-level positions within the league—has stated that his offenses employ "The Rule of 53," attempting to reach 53 total completions and rushing attempts in a game because "teams usually win when they reach that number."

To show how absurd that idea is (outside of the fact that 53 is just a total arbitrary cutoff point), consider my "Rule of 3." NFL teams that have at least three kneel-downs in the second half of football games win the game every fucking time. If you're an NFL coach and want to win, all you need to do is kneel down three times and then, WHAMMY!, you win the game. It's flawless.

So clearly there are times when the numbers are going to point certain ways when there isn't a causal relationship. It's not that the numbers are "wrong," but just that we need to be careful when interpreting data.

A related topic that has interested me in the past is the relationship between both weight and speed on running back efficiency. It seems logical to assume that both speed and weight would help a running back. All other things equal, we want bigger, faster running backs.

The problem is that those two variables are generally inversely correlated; typically, the heavier a running back, the slower he'll run. So what happens is that when we test for the correlation between weight and running back success, we see a negative one (i.e. as weight increases, running back production decreases).

That doesn't mean that weight is bad, though; it just means that speed is so important for running backs (way more important than most people realize, actually) that sometimes extra weight hurts, even though being bigger and stronger isn't a bad thing in a vacuum. So given the choice between a 210-pound back and a 220-pound back, we should choose the heavier one if all else is equal, namely speed.

One thing I wanted to do is simply combine the two variables to see what sort of effect there is between weight and long runs. So here you go, starting with the average weight of the top 10 running backs in runs of 15-plus yards over the past five seasons (50 total running backs).

The dotted line is the average weight for running backs who started 10 or more games. Unsurprisingly, the running backs with the most long runs have weighed pretty significantly less than the league average in all five seasons studied.

It's the same relationship with breakaway percentage, which simply reflects the percentage of total rushing yards that came from runs of 15 or more yards.

Weight is one way to analyze size, but I think body mass index (BMI) is a better one. BMI is simply a measure of how much mass someone has (it takes into consideration only weight and height). I've done some past research on the importance of BMI for running backs.

Basically, running backs with a high BMI have proven to be more 1) effective and 2) durable. I think the latter trait is a really important one that goes overlooked by NFL teams; short, stocky running backs have gotten injured far less frequently than tall, lean running backs.

Here's a look at average BMI of the top 10 running backs in 15-plus yard runs over each the past five years.

The numbers still check in below the league-average BMI in every season, but they're closer than with weight. The same goes for breakaway percentage.

When you consider that weight is indeed an important trait for running backs, these numbers suggest two things:

1) Speed is really, really important, especially for the acquisition of big plays.

2) BMI is a better predictor of success than weight.

## Possession vs. Big-Play Wide Receivers

I've long been interested in the difference between big and small receivers. A similar (but not exactly comparable) topic is the difference between "possession" and "big-play" receivers. There are probably 1,000 different ways we could define those traits, but I decided to just look at yards per catch—the top five and bottom five in each of the past five seasons (among top 40 receivers in fantasy).

Then, I broke down the two buckets according to the opponent (pass defense rank). My thinking was that possession receivers might be a little more consistent from week to week, with their production coming more independently of the matchup, because they generally see targets that are less volatile than big-play receivers.

The first thing to note is that big-play receivers have simply produced more than possession receivers. I wouldn't read too much into that because there's obviously a selection bias here; big-play receivers by definition posted greater efficiency, so all we're seeing is that efficient wide receivers are productive ones. Yeah, got it.

The other important thing here is that both possession wide receivers and big-play wide receivers are better against weaker defenses, specifically defenses ranked in the bottom 10 in pass defense. Here's how the numbers break down in terms of percentages.

Overall, possession wide receivers are right around 16 percent better against bottom 10 pass defenses as compared to top 10 pass defenses, while big-play receivers around 20 percent better. That's a small difference, and it would get even smaller if we accounted for the fact that big-play wide receivers post better stats (again, by definition), so we'd naturally expect more variance in their results anyway.

Even though I still think there's merit in fading receivers who _rely_ specifically on big plays for fantasy production, there doesn't seem to be any merit in using yards per catch as a gauge of week-to-week volatility.

## Probability of Top-Five Finish

Whether you play season-long or daily fantasy football, you've inevitably been in a position in which you need to determine if a player who has started the year hot (or cold) is going to continue trending in that direction. Whether you're "buying low" in a season-long trade or targeting an underperforming player on DraftKings, you're trying to figure out how predictive early-season results are of the rest of the year.

Well, here are some numbers to help you out. I charted the probability of a player who is ranked in the top five at his position at a certain point in the year maintaining that ranking by the end of the season.

The first thing that sticks out is that, after Week 4 and Week 8, the top players at quarterback, running back, and tight end have a much better chance to finish among the best than wide receivers. I think part of that effect is simply because there are more fantasy-relevant players at the wide receiver position, and thus more competition. A top-five wide receiver has a larger number of people who are competing to potentially surpass him in fantasy points.

You could potentially say the same about running backs (as compared to quarterbacks and tight ends), but I'm not sure that's the case. Yes, more running backs touch the ball, but RB2s don't really have a shot at finishing in the top five in fantasy. I think the reason that we see running backs check in just slightly below quarterbacks and tight ends is injury rates; running back is pretty predictable if we can project workload, but the fact that running backs get injured more than any other position throws a wrench in our ability to predict that.

Quarterbacks and tight ends have been the most predictable based on early-season production. I don't think either result is surprising; quarterbacks have proven again and again to be really consistent from season to season and game to game, while there are few enough truly elite wide-receiver-esque tight ends that there's simply not a ton of competition at the position.

Whatever the reasoning at each position, I definitely think the data is actionable early in the year. When a quarterback starts the season hot, there's good reason to think he has a higher probability of finishing among the top fantasy performers than a running back.

If you're looking to sell high or trade away an overachieving player, I'd be most inclined to do it at the wide receiver position. Yes, the data is slightly skewed, but the results are dramatic and wide receivers are far more volatile from week to week than the other skill positions, so there's a higher chance of someone who doesn't "belong" in the top five finding themselves there after Week 4.

## Postface

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