cloudxtreme / cfbrank Goto Github PK

cfbrank -- A college football ranking algorithm

Overview

With a rather small slate of games each year, ranking college football teams is a rather puzzling challenge. Lots of different metrics can be used, all with their own advantages and disadvantages. I've set out to make a simple set of criteria with as few parameters as possible to (hopefully!) come up with a sensible ranking of all Division I FBS teams.

My general thoughts on how to determine the ranking of teams are as follows. First and foremost, overall record matters. With far more teams than there are slots in any one team's schedule, this is the best first order metric to use. However, not all schedules are created equal, and so the overall quality of resume must be considered, as well.

Ranking schemes

There are a few different ranking schemes that can be utilized, and they are described individually below.

Original method

The following method was the first implemented, and thus carries the completely non-descriptive name of the "original" method. The general ranking scheme is as follows. For a given team, T, we consider T's winning percentage w_0 (with FCS penalties) and T's opponents' winning percentage, w_1 (with FCS penalties). Then T's point total is given by

P = A_0 w_0 + A_1 w_1,

where A_i are constants to weight the importance of each parameter. There is one caveat. Scheduling FCS teams shouldn't be rewarded [1], and therefore a FCS win counts as a fraction of a whole win, where that fraction is another tunable parameter, B. A loss to an FCS team counts the same as a loss to an FBS team.

AWPMOV Method

The AWPMOV method considers a team's wins, opponents' winning percentage, and margin of victory (actually point differential as currently implemented). The score is calculated by

P = A_0 w_0 + A_1 w_1 + A_2 w_2,

where w_0 is the winning percentage, w_1 is the aggregate winning percentage of opponents that the team has beaten, and w_2 is the normalized point differential.

Park-Newman method

See J. Park and M.E.J. Newman, J. Stat. Mech. 2005, 10014 (2005) or arXiv:physics/0505169. This method is not yet implemented.

Data entry

Perhaps the most daunting thing about developing a CFB ranking algorithm is entering a season's worth of data (even if only one week at a time!). Luckily, there are a number of places on the web where basic data can be downloaded in simple CSV format. Right now, cfbrank has functionality provided by the dataparse module to parse CSV files from Sunshine Forecast, the NCAA [2], and cfbstats.com (soon).