Avoiding Bias Due to Nonrandom Scheduling When Modeling Trends in Home-Field Advantage

Abstract

Existing approaches for estimating home-field advantage (HFA) include modeling the difference between home and away scores as a function of the difference between home and away team ratings that are treated either as fixed or random effects. We uncover an upward bias in the mixed model HFA estimates that is due to the nonrandom structure of the schedule -and thus the random effect design matrix -and explore why the fixed effects model is not subject to the same bias. Intraconference HFAs and standard errors are calculated for each of 3 college sports and 3 professional sports over 18 seasons and then fitted with conference-specific slopes and intercepts to measure the potential linear population trend in HFA.