Podcast: Ben Alamar on the ESPN Football Power Index and NFL Divisional Playoffs

On this week’s episode of the Football Analytics Show, I’m honored to have Dr. Ben Alamar, Director of Sports Analytics at ESPN. We dig into a host of topics, which includes:

  • The NBA executive that opened the conversation with “I don’t believe this analytics stuff”
  • How FPI (Football Power Index) makes adjustments for quarterback injuries
  • The surprising team to which FPI assigns the second highest Super Bowl win probability
  • The hidden factor that gives Dallas an extra point in the FPI prediction against Green Bay
  • Spread predictions for all 4 Divisional Playoff games for FPI (not available anywhere on the internet to my knowledge) and my member numbers
  • Ben’s 3 tips for breaking into sports analytics

You can check out the work of ESPN analytics team by clicking here.

To listen to the show on iTunes, click here.

To listen here, click on the play button:

How Andy Reid wins football games with interceptions

Andy Reid’s teams throw a low rate of interceptions. The visual shows how his teams in Philadelphia and Kansas City have had a lower than NFL average interception rate (interceptions divided by attempts) in all but 3 of 18 seasons.

Reid’s Eagles had a particularly good stretch of pick suppression from 2000 through 2004. Led by quarterback Donovan McNabb, Philadelphia never won fewer than 11 games in any of those 5 seasons.

Despite a decreasing interception rate across the NFL, Reid has continued to beat the NFL average over the past four years in Kansas City. Led by quarterback Alex Smith, the Chiefs have won 43 regular season games and never dipped below 9 wins in any one season.

The randomness of turnovers

The visual goes against the typical quant narrative that turnovers are random.

For example, I’ve shown this visual that shows the relation between interception rate the first 6 games of the college football season versus the remainder of the season.

The lack of correlation between these quantities shows that you can’t predict a team’s interception rate later in the season based on the same quantity during first 6 games.

This suggests interceptions are random, and a team has a 50% chance to have a better or worse than average interception rate. However, if you assume this for Andy Reid’s teams, there’s only a 0.37% chance his teams would have had 3 or fewer seasons with a below average interception rate.

Randomness certainly plays a role in interceptions. No one who has ever seen a tipped pass fall into the hands of a defender should doubt that.

However, the Reid visual suggests that some coaches can suppress interceptions over a very large sample of games.

Steelers at Chiefs

This has implications in predicting the outcome of the Steelers at the Chiefs playoff game this weekend.

Kansas City doesn’t seem like much of a Super Bowl threat with the 16th and 11th ranked pass offense and defense, respectively, by my adjusted yards per attempt. I use these pass efficiency numbers to evaluate teams for two reasons:

  • My research shows the importance of passing over rushing in the NFL.
  • Turnovers have little impact on yards per pass attempt.

However, if Kansas City is truly skilled at not throwing interceptions, then these pass efficiency numbers will underestimate their team strength.

Team rankings based on adjusted margin of victory might be a better way to evaluate Kansas City. Their low interception rate will impact margin of victory, as I’ve found that an interception is worth 5 points in the NFL.

My member numbers combine both pass efficiency and margin of victory to make Kansas City a 1.3 point favorite against Pittsburgh. However, my team rankings based on only points would make Kansas City a 3 point favorite.

I interviewed Ben Alamar on the Football Analytics Show this week, and his FPI (Football Power Index) makes the Chiefs nearly a 5 point favorite. They use an expected points added, a metric which accounts for interceptions but their own twist. To listen to that part of the discussion, go to 14:50 of my interview with Ben Alamar.

Podcast: Cade Massey on the NFL Playoffs and College Football Championship Game

On this week’s episode of the Football Analytics Show, I’m joined by Cade Massey, professor at the Wharton School at the University of Pennsylvania. He studies judgment under uncertainty, and there’s no better example than his Massey-Peabody football predictions.

We cover a wide range of topics, which includes:

  • How Cade has learned humility in building a predictive football model
  • The playoff karma of the New York Giants
  • What data says about whether match ups matter in football predictions
  • The sneaky trick for breaking into the sports analytics world
  • The Massey-Peabody prediction for Alabama versus Clemson

For match ups, I discuss a similar study in college basketball.

To listen on iTunes, click here.

To listen here, click on the play button.

Super Bowl win probabilities for 2016-17

It’s not surprising that New England has the highest Super Bowl win probability.

No Rob Gronkowski, no problem. The offense has been fine so far without the elite TE, and Belichick machine marches on.

However, it might be a surprise that Atlanta has the second highest Super Bowl probability over Dallas.

Matt Ryan and the Atlanta has the top ranked pass offense by my adjusted yards per attempt. The pass defense has been respectable at 8th.

Dallas has had a fantastic season, but Dak Prescott is still a rookie quarterback. Will he hold up now that defensive coordinators have a season’s worth of tape to study?

Still, the Cowboys have a 16.8% chance to win the Super Bowl, not far behind the Falcons at 19.1%.

Get a sample of my best NFL predictions

At The Power Rank, I combine predictions based on a number of different data sources to make the best possible football predictions.

It started with team rankings that take the margin of victory and adjust for strength of schedule. Back in 2008, I developed an algorithm that makes these adjustments, and you can see these points based predictions here.

The ensemble of predictions now contains calculations based on other data sources. For example, I use yards per play, a powerful efficiency metric, to evaluate teams.

I save these predictions for members of The Power Rank, as the NFL predictions went 53.1% against the closing spread during the regular season. You can get a sample of the NFL predictions by signing up for the free email newsletter.

To sign up for this free service, enter your best email address and click on “Sign up now!”








Methods for Super Bowl win probabilities

These win probabilities start with my member predictions that combine data from a number of different sources.

The predictions imply a win probability for each team in each game, and these numbers provide the parameters to simulate the playoffs.

Each simulation accounts for the shifting match ups based on seed (e.g. New England will play the lowest seed after this Wild Card Weekend) and neutral site of the Super Bowl.

Podcast: Interim college football coaches, the Oakland Raiders and match ups in college football

This week’s episode of The Football Analytics Show dives into the first week of bowl season and a stand out game from week 15 of the NFL. I discuss the following:

  • Do interim coaches matter in predicting bowl games?
  • The NFL game that made me do a double take
  • The source that I double check my NFL prediction with
  • The match up that might allow Central Michigan to beat Tulsa

To listen on iTunes, click here.

To listen to the podcast, click on the play button.