## 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: