When Coaches Use Timeouts

As I continue to work on the next generation WP model, I'm looking hard at how timeouts are used. Here are 2 charts that capture about as much information as can be squeezed into a graphic.

The charts need some explanation. They plot how many timeouts a team has left during the second half based on time and score. Each facet represents a score difference. For example the top left plot is for when the team with the ball is down by 21 points.  Each facet's horizontal axis represents game minutes remaining, from 30 to 0. The vertical axis is the average number of timeouts left. So as the half expires, teams obviously have fewer timeouts remaining.

The first chart shows the defense's number of timeouts left throughout the second half based on the offense's current lead. I realize that's a little confusing, but I always think of game state from the perspective of the offense. For example, the green facet titled "-7" is for a defense that's leading by 7. You can notice that defenses ahead naturally use fewer timeouts than those that trail, as indicated by comparison to the "7" facet in blue. (Click to enlarge.)

What I'm Working On

It's been almost 6 years since I introduced the win probability model. It's been useful, to say the least. But it's also been a prisoner of the decisions I made back in 2008, long before I realized just how much it could help analyze the game. Imagine a building that serves its purpose adequately, but came to be as the result of many unplanned additions and modifications. That's essentially the current WP model, an ungainly algorithm with layers upon layers of features added on top of the original fit. It works, but it's more complicated than it needs to be, which makes upkeep a big problem.

Despite last season's improvements, it's long past time for an overhaul. Adding the new overtime rules, team strength adjustments, and coin flip considerations were big steps forward, but ultimately they were just more additions to the house.

The problem is that I'm invested in an architecture that wasn't planned to be used as a decision analysis tool. It must have been in 2007 when I recall some tv announcer say that Brian Billick was 500-1 (or whatever) when the Ravens had a lead of 14 points or more. I immediately thought, isn't that due more to Chris McAllister than Brian Billick? And, by the way, what is the chance a team will win given a certain lead and time remaining? When can I relax when my home team is up by 10 points? 13 points? 17 points?

That was the only purpose behind the original model. It didn't need a lot of precision or features. But soon I realized that if it were improved sufficiently, it could be much more. So I added field position. And then I added better statistical smoothing. And then I added down and distance. Then I added more and more features, but they were always modifications and overlays to the underlying model, all the while being tied to decisions I made years ago when I just wanted to satisfy my curiosity.

So I'm creating an all new model. Here's what it will include:

Sloan Sports Analytics Conference

I was just added to the football analytics panel as a last-minute fill-in. The panel is at 10:40 AM Saturday. I'll be there from Friday afternoon through Saturday afternoon and I'm looking forward to reconnect with all the great folks I met last year and to make new connections.

I'll make myself available for interviews or sit-down discussions by request. Please send an email to brian@mail.advancednflstats.com to coordinate. See you there!

What Is Football Analytics?

Analytics in football is one of the hot topics in recent years. What began as a hobby for guys like me has turned into a serious and growing field. It's been gaining attention in the media, and now teams themselves have begun to pay attention, many of which have hired analysts to help provide insight to decision makers.

"Analytics" has unfortunately become a trendy buzzword in sports. I've found that many people who are only vaguely familiar with analytics, including some team executives, media members, and fans, have the wrong idea about what analytics is. Some think it's a panacea that can optimize the solution to any problem. Some think it's just statistical trivia or scientific minutia, like ESPN's Sports Science series (Dwight Howard's arm-span is as big as a 2-car garage!). Others think it's just Moneyball, a one-time talent arbitrage applicable to only one sport. So I thought I'd put my own thoughts down on what analytics is, at least as it applies to football.

I'm not the gatekeeper on what qualifies as analytics, and I'm not going to say what counts and what doesn't. But I think analytics comprises all or parts of four general processes:

Podcast Episode 20 - Jeff Ohlmann

Jeffrey Ohlmann, Associate Professor at the University of Iowa's Tippie College of Business, joins Dave this week to talk about the NFL draft, the sports analytics class he teaches, and his upcoming panel discussion at the MIT Sloan Sports Analytics conference. Professor Ohlmann discusses his research on optimal strategies for the NFL draft and explains how teams must strategize with imperfect player data and knowledge of the draft strategies of other teams. He also discusses how NFL franchises implement different strategies in the draft, and how combinations of NFL combine measurements may be more useful than any one metric.

Professor Ohlmann also describes how he created a sports analytics class for undergraduates, and provides some examples of the coursework. He and Dave end the episode with a look towards the upcoming MIT Sloan Sports Analytics conference, how Professor Ohlmann prepared for his panel, and what he's most looking forward to in Boston.

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Podcast Episode 19 - Best of 2013

The “Best of 2013” episode contains highlights from this season’s most compelling Advanced NFL Stats Podcast interviews. On the show, Virgil Carter tells some terrific stories about playing quarterback for Bill Walsh, David Romer describes the feedback Bill Belichick gave him on his paper and Jeff Sagarin and Wayne Winston debate the best ways to analyze play by play data. Brian Burke concludes the show with thoughts on his four part series on momentum.


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2013 Seahawks Defense: In the Conversation for Best Ever?

The SEA defense dominated the league's best offense in years to take home the championship. Where should they stand in relation to some of the great defenses in recent times? Could they be the best defense ever?

One of the best things about Expected Points Added is that it separates the contributions of offenses, defenses, and special teams. A defense with a very good offense will appear better in terms of other metrics because their opponents would tend to get possession in poor field position. Conversely, a defense sharing a locker room with a below-average offense won't seem as dominant.

Another feature of EPA is that it's measured in net points. It's not just a klugey stat transformed into an analog of net points. It is net point potential. When EPA says a defense is worth 5.0 points per game, that's universally understandable and comparable.

One drawback, at least in its current general implementation, is that EPA doesn't account for the changing nature of the NFL. The league is a moving target, as offenses consistently gain an ever firmer upper hand over defenses. Even over the the last dozen years, offenses have gained several points of advantage. (How do we know exactly how much? EPA, that's how.) So defenses from a decade ago might appear better than today's defenses only because of how the league has evolved.

It's a trivial matter to account for the average EPA by year. That would allow us to compare apples to apples based on the "scoring environment" of the season. I'll do that below and see where SEA '13 fits in. But there's one other notion we should at least consider.

Super Bowl XVLXVLVLIIICDMVXXXIII Analysis

First of all, I'm getting tired of the Roman numerals. It was cool up until maybe Super Bowl XXV, but now it just hurts my brain.

Secondly, although my numbers pointed to a SEA edge I did not see that coming. The game notched a 1.5 on the Excitement Index, the lowest of any SB in the data (since '99). The next lowest were the TB-OAK 2002 game and the BAL-NYG 2000 game, each at 2.7. There weren't many decisions to analyze because the game got out of hand so quickly, but I'll go over the little we can learn from last night.

Overall, the game hinged on the fundamentals. SEA's defense was faster, bigger, stronger. Even a layman like myself could tell SEA won because of lots and lots of individual matchup victories. They made tackles at first contact. Guys shook their blocks lightning fast. They swarmed to the screens, caved the pocket, and covered the receivers in stride. There weren't many blitzes or scheming contrivances. Instead it was plain old physical football. The only wrinkle I noticed was that SEA played more cover/man 2 than we expected, but that's not exactly something Manning shouldn't normally be able to handle.

The Challenges