Alright, everybody, it’s time for round two. Last year was my first go around with my own ranking system (The Sterritt Score) and writing a weekly column [See the final rankings here; all previous editions can be found here]. Hopefully this year I can bring even better content and analysis for all of y’all. There aren’t any results to discuss yet, but I wanted to take a bit of time to refresh or introduce everyone to what I’m doing.
My name is Ryan Sterritt. I am currently a senior at Auburn, and I am majoring in chemical engineering. I love Auburn, I love college football (most other sports, too), and I’ve always been a numbers guy at heart. Last summer, I was fascinated by our good friend WarRoomEagle’s delve into quantifying what a "knockout" would be in college football. I highly recommend reading through anything WarRoomEagle wrote here, and his posts on knockouts especially. To summarize, though, he determined that a knockout in college football occurs at the last moment the losing team has possession while within 8 points. For example, if Team A scores a touchdown to go up 10 points in the third quarter, and Team B is never again within 8 points of Team A, the knockout would be at the last time Team B had the ball before Team A scored that touchdown. Team B never again in that game had the ball with a chance to tie or take the lead. Thus, they were knocked out and received a negative KO time while Team A received the same (but positive) KO time.
It’s an interesting concept. On its own, though, it doesn’t tell the whole story. After reading those articles, I decided to expand upon the idea. What if the knockout time of a team could be used to help rank them? What else needs to be included with it? On a whim, I decided to use his knockout season averages for teams to help make my own Top 25 from the year before. Using a combination of knockout times, margin of victory, record, and strength of schedule, the Sterritt Score was born. At the suggestion of my friends, I turned it into a weekly column here at College and Mag, and it was a ton of fun to watch the rankings unfold through the season. Because I only used numbers from the current season, the first few weeks had some wacky results. Teams which had beat up on lowly Group of 5 teams soared, as their average margin of victory and average knockout times were through the roof, and teams which narrowly beat out solid competition struggled early on, as my rankings had no way of signifying who a quality opponent was so early in the season. If you’ll remember, Navy held a top 5 spot for a few weeks, before finally crashing down late. While I won’t stand here and say that I think my rankings got everything right, especially earlier in the season, I do think it provided some fun discussions on who was doing what on the national stage.
The way I calculate each team’s "score" is relatively simple. I want to do some further work on it over the next few years, but for right now, I’m keeping it fairly intuitive. It was built on the idea of knockouts and I try to keep it true to that. I want this poll to go a little bit deeper than simple "eye tests" and historical biases, but at the same time, I try to keep it at a level where I could explain it to even the casual football fan. So if you’re looking for a more advanced, mathematically correct formula, I’m not your guy. That’s not to say I don’t like them; I personally think Bill Connelly’s rankings are some of, if not THE, best in the business. But this is just my fun pet project.
Note: If you care about the specifics of how I get my rankings, read the next paragraph. If you like the ideas but aren’t big on playing with the math, I won’t be offended if you skip this part.
In calculating a team’s Sterritt Score, I use four metrics; average knockout time (KO), average margin of victory (MOV), win percentage (W%), and strength of schedule (SOS). Strength of schedule is a tricky one because there is no simple way to assign a value to it. However, I felt this was necessary to include because there had to be a penalty for good teams beating up on bad opponents. So the way I went about getting a number for SOS was just by taking the total record of the team’s opponents (sans the games that said team was involved in) as a percentage. For example, if Auburn’s opponents’ had a combined win/loss record of 100-50 last year, then that means Auburn’s opponent’s on average had a win percentage of 0.666 (100 wins out of 150 games played). With those parameters defined, here is the general formula:
The MOV and KO are used to find a base value to compare the team to the hypothetical average team, which would have a 0 average KO and MOV. The SOS and W% will be values between zero and one, which will scale the base value. For example, if a team has faced a rough schedule (as Auburn’s proposed above), their SOS would be 0.666. However, a team with a weak schedule might have an SOS value of 0.400. This would bring their score down significantly more than Auburn’s SOS would. The same sort of concept applies to the W%. I won’t go through all of it here, but I had to make a few changes for teams that score negative in one or both of MOV and KO. If you’re really interested look in the excel files I posted in last year’s rankings.
Each week I’ll try and point out some fun storylines, as well as discuss how Auburn specifically is faring. This is an Auburn blog, after all! I know I’m looking forward to this fall, I hope you guys are, too! Let me know in the comments if you have any thoughts or suggestions.
Happy game week, and War Eagle!