Applying Cognitive Models to Evaluate Bias in Expert Predictions for NFL Games

Standard signal detection theory (SDT) models use an unbiased criterion as a comparison point. But, in some situations, the unbiased criterion is not the right reference point to measure bias in decision making. We consider the context of experts predicting the winning team in a National Football League (NFL) game. An unbiased criterion assumes that the home and away teams have equal probabilities of winning and that any partiality toward the home team over the away team is detrimental. However, the home team advantage exists, as evidenced by the behavior of betting markets and home teams having won 58% of the games throughout the 1981-1996 NFL seasons (Vergin & Sosik, 1999). Altogether, this suggests that experts should have some partiality toward the home team to improve their prediction accuracy. We apply hierarchical SDT models to expert predictions provided by nflpickwatch.com for the 2014-2019 NFL regular seasons to measure various forms of bias in predictions. In particular, we use the SDT framework to evaluate expert bias in terms of home team advantage, the cumulative win-loss record of teams, and herding by making the same prediction as other experts. Applying our model provides a way of measuring the extent to which experts are under- or over-reliant on these different sorts of biases when they make predictions.Vergin, R. C., & Sosik, J. J. (1999). No place like home: An examination of the homefield advantage in gambling strategies in NFL football. Journal of Economicsand Business, 51(1), 21-31. doi:10.1016/s0148-6195(98)00025-3