Correcting for bias in hot hand analysis: An application to youth golf

This paper illustrates the problems that arise with traditional tests for the hot hand and proposes instead using a consistent dynamic panel data estimator, which corrects for these problems and is easy to implement. The issue is demonstrated by performing regression analysis on a sample of simulated data for junior golfers that does not include any dependence in a golfer’s performance across holes. The traditional regression analysis finds evidence of both hot and cold hand effects, even though the data is known to have no such effects. We resolve this problem by applying the consistent dynamic panel data estimator to a large dataset of amateur, youth golfers, to find no evidence of either hot or cold hand effects overall. When we restrict attention to the most-amateur of the golfers in our data, we do see weak evidence of a small hot hand. Thus, casual athletes may experience small hot hands, but the effect does not persist among more serious athletes. This may give insight into why the belief in the hot hand in professional sports exists, even when the evidence suggests otherwise.