Volatility is the name of the game in golf, and picking winners isn't easy. With fields of 150-plus golfers sometimes being separated by how a putt or two falls each week, predicting golf can be absurdly tough.
We'll never be able to capture everything that goes into a golfer's expectations for a week, but we can try to account for that by simulating out the weekend and seeing what happens.
The Process
Over the years, I have made plenty of tweaks to my original golf model, which uses a combination of the OWGR's field strength numbers and datagolf's field strength numbers to adjust each golfer's score relative to the field (on the PGA Tour, the European Tour, and the Korn Ferry Tour).
The ultimate goal is to place a score from the Waste Management Open, the BMW International Open, and the Knoxville Open on level playing fields. This adjusted strokes metric lets me see how golfers are performing across all tours. From there, a golfer's adjusted stroke data is combined with their round-to-round variance to see how the field is likely to perform when playing out the event thousands of times.
In addition to that long-term adjusted form, I factor in course-level adjustments for course fit.
I run a second model that uses more granular strokes gained data, which allows me to very easily adjust for course fit. The results are averaged out.
I let the data do the talking and don't make many tweaks -- if any. Golfers with a small sample get regressed to a low-end PGA Tour player to round out their samples. Data points are weighted more heavily toward recent performance.
Here are the most likely winners for the Rocket Mortgage Classic, according to the models.
| Golfer | Simulated Win% | Simulated Top-10% | Simulated Made Cut% | FanDuel Sportsbook Win Odds |
|---|---|---|---|---|
| Bryson DeChambeau | 7.0% | 34.4% | 80.3% | +700 |
| Patrick Reed | 6.2% | 33.2% | 79.6% | +1400 |
| Webb Simpson | 5.4% | 30.7% | 78.2% | +1600 |
| Hideki Matsuyama | 3.3% | 25.0% | 73.9% | +1600 |
| Will Zalatoris | 3.0% | 22.4% | 72.3% | +2000 |
| Joaquin Niemann | 3.0% | 21.6% | 71.6% | +2200 |
| Jason Kokrak | 2.7% | 19.7% | 70.2% | +2700 |
| Cameron Tringale | 2.3% | 18.1% | 68.7% | +4100 |
| Si-Woo Kim | 2.3% | 18.4% | 68.3% | +5000 |
| Jason Day | 2.2% | 18.4% | 68.9% | +2900 |
| Charley Hoffman | 2.2% | 17.5% | 67.9% | +3300 |
| Brendon Todd | 2.2% | 16.8% | 67.1% | +6000 |
| Sungjae Im | 2.1% | 17.6% | 68.2% | +2900 |
| Emiliano Grillo | 1.8% | 16.2% | 66.5% | +4100 |
| Keegan Bradley | 1.7% | 16.7% | 66.6% | +3400 |
| Lanto Griffin | 1.6% | 13.2% | 63.2% | +8000 |
| Kevin Kisner | 1.5% | 13.3% | 63.4% | +2900 |
| Bubba Watson | 1.5% | 12.7% | 62.3% | +3400 |
| Max Homa | 1.4% | 13.9% | 63.4% | +5000 |
| Chris Kirk | 1.3% | 12.6% | 62.2% | +8000 |
| Matt Jones | 1.1% | 12.0% | 61.5% | +8000 |
| Doug Ghim | 1.1% | 11.8% | 61.1% | +8000 |
| Alex Noren | 1.1% | 12.1% | 61.7% | +6500 |
| Lucas Glover | 1.1% | 10.7% | 59.1% | +6500 |
| Harold Varner III | 1.1% | 11.7% | 61.0% | +8000 |
| Mackenzie Hughes | 1.0% | 9.2% | 56.5% | +12000 |
| Charles Howell III | 1.0% | 10.4% | 58.5% | +12000 |
| Gary Woodland | 0.9% | 11.4% | 59.9% | +4100 |
| Patton Kizzire | 0.9% | 9.9% | 58.2% | +12000 |
| Sebastian Munoz | 0.9% | 10.0% | 58.0% | +10000 |
| Rickie Fowler | 0.9% | 9.6% | 58.1% | +4100 |
| Matthew Wolff | 0.9% | 8.7% | 56.0% | +2700 |
| Cameron Davis | 0.8% | 9.2% | 57.1% | +9000 |
| James Hahn | 0.8% | 7.8% | 54.7% | +21000 |
| Adam Hadwin | 0.7% | 8.9% | 56.2% | +10000 |
| Tom Hoge | 0.7% | 8.0% | 55.3% | +15000 |
| Denny McCarthy | 0.7% | 7.3% | 54.1% | +21000 |
| Doc Redman | 0.7% | 8.6% | 55.9% | +5000 |
| Joel Dahmen | 0.7% | 9.2% | 56.8% | +12000 |
| Maverick McNealy | 0.7% | 9.3% | 56.9% | +5500 |
| Ryan Armour | 0.6% | 7.6% | 53.7% | +8000 |
| Pat Perez | 0.6% | 8.0% | 55.1% | +10000 |
| Mark Hubbard | 0.6% | 6.5% | 51.7% | +12000 |
| Kyle Stanley | 0.6% | 9.3% | 57.0% | +6500 |
| Chez Reavie | 0.6% | 7.8% | 54.8% | +8000 |
| Sepp Straka | 0.6% | 7.6% | 54.0% | +6500 |
| Dylan Frittelli | 0.6% | 7.1% | 53.2% | +21000 |
| Scott Stallings | 0.6% | 7.0% | 53.1% | +15000 |
| Danny Willett | 0.6% | 6.9% | 52.2% | +6500 |
| Kyounghoon Lee | 0.6% | 7.1% | 52.9% | +15000 |
| Brice Garnett | 0.6% | 7.8% | 55.1% | +12000 |
| Troy Merritt | 0.6% | 7.4% | 54.0% | +10000 |
| Hank Lebioda | 0.5% | 7.4% | 53.8% | +12000 |
| Adam Long | 0.5% | 6.7% | 52.0% | +15000 |
| Russell Knox | 0.5% | 7.8% | 55.0% | +12000 |
| Garrick Higgo | 0.5% | 7.3% | 53.5% | +4100 |
| Richy Werenski | 0.5% | 7.1% | 52.9% | +15000 |
| Nick Taylor | 0.5% | 6.8% | 52.4% | +21000 |
| Luke List | 0.5% | 7.2% | 53.0% | +10000 |
Bryson DeChambeau (+700) is the favorite both in the simulations and also the betting market at Golf odds. The model is showing him as a pretty poor value at such a steep number. Should the simulations be higher on him at a course where he just won? Perhaps. Do I change the data to fit what I think should happen? No. Am I still considering betting DeChambeau? Probably, but I prefer him as a DFS play than an outright.
There's value on Webb Simpson (+1600), slightly. Simpson feels like he's been off the radar for a while but is in fine form and doesn't need length at Detroit Golf Club, so it's a good overall fit.
Cameron Tringale (+4100) is an even value and has finished 30th and 5th at this course, which isn't actually factored into the model. and Si Woo Kim (+5000) is rating out with positive expected value. The same applies to Brendon Todd (+6000), Lanto Griffin (+8000), Chris Kirk (+8000), Doug Ghim (+8000), and Cameron Davis (+9000), to name a few. That's what happens when the model sees a heavy favorite be overvalued.
I'm initially torn on pairing DeChambeau with these long shots or targeting more of the middle tier with Simpson, Joaquin Niemann (+2200), Tringale, and Kim.
I'll be settling on Simpson, Tringale, Kim, Griffin, Ghim, and Davis in some form or fashion.