Golf Analytics

How Golfers Win

Regression Rules Everything

This post will be number/graph heavy, but it explains perhaps the most important concept in predicting golf performance – everyone regresses to the mean, no matter their performance. The below are two charts that show this effect in action. The first uses large buckets and compares all players performance in seasons with N > 50 rounds with their performance (regardless of N) in the subsequent season. The following shows similar data, broken down more at a more granular level, which also includes which percentage of seasons meet the criteria. Read the buckets as seasons within 0.05 standard deviations.



In the first graph, all golfers better than +0.30 (approximately Tour average) in year 1 declined in year 2. Those worse (think Challenge Tour average) did not improve or decline, on average. Only those who performed very poorly in year 1 actually improved. For those better than PGA Tour average, the decline was fairly uniform (~0.05 to ~0.10 standard deviations). Remember, these are the aggregation of huge samples; many players improved at all skill levels, but on average regression/decline ruled everything.

In the second graph, the most important lesson is how rare the truly elite seasons are. Only roughly 1/4 of seasons came in below -.15 (which is roughly the talent level of the average PGA Tour card holder). The cut-off for the top 5% of seasons (2010-2012) came in at -0.45. Also, the regression of almost all players is evident; no bucket better than +0.35 improved in the subsequent season.

This data is fairly strong evidence that we should expect decline from most performances, on average. In fact, based on the rarity of rounds and the demonstrated regression, we should be skeptical about predicting any elite performance to be repeated the following season.


One response to “Regression Rules Everything

  1. Pingback: Putting Driven Performance Changes are Illusory | Golf Analytics

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