The forecasting accuracy and determinants of football rankings

Abstract

Numerous models vie to explain the extent to which and the manner in which people use new information to reconsider existing beliefs. We analyze week-to-week changes in the rankings of big time college football teams to test predictions based on these models. We test logistic regression models of whether Top 25 teams moved up a certain number of places in the rankings following victories, using data on weekly movement in the AP rankings, 1985–95. The predictors in these models are indicators of inertia and constraint, rank elevators, and the passage of time. Lower-ranked teams move up faster after a victory than do higher-ranked teams, but moving up in the rankings after a victory is an incremental process — much more incremental than moving down after a loss. All the focal predictors in the models perform as expected in influencing a team’s chances of moving up following a win. That is, the odds of moving up are greater: the fewer prior losses a team has suffered; the lower the team’s rank before the victory; if an opening has occurred higher in the rankings; if the victory is over a ranked opponent, and especially a higher-ranked opponent; and if the standing of opponents played earlier in the season has risen. These results are most compatible with a model that combines what are often treated as contradictory ideas — that people are ‘naive scientists’ or ‘intuitive statisticians’, on the one hand, and that they are extremely conservative information processors, on the other.