Team Project, Network Analysis, Algorithms, Optimization
Using win probabilities, centrality measures, Bradley-Terry rankings, and loop detection and removal, we construct an optionally weighted directed graph representing a season of college football and derive from it a ranking of the college football teams for that season. We use weekly prediction to evaluate our ten implemented ranking algorithms and compare performance with the AP Poll. We find that our modified BeatPaths algorithm outperforms all the other algorithms we tried, weighted algorithms outperform their unweighted counterparts, and our garbage time weighting scheme slightly outperforms a margin of victory weighting scheme. Notably, many of our network-based algorithms outperform the AP poll, which is traditionally considered to be the go-to metric for comparing college football teams.