- Home All posts
Sunday's Numbers Have Been Crunched
What Are You Doing, Chip?
Cards on the table, I'm a huge Eagles fan. As an NFL stats nerd, I could not have been more excited for Chip Kelly to make the transition to the big leagues. While I did not expect him to immediately institute his Oregon trademarks, I did expect to see him going for it more often on fourth down, especially in situations where the numbers called for it -- and generally, making decisions to maximize the Eagles win probability.
It's four weeks into the season, and too many times I've asked my TV, "What are you doing, Chip?" Today against the Broncos, there were a couple of questionable decisions. Down 14-3, the Eagles were moving the ball very well to start the game. Vick and company strung together a 15-play drive that ended up with a 4th-and-4 from the Broncos 7-yard line. Using our Markov model, we can look at the progression of the drive:
Position Leaders Visualization
I made a Tableau viz to illustrate last season's ANS Player of the Year Awards. The idea was to see just how far from the pack the very top performers stood at each position. For fun I also included previous seasons. Now I've finally gotten around to setting up an automated update process so that we can see the position leaders as the season develops. The viz is permanently available under the Graphs | Visualizations | Position Leaders menu link. Like the other visualizations, it will
It's no surprise where Peyton Manning ranks, but check out LeSean McCoy on the RB page. One thing you can use this plot for is to see which teams, players, and squads are likely to regress. for example. If you look at the QB chart, you can see that Cutler, Tannehill, and Locker have WPA numbers that far exceed what we'd expect given their EPA numbers. Recall that they each had game-stealing TD passes so far this season, inflating their WPA. It's likely that they'll return to earth over the course of the season and we'll see their respective logos drift down toward the trend line.
Likewise, we would expect guys who can produce yards and points but not a lot of WPA to drift upward on the WPA axis. This would include Newton, Romo, Eli Manning, and Ponder (if he recovers from his rib injury and gets his job back). I guarantee it!*
*Guarantee not an actual guarantee.
Should You Bench Your Fumbling Running Back?
Sam Waters is the Managing Editor of the Harvard Sports Analysis Collective. He is a senior economics major with a minor in psychology. Sam has spent the past eight months as an analytics intern for an NFL team. When he is not busy sounding cryptic, he is daydreaming about how awesome geospatial NFL data would be. He used to be a Jets fan, but everyone has their limits.
ANS Partnership with Harvard Sports Analysis Collective
One of the new features this season will be a partnership with the Harvard Sports Analysis Collective. I've teased these guys in the past about their vaguely Maoist name, but I've always been a fan of their straightforward analytic style. Some of the brightest minds in analytics are coming from places like HSAC.
In case you're not familiar with them, HSAC is an undergraduate organization at Harvard College dedicated to the quantitative analysis of sports strategy and management. It was founded in 2006 under the guidance of Professor Carl Morris. HSAC's work has focused on applying some scientific rigor to sports analysis. Their previous work on the NFL has ranged from identifying inefficiencies in the NFL draft to criticizing poor in-game decision making, which you can find on their new website. Many of its members have worked in the sports analytics industry in both the media and on the team side. According to Kevin Meers, co-president of the group, says that right now they're focused on getting our bench press reps up so they can get off the computers and onto the field. Good luck with that.
Kevin will be coordinating weekly contributions from HSAC members here at ANS. He's majoring in economics with a minor in statistics. His work has focused on the NFL draft, but he is currently studying applications of game theory on in-game decisions. He has also spent the past two years as an intern in the NFL, and he may have been the only person from Washington DC who wasn't excited about the RG III trade.
Please welcome Kevin and HSAC, comrades! Look for their first post shortly.
Weekly Game Probabilities Are Back
Game probabilities for week 4 are up at the New York Times. With the demise the Fifth Down Blog, the probabilities are now on the NYT website proper. This week I explain why the randomness of turnovers makes the Giants-Chiefs game a closer match-up than their win-loss records suggest.
In retrospect, turnovers explain a great deal of a team’s fortunes. But prospectively, team turnover statistics don’t predict game outcomes as much as you might think. The reason behind this distinction is something called auto-correlation. Put simply, turnovers are very random. Only a small portion of a team’s past turnover rate carries forward to be predictive.
"Thursdays are 6.3 percent less exciting."
Friend of ANS Aaron Gordon used the Excitement Index (EI) and Combeback Factor (CBF) to find out if the Thursday night games really are more boring than most games. From Aaron's article at Sports on Earth:
What NFL Network games have sorely missed are big comebacks. NFL Network games average a Comeback Factor 3.32 -- half the league average -- and only five games with a CBF of 5 or above (where the winning team had a win probability below 20 percent). By definition of the win probability model, 20 percent of the games played should feature a comeback with a CBF of 5 or above (which the larger data set confirms). For NFL Network games, its only 13 percent. For comparison, Monday night games --which often feature hand-picked matchups -- have an average CBF of 8.15, but are right about where they should be in terms of CBF games of 5 or above: 23 percent.
Podcast Episode 2: Keith Goldner
Keith Goldner, chief analyst at Numberfire and a regular contributor to Advanced NFL Stats, joins Dave to discuss his recent research. Keith begins by outlining his Markov model for football, explaining how to use historical data to calculate the probability of an offensive drive ending in a particular "end state". The two then discuss the concept of "net expected points", and compare and contrast its value as a tool in combination with other efficiency stats. The show ends with a detailed analysis of two fourth down decisions from last week's games, highlighting the frustrating difference between good process and good results.
For more of Keith's work, and to check out some of the visualizations and data sets discussed in the episode, visit his site: Drive By Football
Make sure to never miss an episode of the Advanced NFL Stats Podcast by subscribing on itunes.


