We're reworking our website and still have some loose ends.
SportsQuant College Football Rankings
Here are the current 2012 rankings based on our proprietary algorithm that allows comparisons among teams that do not play one another directly.
Our 2012 BCS Bowl predictions are here.
SportsQuant Gets Some Press!
Objective Sports Analysis
Have you ever complained at your favorite team's personnel decisions? Have you ever groused about your coach's play selection? Have you ever argued about who really is #1? So have we, and that's why we're here. At SportsQuant, we apply operations research1 methods to sports problems such as game strategy, player evaluation, injury profiling and salary-cap management. You can read more at our behind the numbers page.
OR, which has its origins in the Allied effort in WWII, is the quantitative study of complex systems. Of course the methods have advanced considerably over the past six decades, and continue to advance, including our contributions.
Game situations, like all complex systems, are impossible to describe using conventional mathematics and, consequently, require specialized quantitative methods. Since these complex systems can't be optimized analytically, one alternative is to model mathematically the relevant information (event probabilities, game decisions, etc.) and simulate the events you're interested in. You can visit our football and baseball simulators by following the hyperlinks or visit the main simulation page by clicking the button on the left of the page.
The football game simulator mathematically generates the conclusion of an NFL game from a situation that you choose (for example, trailing by 3 points with the ball at your own 35 yard-line). You can use this applet to investigate the probability of winning a game under these circumstances.
The baseball lineup optimizer lets you explore the effects of lineup ordering on run production. Rearrange the batters to your liking and then simulate to determine average runs per 9 innings and an approximate distribution of runs scored. The discussion page gives a few heuristic observations based on simulated data and empirical evidence.
This is how we got our start. Dissatisfied with the available computer ranking algorithms used in college football, we developed our own. Unlike other ranking methods, which focus either on wins and losses or points scored, our rankings use both win/loss and scoring data to present a more complete picture of teams' abilities. You can read more about our rankings here or jump to the current rankings here.
[Warning: Rant] Automated ranking programs frequently differ from human opinion polls. We'd argue the computers tend to get it right because human voters can't possibly recall all the game information over the course of the season. Furthermore, people are stubborn. It's common practice in the media polls that a team which wins doesn't drop in the polls, while a team which loses should. So, for instance, if #2 loses to #1 (which is entirely consistent with their rankings), #2 will almost certainly fall in the polls. Why should the #3 team be virtually guaranteed to move up just because the schedule forces a #1 vs. #2 showdown that turned out the way their current rankings would predict?