Franchise Season Plots Updated

Check out how your favorite team's fortunes have evolved over the past 14 seasons. The Franchise Season visualization has been updated with 2013's data. It's a fun snapshot of each team's "identity" from year to year, plotting Expected Points Added per Game.

You can examine how SEA has completed its circle of life with its 2013 campaign, covering all possible combinations of good&bad offense&defense. See how DEN's offensive production changed with the arrival of Peyton Manning (or check out IND's 2011 year in the wilderness).

The reigning champions are always the default selections for all our charts and tables at ANS, so this is will be the last week as the default for my hometown team. To me it's interesting to see that they've spent most of their franchise's existence along one axis, ranging from all-world defense/terrible offense to above-average offense/average defense, and won the Lombardi Trophy on both extremes of the axis.

It's rare for a team to sustain above-average production on both sides of the ball for beyond a season or two at a time, even for perennial contenders like NE and IND. PIT might be the best counter-example, with seasons that cluster in the upper-right quadrant consistently through the recent era. Even their "down" years were decent. For example, 2013 saw the Steelers finish 8-8, falling perfectly at the average/average intersection in the plot.

CHI's plot is a really interesting one. They should feature it on Sesame Street. One of these years is not like the other...

It's interesting to see how the average line can appear to be something like the sound barrier to some squads. ATL just can't seem to put together an above average defense, and BUF can barely break the average-barrier on offense.

A smaller version is included below, but the permanent full-size version is available via the Tools | Visualizations menu.

As always, up and to the right is good. Down and to the left is bad. My thanks to Chase Stuart who suggested the idea for this a couple years ago.

Podcast Episode 17 - Kevin Quealy

Kevin Quealy, graphics editor at the New York Times, comes on the show to discuss data visualizations. An expert in using data to tell visual stories, Kevin walks Dave through the process he and the graphics department use to tell news-worthy stories with data and graphics. 

Kevin describes some of his recent sports work, including a graph of longest QB start streaks, the 4th Down Bot and his NFL draft success chart. He talks about how he and his team begin their work process by looking for interesting stories and data-sets, and how those stories go through a variety of iterations before they are turned into visually striking charts. Kevin explains his criteria for what makes for a great visualization and provides some helpful tips for aspiring information designers.

If you're interested in learning more about data visualizations, check out the rest of Kevin's work here and his post about the process of creating the QB streak graphic. While you're at it, take a look at Jennifer Daniel's terrific 4th down Bot sketches.

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The Value of a Timeout - Part 2

In the first part of this article, I made a rough first approximation of the value of a timeout. Using a selected subsample of 2nd half situations, it appeared that a timeout's value was on the order of magnitude of .05 Win Probability (WP). In other words, if a team with 3 timeouts had a .70 WP, another identical team in the same situation but with only 2 timeouts would have about a .65 WP.

In this part, I'll apply a more rigorous analysis and get a better approximation. We'll also be able to repeat the methodology and build a generalized model of timeout values for any combination of score, time, and field position.

Methodology

For my purposes here, I used a logit regression. (Do not try to build a general WP model using logit regression. It won't work. The sport is too complex to capture the interactions properly.) Logit regression is suitable in this exercise because we're only going to look at regions of the game with fairly linear WP curves. I'm also only interested in the coefficient of the timeout variables, the relative values of timeout states, and not the full prediction of the model.

I specified the model with winning {0,1} as the outcome variable, and with yard line, score difference, time remaining, and timeouts for the offense and defense as predictors. The sample was restricted to 1st downs in the 3rd quarter near midfield, with the offense ahead by 0 to 7 points.

Results

Thoughts on the Extra Point

Roger Goodell is considering elimination of the extra point. I've been whining for 5 years now, advocating some kind of change to the XP. I just hope the league doesn't mess it up, like they did with overtime.

I realize quoting myself is not in good taste, but then again I've never been accused of good taste. Here are some of my thoughts.

Kicking field goals is such a peculiar and specialized thing. It has almost nothing to do with the rest of the sport but can be so decisive. It would be like getting extra runs in baseball by lacing up some skates and slapping a shoot-out shot after every home run.

Here's an excerpt from a diatribe in my old Washington Post column.

The extra point is something left over from gridiron football’s evolution from rugby. Originally, the ‘touchdown’ in rugby was less important than the ensuing free kick, and the points given for the touchdown and the ‘point after try’ varied during football’s early history. Today’s extra point is a vestige of football’s rugby roots. It’s football’s appendix–inconsequential, its original purpose uncertain...and safe to remove.

It's easy to see that XPs are pretty pointless, but it's harder to come up with some good ways to fix them or replace them. Here are some of my favorite ideas (and plenty more in the comments):

The Value of a Timeout - A First Approximation

During the NFC Championship Game the other day, we saw a familiar situation. Down by 4 with 14 minutes left in the game, the Seahawks were confronted with a decision. It was 4th and 7 on the SF 37. Should they go for it, punt, or even try a long FG to maybe make it a 1-point game? Pete Carroll ended up making what was the right decision according to the numbers, but not before calling a timeout to think it over.

As I noted in my game commentary, if you need to call a timeout to think over your options, the situation is probably not far from the point of indifference where the options are nearly equal in value. And timeouts have significant value, particularly in situations like this example--late in the game and trailing by less than a TD--because you'll very likely need to stop the clock in the end-game, either to get the ball back or during a final offensive drive. Would Carroll have been better off making a quick but sub-optimum choice, rather than make the optimum choice but by burning a timeout along the way?

Here's another common situation. A team trails by one score in the third quarter. It's 3rd and 1 near midfield and the play clock is near zero. Instead of taking the delay of game penalty and facing a 3rd and 6, the head coach or QB calls a timeout. Was that the best choice, or would the team be better off facing 3rd and 6 but keeping all of its timeouts?

Both questions hinge on the value of a timeout, which has been something of a white whale of mine for a while. Knowing the value of a timeout would help coaches make better game management decisions, including clock management and replay challenges.

In this article, I'll estimate the value of a timeout by looking at how often teams win based on how many timeouts they have remaining. It's an exceptionally complex problem, so I'll simplify things by looking at a cross section of game situations--3rd quarter, one-score lead, first down at near midfield. First, I'll walk through a relatively crude but common-sense analysis, then I'll report the results of a more sophisticated method and see how both approaches compare.

NFCCG SF-SEA Observations

As expected, this was a real defensive slugfest. The winning QB had -3.4 EPA. Kaepernick posted -0.28 WPA and 2.2 AYPA. Both offensive lines were beaten soundly. SF's notched -5.4 EPA and SEA's had -2.6 EPA.

Unlike the AFC game, this one was all about 4th downs. HUGE leverage throughout the game. I know I can be a broken record on this stuff, but this game really hinged on some very interesting strategic decisions.

-SF 4th and 2 on the SEA 7, 1st qtr. They punted. Probably should have gone for it.

-SF 4th and goal on the SEA 1. They went for it. Great call.

-SF 4th and 6 on the SEA 46, 1st qtr. They punted. Probably should have gone for it.

-SEA 4th and 6 on SF 38, 26 sec in 2nd qtr. They went for it, converted then kicked a FG to end the half.

-SEA 4th and 7 on the SF 35, 4th qtr 14 min to play, down by 4. They went for it. Great call except SEA burned a timeout that they were reasonably likely to need in order to think things over. Here's the thing: Timeouts are very valuable. If you can't decide between going for it or kicking or punting, you're probably very close to the point of indifference anyway. You may be better off making any quick decision and saving the timeout than you are making an optimum decision but wasting a timeout.

-SEA 4th and goal from the 1, 4th qtr 8:39 to play, up by 1. The went for it. Great call. Why? First, because they'll probably make it and virtually put the game away. And if they don't they're likely to leave the ball on the SF 1-yd line. That's not exactly a good place to be for an offense. I heard someone say that despite the math you can't take a chance like that against the SF defense. But as I noted last week, over the past 2 seasons SF has faced 15 (now 17) plays from the 1-yd line and allowed TDs on 10 of them. That's worse than league average. Don't get me wrong. I'm not saying that the SF defense is below average. Instead, the point is that good and bad teams aren't that different on any one given play. It's just that good or bad teams show up that way after accumulating very small advantages over several dozen individual plays in a game.

-Here's a weird one. SEA 4th and 11 on the SF 29, 4th qtr 3:43 to play, up by 3. My numbers say...punt? Yes, punt. Here's why:

AFCCG NE-DEN Observations

I thought the big story of the game wasn't how easily DEN moved the ball. We all expected that. The big story was DEN's defense, which held NE to just 3 points in the 1st half and 10 points through 55 minutes. Brady was held to -0.02 WPA. He did notch +8.3 EPA, but a lot of that was after the game was mostly decided.

NE was going to need some fluky things to go their way to win--turnovers, a special teams play, or some terrible call by the refs. It never came.

Manning and the DEN passing game did have a fantastic day. Manning: +.48 WPA, +17.9 EPA, 9.3 AYPA, no turnovers, no sacks.

I, and the NYT 4th Down Bot--(funny how you never see the two of us together at the same time), agreed with every 4th down call during the game. Belichick knows what he's doing. I was disappointed to see DEN burn a timeout just prior to NE's 4th down conversion attempt. Teams should be better prepared for a 4th down attempt, particularly in situations like this: a 4th and short in or near the red zone. In a high-leverage situation like that, it's ok for a team with a significant lead to use a timeout, but in a closer game, it would be much more costly. (I'm working on a project to value timeouts in terms of WP now, and without any spoilers--they are very precious in the 2nd half.)

Live Super Bowl Probabilities

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