Abstract
In the National Football League (NFL), teams that outperform their opponents in four categories usually are victorious. These statistics are yards gained, rushing yards gained, turnover margin, and time of possession. In fact, through the first eight weeks of the 1994 regular season, no team leading its opponent in all four categories had lost. In general, the more categories a team leads, the greater its chance of winning a game. Therefore, the relative strength of NFL teams can be established by comparing these four statistical categories. Several neural network strategies are tried to predict winners in NFL games. Binary, ternary, and continuous input vectors are used as inputs to appropriate networks: Hamming, adaptive resonance theory (ART), Kohonen self-organizing map (SOM), and backpropagation (BP). Predictions are presented, and the performance of each network is examined. Network results using supervised and unsupervised training methods are also compared
