I want to quantify that qualitative scale. And I want to do it in a retrodictive way. In other words, I’m not as interested in the degree to which the winning team outplayed the losing team as I am in the degree to which the winning team was in control of the game. To see the difference, imagine a game where one team opens up a 14-0 lead on a kickoff return touchdown and a fluke turnover that leads to a score, then cruises to an uneventful 31-17 win. The advanced stats might even show that the losing team was more efficient. The predictive measures might give the losing team a better grade, because the reasons the winning team won were not things that are likely to carry over to future games. I don’t care about any of that. The kick return happened, and the turnover happened, and the result was that the game was never in any serious doubt.
The easiest way to do this is to use margin of victory, and that works well in most cases, but there are obvious outliers. Consider the Green Bay – Washington game from week two, which was 24-0 midway through the second quarter and never really got any closer, and the Colts-49ers game, which was a one-score game with five minutes remaining. The latter game finished with a larger margin of victory. Again, if you’re interested in predictive measures, you probably do want to record that Robert Griffin III was able to generate a couple of late TDs and that the Colts were able to put away the 49ers so quickly and thoroughly. But I’m not interested in that here.
Another natural answer would be to use Chase’s game scripts. Or, if you wanted to fancy up the same concept, you could compute the average win probability throughout the game. This too would work in the majority of cases, but not always. If a game is tied with two minutes left, that’s really all I need to know: the game should be graded as “could’ve gone either way.” But game scripts (or average WP) would be sensitive to how the game progressed for its first 58 minutes. Whether one team went up 21-0 and then the other team came back to tie it, or the game was a seesaw affair, all that really matters that the game was still very much in question at the end.
In 2008, I borrowed an idea that the great Matt Hinton called Time of Knockout. Chase later refined the idea with these two posts. Those were a couple more attempts to get at what I’m trying to get at above. These are fun, but they are flawed in ways similar to margin of victory and game script. The comments to Chase’s posts contain a lot of the ideas in the discussion above.
Now I’m going to tell you my answer. Then you’ll use the comments to tell me how to improve it.
First we’ll define time_1 to be the point in the game (measured in minutes elapsed since the opening kickoff) when the eventual losing team last took an offensive snap while trailing by 8 or fewer points. And we’ll define time_2 to the be the point in the game when the eventual losing team last took an offensive snap while trailing by 16 or fewer points. Then we declare that
Moral Margin of Victory = 39.17 - .1947*time_1 - .3681*time_2
You can probably guess where this came from. I computed time_1 and time_2 for every NFL game since 2001, then I ran a regression of margin of victory on time_1 and time_2. I spent a lot of time debating whether straight linear regression was the right tool for the job, eventually deciding that I like it. But I’m certainly listening to your improvement suggestions. I’ll put a table of all the 2013 NFL games through October 27th at the end of the post, so you can find the ones you watched and determine if the MMOVs feel right.
The formula and the explanation make the idea seem more complicated than it is. Essentially, the winning team gets points for every minute after which the game was not in imminent danger, and they get bonus points for every minute after which it wasn’t in imminent danger of being in imminent danger. Pretty simple. Of course, there’s some arbitrarity here. I’m using two levels of danger here, and I could have used one or three. There are probably other reasonable definitions of danger. Again, I’d be interested in hearing if it feels about right.
Why do we need this?
For the NFL, we don’t. But I believe that a rating system incorporating this kind of data is exactly what college football needs. I am aware that the computer component will no longer be an official part of the formula determining who gets ranked where, but I would like to hope that the new committee will at least consider some set of algorithmic rankings (in the same way that the basketball people consult RPI). For many years, margin of victory was forbidden in the official BCS ranking systems for fear that it would lead to poor sportsmanship. I can understand that. But if you use the above MMOVs in place of raw margin of victory in the SRS (or any other margin-based system), you’ve got yourself a much more informative ranking and there are absolutely no sportsmanship concerns. Your goal as the coach of a national title-contending team against an overmatched opponent is to make it a three possession game as quickly as possible, and then keep it there. There is nothing unsportsmanlike about that, nor does it alter anyone’s incentives in any meaningful way.
A ranking system based on MMOV would be neither predictive nor retrodictive, rather inhabiting a middle ground that might be referred to as “resume-dictive,” which is exactly what the NCAA’s committee needs as a guide. If Florida State beats Clemson (which we’ll presume for now to be a very good team) by 37, the degree of that victory most definitely should be a part of Florida State’s resume. The fact that 37 is a large number is not what made the victory impressive; what made it impressive is the fact that the game was never in any real doubt after the first few minutes.
My goal is to publish this ranking system for college football in the coming weeks. While I’m beating the data into a usable format, tell me how I can improve the system.
And one more thing: Chase hates the name. Tell him how wrong he is.