By: Edward Egros

PGA

...One More Thing About the PGA Championship

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(Courtesy: Stuart Franklin/Getty Images)

At one point, there was a five-way tie atop the leaderboard during the back nine of the final round of the 99th PGA Championship. Then, Justin Thomas cards a birdie on the 13th hole, enters the Green Mile with a par on 16, a birdie on 17 and an insignificant bogey on 18. While the rest of the field struggled to finish, Thomas blazed through the toughest closing stretch at a major this year, to capture his first Wanamaker Trophy.

My pick to win, Hideki Matsuyama, fared more than respectably, finishing tied for 5th. But as I watched the television coverage of the moments he struggled, one of the commentators pointed out his performance mirrored that of last year's PGA Championship, where he was the best hitter of the golf ball, but could not make any putts. At that point, he finished tied for 4th.

This year, Matsuyama missed a few critical putts, but he was 12th in Strokes Gained: Putting. However, SG: Approach the Green and SG: Around the Green were 20th and 27th, respectively. As for the champion, Thomas was tied for 15th in SG: Approach the Green, 22nd in SG: Around the Green and 4th in SG: Putting. Overall, these numbers are slightly better and equaled a commanding win.

I am reminded of a paper by Dr. George Kondraske of UT Arlington titled: "
General Systems Performance Theory and its Application to Understanding Complex System Performance". In it, Kondraske attempts to explain human systems through complex machines. Regressions have a number components that are often considered additive (which is why we have a lot of "+" signs in our equations). But if one explanatory variable is largely deficient, it is not satisfactory to say the dependent variable decreases by the same amount. The output depends upon everything working together; components are so interconnected that any one piece that does not work or is largely deficient means the entire system might fail to perform.

What does this have to do with golf? If someone cannot putt at all, they will post a high score and have no chance of winning a tournament; they cannot simply overcompensate with a longer drive or a more accurate iron shot. Granted, professional golfers are at least competent in every component of a golf game, but any significant deficiency makes for a bigger setback than simply subtracting odds to win based upon a negative strokes gained metric.

This approach is intuitive to golf enthusiasts. It is why golfers work on everything, not just emphasizing the skills with which they excel. What matters here is when data scientists are putting together models for forecasting winners, perhaps it is important to think less linearly. Maybe it has less to do with the sum of skills coming together and how they fit with a particular course, and more about if every skill is adequate for the demands of a specific tournament. Justin Thomas' skills certainly were.