Now that we're two-thirds of the way through the season, let's look at who is losing the most strokes with the new +5 max rule.
The genesis of the rule change was the Board's hypothesis that higher handicapped players were unfairly getting an advantage, especially on par 3s. Since any max score is a benefit to ALL players, one can easily rationalize that high handicaps hit the max more often, and thus receive that benefit often.
Without any data to prove this hypothesis (it's impossible to tell what previous max scores were natural or not, let alone what those unnatural scores would have been), the Board settled on changing the max score to +5 for all holes. At least one member wanted NO maximum, but conceded that wouldn't work unless we kicked out over half the league.
Personally, I didn't need any data to prove this was a good rule change. I only needed to listen to the cries of our resident "canary in the coal mine" (Gorecki) to know this was a needed adjustment. In particular was the objection that "this would only hurt higher handicappers!"
Yep. That's the entire point. #playbetter
Six weeks in, what does the data show? We can now count how many extra strokes each person has received per round, that they would not have received in the old system: 7s and 8s on par 3s, 9s on par 4s.
The median Index of those losing more than half a stroke per round: 21.6. The median Index of those losing less than half a stroke per round: 13.1.
Should we expect to see handicaps increase for those at the top of the list, thus giving them more strokes overall? Not necessarily. The Equitable Stroke Control portion of the handicap formula prevents this from happening (quickly). In all tournaments, no matter your handicap, terrible blow-ups should remove you from the winner's circle.
So keep the ball in play and strengthen your course management!

Stataticiians would argue that a small sample size would increase your margin of error for this type of data set. One of your members with 2 rounds only played 19 of those 36 holes with 2 good hands. A sample size of less than 30 is considered too small and increases the likelihood of a Type II error skewing the results, which decreases the power of the study.
ReplyDeleteSounds like that member’s hands are too small...
ReplyDeleteI love these kinds of stats. I'm very interested to see how this plays out over the next few seasons.
ReplyDelete