How can two people with the same make percentage have different Strokes Gained performances?

Super interesting things happen when we are able to also view Strokes Gained putting when evaluating putting performance. Traditionally, we have just looked at the number of putts to evaluate if a round was good; then if we kept more detailed stats, then we might be able to do make percentages from different distances. The new way of looking at putting performance is by using Strokes Gained as a primary variable, and then make percentage and other putting variables as supporting variables.

The new way of looking at putting performance is by using Strokes Gained as a primary variable.

Let’s take a look at a two players’ performances on 25+ putts:

Adam Bland’s putting stats.

Adam Bland: 6.3% make percentage; Strokes Gained avg: -0.133

 

 

 

 

Andrea Wong’s putting stats.

Andrea Wong: 6.3% make percentage; Strokes Gained avg: -0.085

 

 

 

These two players have exactly the same make percentage, but different Strokes Gained numbers. How can this be?

Make percentage is a binary statistic: either the ball goes in or it doesn’t. If the ball doesn’t go in, it doesn’t matter how much it misses by. Strokes Gained captures the nuance and gives a missed putt that ends up close to the hole a better score than a putt that ends up a long way from the hole.

Make percentage is a binary statistic: either the ball goes in or it doesn’t. If the ball doesn’t go in, it doesn’t matter how much it misses by. Strokes Gained captures the nuance and gives a missed putt that ends up close to the hole a better score than a putt that ends up a long way from the hole.

In this case, Andrea Wong beats Adam Bland by (-0.085)-(-0.133)-=0.048 per round. This means that every time they play a round of golf, Andrea performs better than Adam by 0.048 strokes per round on just long putts over 25+ feet. This is simply due to better speed control.

First, if you need to review exactly how strokes gained works, you can check out this blog post in the meantime.

Let’s take a look at a situation where both putts will generate an equal make percentage but a different strokes gained value:

Example 1: Let’s say a player has a putt from 33 feet. The expected score for a tour player from 33ft is 2.0. If the player narrowingly misses the putt and the ball ends up 1 inch from the hole, at which point the new expected score from 1 inch would be 1.0. On a make percentage basis this would just be a ‘miss’; and the strokes gained value the player would get from this putt would be 2.0-1.0-1=0.0.

A missed putt is always a ‘missed putt’ regardless of how much it misses by.

Example 2: Let’s say a player has a putt from 33 feet. If the player instead misjudges the speed and hits the putt 33 ft long, the strokes gained calculation becomes: 2.0-2.0-1=-1. On a make percentage basis, this would also just be a ‘miss’, despite being a way worse putt than the one in example 1.

The strokes gained methodology more accurately captures the difference in performance level on different putts.

Both of the above putts generate a ‘miss’ when using the make percentage statistic; but the strokes gained methodology is able to capture this difference in performance level of these two putts and accurately gives the first putt, which was hit to 1 inch from 33 feet, a way better score than the putt hit 33 feet long.

This is why we really like using the Strokes Gained variables as a primary variable in an objective way, where we simply look at them as an objective performance score. Then we have to put the score in a relevant context, which is described more in detail in this blog post. Make percentage is still an interesting variable, but better used as a supporting variable since it is binary and doesn’t account for the interesting nuances that happen during a round of golf.

About the Author:

Thomas is a professional golfer and has played events on the European Tour, Web.Com Tour, Asian Tour, PGA Tour of Australasia as well as on PGA Tour China/Canada/Latinoamerica. He built Anova.Golf when no existing products could answer his detailed performance questions. The resulting information from Anova was astonishing: what he thought of as his biggest strength ended up being his biggest weakness.

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