Golf Analytics

How Golfers Win

Measuring the Signal in First Round Performance

After the 1st Round of the Deutsche Bank Championship a month ago, Keegan Bradley sat two strokes off the lead. Playing in front of the home fans, Bradley fired a six under 65 fueled by great putting (4.2 strokes gained) and a solid long game (2.3 strokes gained on tee shots and approach shots). At that point he looked in great shape keep it going and capture his first win of the season. However, he came out the next three rounds and shot 71-69-71 to finish T16. The culprit wasn’t his long game either; he gained 1.6 strokes on the field per round in the second, third, and fourth rounds, good enough to finish in the top ten for the event in strokes gained off tee shots and approach shots. No, it was the putter that let him down. After being hot in the opening round, he actually lost 0.4 strokes per round from his putting.

My question is: how common is Bradley’s experience? When golfers come out in the 1st round and play/putt very well, how often do they keep playing/putting well? What about when they come out hitting the tee shots and approach shots well? Does that carry over to the next day? Many around the game act like one round of performance is really meaningful (just look at everyone who advocated for playing Jordan Spieth and Patrick Reed after their Friday morning 5&4 win at the Ryder Cup), but does first round performance tell us anything about how a player will perform in the following round?

Looking at Putting:

I gathered round by round Strokes Gained Putting data from the twelve most recent PGA Tour tournaments (Travelers Championship through the Tour Championship). First, I checked how 1st round putting performance predicted 2nd round putting performance. That’s the first graph below, and the results show how player putted in the 1st round hardly sheds any light on how they will putt in the 2nd round (R^2 of 0.001). In fact, someone who putted as well as Keegan Bradley did in the above mentioned round would be predicted only to putt 0.2 strokes above average the following round.

rd1SGP v rd2SGP

Next I generated prior expectations of Strokes Gained Putting performance from the past several years of data. I’ve shown before that putting performance isn’t very consistent season-to-season, so I’m using performance from 2011 to 2014 to generate the prior. The below graph shows how well the prior expectation predicted 2nd round putting. The results still were not highly predictive – R^2 of 0.01 (performance round to round is highly variable in golf) – but the regression line produced tracks pretty closely with results. Players predicted by the prior to putt well generally putted well and those predicted to putt poorly generally putted poorly.

priorSGP v Rd2SGP

Finally, I tied both pieces of information together. The prior estimate proved way more predictive than just 1st round performance, but does 1st round performance have any information to add? I set-up a linear regression with the prior estimate as x1 and the 1st round performance as x2. The results indicated 1st round putting performance provides no extra information to predict 2nd round putting performance (the coefficient was indistinguishable from zero). If you have a good guess of how well a player will putt, you can safely ignore first round putting performance.

Looking at Long Game Performance:

The long game is tee shots and approach shots (drivers/woods/irons essentially). I gathered long game performance data from the same twelve PGA Tour tournaments for the first and second rounds. I then ran the exact studies as above just substituting long game data for putting data. The correlation between 1st round long game performance and 2nd round long game performance was higher than with putting, but still didn’t contain a lot of information (R^2 of 0.03). If a player plays four strokes above field average in long game strokes gained, they’re expected to play 0.6 strokes better in the long game in the 2nd round.

rd1LONG vs rd2LONG

There was also a higher correlation between my prior estimate for long game ability and 2nd round long game performance (R^2 = 0.10). Again though, the regression line tracks closely with the results. Top ten long game players (around +1.2 strokes or above) generally performed to that level in the 2nd round.

priorLONG vs. rd2LONG

Tying both pieces together indicated that there is a small amount of signal in 1st round long game performance. Combining the prior estimate with 1st round performance slightly increases the fit of the model. The regression equation suggests that you should weight your prior estimate at twelve times the strength of first round performance. This indicates that someone who is PGA Tour average in long game shots, but produces an elite round of 4.0 long game strokes gained, should be expected to play about 0.3 strokes above average in long game shots. That seems like a small difference, but it’s enough of a shift in talent to move a player from around 50th best in the world to about 30th best in the world.

The Takeaway:

Based on these results, it looks like 1. a single round of performance is much less predictive than an estimate built on past-performance and 2. the small amount of signal contained in single rounds is from performance on tee shots and approach shots. Putting results from one round provide no more information than was available before the round. On the other hand, golfers who play particularly well on tee shots and approach shots in a round should perform slightly better than expected the following round.

 

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2 responses to “Measuring the Signal in First Round Performance

  1. Pingback: Research on Pyschological Impacts on Performance | Golf Analytics

  2. Pingback: Don’t Trust a Hot Putter | Golf Analytics

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