Gdula's Golf Simulations: AT&T Pebble Beach Pro-Am
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 adjust for course fit very easily. 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 AT&T Pebble Beach Pro-Am, according to the models.
| Golfer | Simulated Win% |
Simulated Top-10% |
Simulated Made Cut% |
FanDuel Sportsbook Win Odds |
|---|---|---|---|---|
| Patrick Cantlay | 9.9% | 45.3% | 83.7% | +750 |
| Daniel Berger | 8.7% | 47.5% | 86.4% | +1400 |
| Will Zalatoris | 3.9% | 29.2% | 77.9% | +1900 |
| Sam Burns | 3.3% | 23.8% | 71.0% | +3700 |
| Paul Casey | 3.2% | 22.1% | 69.5% | +1800 |
| Jason Day | 3.0% | 22.6% | 71.0% | +2100 |
| Si Woo Kim | 2.8% | 20.2% | 67.3% | +3700 |
| Jordan Spieth | 2.8% | 15.0% | 62.4% | +2300 |
| Cameron Davis | 2.5% | 20.6% | 69.3% | +4100 |
| Kevin Streelman | 2.4% | 19.7% | 67.2% | +3800 |
| Joel Dahmen | 2.0% | 17.0% | 63.5% | +7500 |
| Max Homa | 1.7% | 14.0% | 60.1% | +3300 |
| Rickie Fowler | 1.6% | 15.2% | 64.2% | +4800 |
| Brendan Steele | 1.6% | 15.1% | 62.3% | +7500 |
| Brian Harman | 1.5% | 17.8% | 69.4% | +4300 |
| Cameron Tringale | 1.5% | 16.1% | 68.5% | +3500 |
| Charley Hoffman | 1.4% | 14.9% | 63.9% | +14000 |
| Peter Malnati | 1.3% | 12.1% | 59.0% | +8500 |
| Harold Varner III | 1.3% | 17.8% | 65.7% | +7000 |
| Francesco Molinari | 1.2% | 9.9% | 56.3% | +2300 |
| Henrik Norlander | 1.2% | 13.5% | 64.3% | +4300 |
| Patton Kizzire | 1.1% | 11.5% | 58.6% | +14000 |
| James Hahn | 1.1% | 12.4% | 63.4% | +8000 |
| Rory Sabbatini | 1.1% | 11.8% | 59.5% | +12000 |
| Phil Mickelson | 1.0% | 8.5% | 51.9% | +4700 |
| Alex Noren | 0.9% | 11.6% | 60.4% | +7500 |
| Mark Hubbard | 0.9% | 11.2% | 59.6% | +9500 |
| Patrick Rodgers | 0.9% | 9.5% | 55.8% | +17000 |
| Doug Ghim | 0.8% | 9.9% | 58.8% | +9000 |
| Adam Long | 0.8% | 10.2% | 57.7% | +10000 |
| Matthew NeSmith | 0.8% | 10.0% | 57.4% | +6500 |
| Chris Kirk | 0.8% | 9.0% | 56.5% | +8000 |
| Chez Reavie | 0.8% | 9.1% | 55.7% | +9000 |
| Jhonattan Vegas | 0.8% | 9.6% | 54.2% | +15000 |
| Kristoffer Ventura | 0.7% | 9.6% | 56.8% | +25000 |
| Matt Jones | 0.7% | 9.6% | 58.9% | +6500 |
| Tyler Duncan | 0.7% | 8.4% | 54.4% | +20000 |
| Scott Piercy | 0.7% | 8.6% | 54.3% | +14000 |
| Jim Furyk | 0.6% | 8.4% | 54.6% | +9000 |
| Maverick McNealy | 0.6% | 8.3% | 53.7% | +9000 |
| Ryan Moore | 0.6% | 7.6% | 52.6% | +10000 |
| Tom Hoge | 0.6% | 8.9% | 57.0% | +13000 |
| Stewart Cink | 0.6% | 8.7% | 55.1% | +15000 |
| Tom Lewis | 0.6% | 7.0% | 49.3% | +20000 |
| Michael Thompson | 0.6% | 7.4% | 53.8% | +9000 |
| Akshay Bhatia | 0.5% | 7.1% | 48.9% | +25000 |
| Brandt Snedeker | 0.5% | 6.2% | 49.3% | +9000 |
| Bo Hoag | 0.5% | 7.0% | 53.1% | +15000 |
| Harry Higgs | 0.5% | 7.5% | 52.7% | +15000 |
| Chesson Hadley | 0.5% | 6.6% | 50.9% | +18000 |
| J.B. Holmes | 0.5% | 5.5% | 44.5% | +18000 |
| Scott Stallings | 0.5% | 7.5% | 55.7% | +13000 |
| William Gordon | 0.5% | 6.9% | 50.8% | +15000 |
| Nick Taylor | 0.5% | 6.4% | 52.5% | +7500 |
With Dustin Johnson withdrawing, he frees up about 20% of the expected wins (both at his win odds of +400 and my model's expectation of a 20.2% win probability).
Patrick Cantlay rates out a little overvalued here, but the models again love Daniel Berger, who had a rough go last week with a missed cut. One week (and two rounds in this instance) isn't that predictive. He finished fifth here last year, so I'm warming up to going back to him this week.
Will Zalatoris pops here, as does Sam Burns at much better odds. Each had top-25 finishes last week, though Burns gained 10.9 strokes from putting, which isn't sustainable by any means.
Jordan Spieth is worth discussing. He entered the Avatar State on Saturday at the Waste Management Phoenix Open and didn't hit the driver well at all. Spieth has, though, fared really well at this event with eight top-25 finishes and a win (2017). He's far from all the way back, but if he is back, then he'll get bogged down by the older data, even if it's weighted less heavily. He's now close to 3.0% of the expected wins, so he's a little overvalued at +2300.
Value has opened with Johnson out of the field. The best bets on a sheer value standpoint are Berger (+1400), Joel Dahmen (+7500), Burns (+3700), Charley Hoffman (+14000), and Brendan Steele (+7500).
My outrights so far are Berger, Burns, Cameron Davis, and Dahmen with top-10s on Brian Harman, Cameron Tringale, and Ryan Moore.
