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PGA DFS: Introduction to Power Ranking Model

There’s an overwhelming amount of statistics in golf. You can go down the rabbit hole and zoom into proximity from certain distances or swing speeds. We’ve even seen apex height touted around the industry. The way I’ve always approached model building for golf has been from a course fit perspective. Looking at how a course has played in the past and trying to find golfers that fit there from a statistical perspective has been an invaluable part of my process. I’m not going totally away from that, but there’s a certain beauty in simplicity. We want to roster good golfers. We want the guys that we click to score well. One of the best ways to determine whether or not a golfer will score well in an upcoming event is judging their talent and form over the short term and over the long term. We don’t want to miss out on the golfers who are playing well in short spurts, but we don’t want to overweight that either and miss out on supremely talented golfers over a longer timeframe. 

Power Ranking Explained

I wanted to come up with a way to quantify how a golfer performs so, rather than diving into each individual aspect of a golfer’s game, I looked at how a golfer scores relative to the field, course, etc. The raw scores from the model indicate how a golfer is expected to perform relative to the field each round. Instead of just looking at one time frame, the model incorporates both long term and short term scoring and blends it together in a way that captures both talent and recent form. It’s an easy way to see the difference between the talent and form of different golfers. Since it doesn’t incorporate recently introduced stats like strokes gained, we were able to backtest the model all the way back to 1991. 

Historical Performance

The variance in golf is mind-blowing at times. Regardless of how much you trust a given expert or model of your own, you have to know that sometimes there’s only so much you can explain and predict. At the Rocket Mortgage Classic, for example, we saw the last guy in the field get to 25-under and win the event while Dustin Johnson – the odds on favorite – didn’t even play the weekend. We want to feel reasonably comfortable that the way we value these golfers is the way they’re most likely to finish.

That’s why it’s important to backtest the model using as large a sample as possible. Although week-to-week variation is unavoidable, over a long enough time horizon we want a model that allows to get things right more often than not. Therefore, in order to test the model, I grouped the golfers in buckets and looked back over the last 28 years. Here are the finish rates for each group since 1991. 

Power Ranking - Historical Results

Power Ranking Win RateTop 5 RateTop 10 RateTop 20 RateOutside Top 70 Rate
-2 and Better11.330.545.162.413.8
-2 to -1.54.11930.648.117.2
-1.5 to -
-1 to -
-0.5 to
0 to 0.50.436.213.550.8
0.5 to
1 to 1.50.1125.368.2
1.5 to
2 to 2.500.512.382.3
2.5 and Worse000.41.489.7

As you can see, the model itself predicts finishing position fairly accurately. Whichever group you choose to look at outperforms each of the groups that follow below it with an inferior power ranking. 

Below is a first run of the model for the Open Championship.

The Open Championship - Model Results

There are "NAs" in the table. Many of them are because I don't have enough data in the set to run the numbers because of limited Euro data.

For a look at how the model has performed historically search "Introduction to Power Ranking Model" above.
NameSalaryPower RankingOwnership
Rory McIlroy11600-2.9518.9
Brooks Koepka11400-1.4116.9
Dustin Johnson10900-211.9
Jon Rahm10600-218.9
Tiger Woods10200-1.687.15
Justin Rose9900-2.0312.9
Tommy Fleetwood9700-1.3510.84
Xander Schauffele9500-1.4218.9
Francesco Molinari9400-0.619.15
Rickie Fowler9300-1.287.15
Bryson DeChambeau9200-1.498.91
Justin Thomas9100-1.2414.15
Patrick Cantlay9000-2.1321.9
Jordan Spieth8900-0.728.15
Adam Scott8800-1.8824.18
Matt Kuchar8700-1.9723.93
Jason Day8600-1.088.65
Hideki Matsuyama8500-1.9914.9
Henrik Stenson8400-1.1528.07
Paul Casey8300-1.5310.9
Gary Woodland8200-1.48.74
Matt Wallace8100-0.67.9
Louis Oosthuizen8000-0.614.9
Shane Lowry7900-1.188.26
Graeme McDowell7900-1.1210.19
Tony Finau7800-0.627.9
Sergio Garcia7800-0.778.9
Marc Leishman7700-0.8615.9
Ian Poulter7700-0.455.48
Webb Simpson7600-1.5915.9
Matthew Fitzpatrick7600-0.516.75
Eddie Pepperell7600-0.89.75
Phil Mickelson75000.365
Rafael Cabrera Bello7500-0.5815.35
Danny Willett7500-0.392.73
Lee Westwood74000.53.28
Tyrrell Hatton7400-0.878.82
Patrick Reed7400-1.019.75
Brandt Snedeker7400-0.94.66
Branden Grace73000.330.91
Alexander Noren73000.282.4
Kevin Kisner7300-0.651
Thorbjorn Olesen7300-0.153.47
Hao-Tong Li730005.92
Abraham Ancer7200-0.131.83
Jason Kokrak7200-0.763.75
Zach Johnson7200-0.345.6
Bubba Watson7200-0.252.46
Padraig Harrington72001.090.36
Erik Van Rooyen7100-1.197
Chez Reavie7100-1.774.32
Russell Knox7100-0.892.47
Emiliano Grillo7100-0.843.5
Bernd Wiesberger7100-1.218.25
Jim Furyk7100-1.222.99
Byeong-Hun An7000-0.622.91
Thomas Pieters7000-0.921.11
Sung-jae Im7000-1.041.05
Keegan Bradley7000-0.671.13
Andy Sullivan7000NA3.59
Kevin Streelman7000-1.270.56
Mike Lorenzo-Vera7000NA3.91
Joaquin Niemann6900-1.158
Aaron Wise6900-0.630.9
Jorge Campillo69003.190.2
Billy Horschel6900-0.873.32
Cameron Smith69000.291
Adam Hadwin6900-1.021.96
Joost Luiten6800-0.131.12
Kiradech Aphibarnrat6800-0.230.96
Lucas Bjerregaard6800-0.31.34
Charles Howell III6800-0.891.3
Cheng-Tsung Pan6800-0.264
Ryan Fox6700NA1.06
Kyle Stanley67000.141.4
Charley Hoffman6700-0.271.03
Luke List67000.270.64
Si Woo Kim6700-0.010.59
Alexander Bjork67001.10.56
Tom Lewis67002.720.57
Robert Macintyre6700NA0.2
Andrew Putnam6600-0.873.11
Jimmy Walker6600-0.40.39
Paul Waring6600-0.522.38
J.B. Holmes66000.130.95
Robert Rock6600NA0.34
Romain Langasque6600NA1.47
Jazz Janewattananond6600-0.90.35
Adri Arnaus66000.560.2
Lucas Glover6500-0.893.34
Adrian Otaegui65000.850.47
Brandon Stone6500NA0.81
David Lipsky6500NA0.84
Keith Mitchell6500-0.480.72
Christiaan Bezuidenhout6500NA1.01
Ryan Palmer6500-0.320.87
Alexander Levy6500NA0.35
Mikko Korhonen6500NA0.35
Shugo Imahira64000.490.52
Andrea Pavan6400NA2.24
Justin Harding6400-0.440.95
Joel Dahmen6400-0.270.93
Stewart Cink6400-0.170.45
Prom Meesawat64002.50.17
Brandon Wu6400NA0.22
Richard Sterne64001.610.33
Oliver Wilson64000.560.46
Corey Conners6300-0.440.9
Patton Kizzire63000.620.29
Chris Wood63000.150.26
Nate Lashley6300-1.090.32
Connor Syme6300NA0.16
Zander Lombard6300NA0.24
Kurt Kitayama63000.490.3
Sung-Hoon Kang6300-0.590.61
Miguel Angel Jimenez6300-0.220.1
Callum Shinkwin6200NA0.81
Jack Senior6200NA0.08
Thomas Thurloway6200NA0.11
Yuki Inamori6200NA0.24
Yuta Ikeda62000.960.26
Darren Clarke62002.230.18
Doc Redman6200NA0.23
Yoshinori Fujimoto62001.730.24
Sang-hyun Park6200NA0.31
Ernie Els62000.560.65
Chan Kim6100NA0.12
Shubhankar Sharma6100NA0.21
Takumi Kanaya6100NA0.05
Tom Lehman6100-0.860.08
David Duval61002.640.08
Sam Locke6100NA0.08
Gunn Charoenkul6100NA0.11
Dimi Papadatos6100NA0.13
Paul Lawrie6100-0.010.15
Mikumu Horikawa6100NA0.21
Jake McLeod6100NA0.21
Shaun Norris61003.350.37
James Sugrue6000NA0.13
Andrew Wilson6000NA0.11
Austin Connelly60002.90.03
Garrick Porteous6000NA0.03
Matthew Baldwin6000-10.04
Inn Choon Hwang6000NA0.04
Yosuke Asaji6000NA0.04
Dong-Kyu Jang6000NA0.05
Doyeob Mun6000NA0.05
Ashton Turner6000NA0.06
Curtis Knipes6000NA0.06
Isidro Benitez6000NA0.06
Matthias Schmid6000NA0.06
Image Credit: Michael Wade/Icon Sportswire. Pictured: Rory McIlroy.

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