With the first college football games kicking off this Saturday, it’s time to update Movelor, our college football rating system. With that update comes an updated explanation of how it works, though I should warn you—it’s the same formula as last year.
The Name
Movelor’s name is close to an acronym, standing for Margin of victory-based elo with recruiting, and that name is to some extent self-explanatory. The only live inputs into the continuously updating formula are 1) the margin of victory or defeat in each game involving a Division I football team and 2) each team’s offseason recruiting score on the 247Sports Composite. Movelor is a very simple system. All it cares about is which teams win or lose, the margin by which that happens, and how well those teams are seen to recruit. Still, it performs fairly well, which we’ll get to in a few moments.
The Scale
The numerical ratings within Movelor have no minimum or maximum. Their average is roughly 0.0, meaning the average Division I football team measures out at 0.0 on this scale. The ratings are equivalent to points per game, meaning a team with a Movelor rating of 28.0 should be expected to beat an average Division I football team by 28 points on a neutral field.
The Movelo Parts
The elo system is the core of Movelor. Its name and much of its design from a rating system first devised in chess (it’s called Elo over in that world, named after its inventor, Arpad Elo). The basic idea of elo systems is that each competitor has a numerical rating and that each rating updates with every competition the competitor completes. If the result of the competition is surprising—if the competitor wins over an opponent with a much higher rating than their own or loses to an opponent with a much lower rating their own—the rating will swing by a broader margin than if the result is unsurprising—if the competitor wins as a significant favorite or loses as a heavy underdog. Wins improve elo. Losses worsen it.
We add on to this with a margin-of-victory component, one which operates in a logarithmic manner, meaning the difference between a 14-point victory and a 7-point victory is larger than the difference between a 28-point victory and a 21-point victory in Movelor’s eyes. The way Movelor handles margin of victory is to look at its own expectation for the margin of a given game and then, just as in the elo piece, assign a larger change in rating to teams who surprise by a lot and a smaller change to teams who surprise little. In short: If Alabama is a 14-point favorite in Movelor’s eyes and wins by 17, Alabama and its opponent will not see their ratings change very much. If Texas is a 3-point favorite in Movelor’s eyes and wins by 42, Texas and its opponent will see their ratings change dramatically.
Home field matters in college football, and we account for location of games in the initial expectations against which the margin is compared. Our best estimate is that home-field advantage is worth three points.
The Recruiting Part
The only other element to Movelor is the R, recruiting, and it is a small element. It’s the only offseason change we do—we don’t even regress ratings to the mean—meaning the bulk of the initial ratings at the beginning of each season are simply the final ratings from last year.
Movelor looks at recruiting through the lens of a “talent score,” a weighted average of recruiting class scores over the last five years. It phases these scores in and out, with the class from three years ago twice as important as the classes from two and four years ago and those classes twice as important as the classes from one and five years ago. This is an extremely, extremely basic way to grade how much talent is in a program.
Once Movelor has each team’s talent score for a given season, it compares it to the same team’s talent score from last year. It then adjusts their rating accordingly. They could have had great recruiting, but if it isn’t as good as their previous recruiting was, their Movelor rating will go down. In this way, it normalizes each team’s talent level to itself. If a team has performed well without much talent, Movelor doesn’t doubt them because they lack talent again this year.
Movelor’s Strengths and Weaknesses
The biggest thing people balk at when learning how Movelor functions—and this is fair, I get it, I have a similar reaction—is the recruiting piece of the formula. Specifically, people balk at it not accounting for transfers. We were highly concerned about this piece last year, especially when Movelor had USC ranked 79th in the country to begin the season. In the end, though, it wasn’t a big problem for Movelor’s evaluation of USC, and it doesn’t appear to be a big problem overall. USC did rise through the Movelor rankings, eventually finishing around the edge of the top 25, but it only went 8–6 against the Movelor spread, implying some of what happened with USC was that they did get better as the year went on. (Also, USC was never as good as the media consensus labeled them to be, something which showed up across all sorts of rating systems, as well as on the field against Utah and Tulane.)
The next-biggest thing people balk at is the lack of any other offseason adjustment. Again, I’m with you, I think systems that consider coaching changes are generally stronger than Movelor, and I think systems that consider returning production and specific players are generally stronger than Movelor. At the same time, though, they aren’t *that* much stronger. Movelor’s average absolute error last year across all FBS and FCS games was 13.4 points per game. Across games between two FBS teams, it was 13.0 points per game. ESPN’s FPI had an average absolute error of 12.3 points per game across games between FBS teams. The closing Vegas spread had an absolute error of 12.1 points per game across games between FBS teams. (Those numbers are taken from The Prediction Tracker.) Movelor isn’t the strongest system out there, but it’s functional, and it can point towards some inefficiencies in how we perceive college football teams.
For example: I would offer that the college football discussion ecosystem overemphasizes recent recruiting and individual players while underemphasizing total team athleticism. I would also offer that those who rank college football teams in the offseason often do so with an eye on entertainment, stemming from either their own boredom, their desire to ultimately look smart, or a desire to engage their following. Perceptions of momentum likewise run rampant in college football evaluations, with a relevant line of thinking this season seeming to be that because Florida State got better last year, it must get even better this year. That’s not necessarily going to be the case.
Movelor doesn’t consider tempo, and it doesn’t break out into offensive and defensive components. These could limit its precision—short-field touchdowns after a penalty and a bad kickoff are just as important to Movelor as well-executed drives down the field—and it makes it so we can’t forecast totals or exact scores, but again, Movelor gets the job done (and if anything, going back to the USC example, it should have led us to overestimate the Trojans by the end of last year).
Movelor also doesn’t look backwards. Once a result is into Movelor, Movelor never looks at that game again. So: If Utah blasts Florida next week and Florida goes on to be a lot better than Movelor expects, Movelor won’t change its impression of Utah’s victory based on how its impression of Florida has changed. All scores are final, so to speak. Again, this mostly works out fine. As with all of this stuff, there are things we’d like to improve with Movelor, but the thing we want to emphasize the most is that this is a very basic rating system, yet still one that gets the job done.
Acknowledgments
We take a lot of our ideas from the elo-based models of Nate Silver and Jay Boice. We also are indebted to collegefootballdata.com for compiling the schedule each year. As always, we are most indebted to you, the reader, for helping this be more than a dorky hobby. Thanks for being here.
