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Regression Alert: Week 14 - Footballguys

Telling the dirty truth about Regression Alert and regression to the mean.

Welcome to Regression Alert, your weekly guide to using regression to predict the future with uncanny accuracy.

For those who are new to the feature, here's the deal: every week, I dive into the topic of regression to the mean. Sometimes I'll explain what it really is, why you hear so much about it, and how you can harness its power for yourself. Sometimes I'll give some practical examples of regression at work.

In weeks where I'm giving practical examples, I will select a metric to focus on. I'll rank all players in the league according to that metric, and separate the top players into Group A and the bottom players into Group B. I will verify that the players in Group A have outscored the players in Group B to that point in the season. And then I will predict that, by the magic of regression, Group B will outscore Group A going forward.

Crucially, I don't get to pick my samples, (other than choosing which metric to focus on). If the metric I'm focusing on is yards per target, and Antonio Brown is one of the high outliers in yards per target, then Antonio Brown goes into Group A and may the fantasy gods show mercy on my predictions.

Most importantly, because predictions mean nothing without accountability, I track the results of my predictions over the course of the season and highlight when they prove correct and also when they prove incorrect. Here's a list of all my predictions from last year and how they fared.


THE SCORECARD

In Week 2, I laid out our guiding principles for Regression Alert. No specific prediction was made.

In Week 3, I discussed why yards per carry is the least useful statistic and predicted that the rushers with the lowest yard-per-carry average to that point would outrush the rushers with the highest yard-per-carry average going forward.

In Week 4, I explained why touchdowns follow yards, (but yards don't follow back), and predicted that the players with the fewest touchdowns per yard gained would outscore the players with the most touchdowns per yard gained going forward.

In Week 5, I talked about how preseason expectations still held as much predictive power as performance through four weeks. No specific prediction was made.

In Week 6, I looked at how much yards per target is influenced by a receiver's role, how some receivers' per-target averages deviated from what we'd expect according to their role, and predicted that the receivers with the fewest yards per target would gain more receiving yards than the receivers with the most yards per target going forward.

In Week 7, I demonstrated how randomness could reign over smaller samples, but regression dominates over larger ones. No specific prediction was made.

In Week 8, I discussed how even something like average career length could be largely determined by regression-prone fluctuations in incoming talent. No specific prediction was made.

In Week 9, I looked at running backs scoring touchdowns at an unsustainable rate and posited that even Todd Gurley must return to earth.

In Week 10, I delved into the purpose of regression alert and the proper takeaways. No specific prediction was made.

In Week 11, I explained an easy way to find statistics that were more prone to regression and picked on yards per carry one more time.

In Week 12, I went into the difference between regression to the mean, (the idea that production will probably improve or decline going forward), and the gambler's fallacy, (the idea that production is "due" to improve or decline going forward). No specific prediction was made.

In Week 13, I badmouthed interception rate for a bit and then predicted that the most interception-prone quarterbacks to that point would throw fewer picks than the least interception-prone quarterbacks going forward.

Statistic For Regression
Performance Before Prediction
Performance Since Prediction
Weeks Remaining
Yards per Carry
Group A had 24% more rushing yards per game
Group B has 4% more rushing yards per game
SUCCESS!
Yards:Touchdown Ratio
Group A had 28% more fantasy points per game
Group B has 23% more fantasy points per game
SUCCESS!
Yards per Target
Group A had 16% more receiving yards per game
Group A has 13% more receiving yards per game
Failure
Yards:Touchdown Ratio
Group A had 26% more fantasy points per game
Group B has 4% more fantasy points per game
SUCCESS!
Yards per Carry
Group A had 9% more rushing yards per game
Group B has 24% more rushing yards per game
1
Total Interceptions
Group A had 83% as many total interceptions
Group B has 42% as many total interceptions
3

Another week, another lay-up for yards per carry. Group B continues to receive more carries than Group A, (as expected), but they've also increased their lead in yard per carry average. Over the last three weeks, Group B backs average 5.18 yards per carry vs. just 5.03 for Group A.

On the interception prediction, this week presented a little bit of a dilemma. Mitchell Trubisky, a quarterback in Group B, was unable to play on Sunday. Normally this isn't a problem because normally my predictions are based on per-game averages, so a missed game neither helps nor hurts a group in expectation. But I set up the interceptions prediction based on total interceptions; in theory, if every Group B quarterback was placed on injured reserve tomorrow, they would throw zero interceptions and win by default.

Were I cleverer and blessed with a tiny bit of foresight, I would have made my predictions based on entire teams instead of individual quarterbacks. Since that's really a much better solution, and because I don't want anyone to think I won on a technicality, that's what I'll be doing going forward. As a result, Group B gets penalized for Chase Daniels' two interceptions in relief of Trubisky.

Other than that snag, everything came up roses for Group B last week. The true randomness of interception rates was best exemplified by Cam Newton against the Tampa Bay Buccaneers. Newton threw interceptions at a rate notably below the league average. The Bucs had just one interception in their first ten games before picking Nick Mullens off twice in Week 12. So what happened when the teams met in week 13? Why, Tampa picked off Newton four times and more than doubled their season-long interception total, naturally!

If interception rate was a stable predictor, such a turn would be virtually unthinkable. Instead, interceptions are so noisy that you could remove Newton's 4-interception game from the sample and Group A still would have thrown more interceptions per game than Group B. As it stands, Group A has a seven interception lead with three games to go.

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