2013 Fantasy Football: How to Rule Your Draft
At this time last year, Robert posted a link to the “Dollarnaire Fantasy Football League,” challenging anyone to come take on the “greatest fantasy football player of all time.” I accepted that challenge, and I even went as far to post this on BSO:
Little did Robert know that the true world’s premiere fantasy football owner—me—would be taking his challenge. I write a fantasy football column for the Times. I’m the author of the top-selling fantasy football book in the world. I personally guarantee a victory in the Dollarnaire league this year. There’s literally zero chance I could lose to these fools. I could rank my players and auto-draft and still win handily. I could get absolutely hammered before the draft and pick only white players and still win. Frankly, all I’m thinking about is if I’ll go undefeated.
Guys, Robert made the playoffs. I didn’t. I was the first team out, and I’m embarrassed. So why in the world should you buy fantasy football content from the owner of a (powerhouse) 7-6 team? Because fantasy football is filled with variance—positive results are only sometimes a reflection of a quality play—and your job is simply to tilt the odds in your favor. And if you can capitalize on prior randomness—the subject of this post—you’ll increase your expected win probability.
From Chapter 6 of my book Fantasy Football for Smart People: How to Dominate Your Draft:
6 Identifying Value: Regression, Randomness, and Running Backs
Back in 2008, I had running back Thomas Jones ranked well ahead of most owners. Jones was playing for the Jets and coming off a season in which he ran for 1,119 yards, but averaged just 3.6 yards-per-rush and scored only two total touchdowns. Those two scores represented just 0.59 percent of Jones’ 338 touches in 2007.
ESPN had Jones ranked 21st among all running backs. I had him 10th. Why would I possibly rank a then 30-year old running back coming off a season in which he tallied 3.6 yards-per-carry and two total touchdowns in my top 10? Regression toward the mean.
Regression toward the mean is a phenomenon wherein “extreme” results tend to end up closer to the average on subsequent measurements. That is, a running back who garners 338 touches and scores only twice is far more likely to improve upon that performance than one who scored 25 touchdowns.
0-16 Detroit Lions: A Coach’s Dream?
Regression toward the mean is the reason the NFL coaches who take over the worst teams are in a far superior position to those who take over quality squads. If I were an NFL coach, there is no team I would prefer to take over more than the 2008 Detroit Lions. Coming off an 0-16 season, the Lions were almost assured improvement in 2009 simply because everything went wrong the previous season. Even though Detroit was a bad team, any coach who took over in 2009 was basically guaranteed to oversee improvement in following years.
The same sort of logic is the reason that there are so many first-round “busts” in fantasy football. Players almost always get selected in the first round because they had monster years in the prior season. In effect, most first-rounders are the “outliers” from the prior season’s data, and their play is more likely to regress than improve in the current year. It isn’t that those players are poor picks, but rather that the combination of quality play, health, and other random factors that led to their prior success is unlikely to work out so fortunately again.
Players Aren’t “Due”
Walk into any casino in America and you will see lines of hopeful grandmothers lining up behind slot machines that haven’t paid recently. Since the machines pay a specific average of money over the course of their lives and those numbers always even out over the long run, surely an underperforming slot machine must be due to pay out soon, right?
This is one of the biggest misconceptions regarding statistics and regression, and it is the cause of millions of lost dollars each year. In a set of random data, previous occurrences have absolutely no effect on future events. If you flip a coin right now and it lands on heads, the probability that it lands on heads again on your next flip is still 50 percent.
Similarly, if the overall payout rate of a slot machine is 40 percent, the most likely outcome of placing $1,000 into it is walking away with $400. You could walk away big or you (theoretically) could lose every penny, but the most probable single dollar amount you could “win” is $400. So when the previous 100 pulls of the lever are fruitless, the payout “improvement” that is likely to take place over the next 100 pulls isn’t because the machine is “due,” but rather it is simply working as normal. That is regression toward the mean.
But football isn’t random.
Football isn’t totally random, but it’s more random than you think. Actually, some statisticians have estimated the “luck factor” to be as high as .924 in the NFL. That means on any given week, the “true” winning percentage of teams that win is really around .538. In a league in which only 16 games make up a season, the talent gap between teams is lessening, and turnovers play a huge role in wins, the amount of luck involved in the game is more so than any other professional sport.
Even disregarding the potential randomness of NFL outcomes, the identification of underperforming players can be of incredible value to fantasy owners. As it relates to Thomas Jones, it doesn’t really matter how much randomness was involved in his two-touchdown season. Heading into the 2008 season as the workhorse back on a team with a strong offensive line and no real reason to think he was a fundamentally poor short-yardage runner, projecting Jones to score more than a handful of times was easy. I projected him at 10 touchdowns. He scored 15.
So when other owners are jumping all over the players who had “extreme” seasons the prior year, look for talented players who actually underperformed. As long as they get similar opportunities to make plays, their numbers will probably improve. For fantasy owners, that represents value.
Of course that doesn’t mean you should select weaker players simply because they had poor years. In the first few rounds, you are almost certain to draft outliers who played better than normal the season before. Your job is to recognize which players’ value is primarily the result of random factors, and thus likely to regress to the average, and which is based largely on talent, and thus likely to repeat itself.
Want to learn how to use this information to project players and draft the perfect lineup? Buy the books, get the draft package, and dominate your fantasy football league in 2013. Or field a team that’s barely better than .500, whatever.