We take a break from football to bring you a golf engine which uses machine learning to evaluate 1,500 different statistics for every golfer on the PGA Tour over each tournament since 2004. The analysis of this massive dataset provides an opportunity to predict players that are due to go low.
The engine looks at how each statistical set contributes to what we can expect from players on this stage, at this tournament. It’s a complex web of information that can only be properly analyzed by a math engine, yet yields some objectively surprising results.
This year’s Championship is no exception as the model is calling for Phil Mickelson (100/1 odds) to break into the top 5, with Kyle Stanley (80/1 odds) and Tony Finau (40/1 odds) cracking the top 10!
Kyle Stanley (80/1 odds), Tony Finau (40/1) and Phil Mickelson (100/1) inside the top 10.
Zach Johnson (100/1) and Patrick Cantlay (50/1) inside the top 15.
Perhaps just as surprising are the golfers that may underperform this week. Two recent Major Championship winners were projected outside of our top 20 – Francesco Molinari (33/1 ) and Jordan Spieth (20/1).
Tiger Woods (28/1), Henrik Stenson (50/1) and Alex Noren (50/1) finishing outside the top 25!
Rickie Fowler (22/1), Justin Rose (22/1), Patrick Reed (35/1) and Bubba Watson (50/1) all finishing outside the top 10.
A few more points of note:
Dustin Johnson (8/1 odds). Getting the top call from both the oddsmakers and the model is not hugely surprising. The TOUR leader in Fedex Cup points and 3-time winner this season is also leading the TOUR in Strokes Gained: off-the-Tee, Strokes Gained: Tee-to-Green and Overall Strokes Gained. Oh, and he’s fresh off of a dominating win 2 weeks back and a low-round 64 at the WGC: Bridgestone this past Sunday. He is a threat to win anywhere and everywhere.
Justin Thomas (14-1 odds). No surprise here considering his dominating performance at last week’s WGC: Bridgestone Invitational.
Jason Day (20/1 odds). A two-time winner in 2018 (so far) and already a PGA Champion, putting better than 90% from inside 10 feet this season… just let that sink in.
Jon Rahm (25/1 odds). The results haven’t been there of late, but his game appears to be rounding back into form in perfect time for the final Major of the year and with the Fedex Cup playoffs just around the corner.
Phil Mickelson (100/1 odds). I’m not sure which is more surprising… seeing “Lefty” in our model’s top 5 at the age of 43 or the 5-time Major Champion at such long odds. Either way, he looks like a the value at 100-1.
Tony Finau (40/1 odds). Finishes in Major Championships this season: the Masters (T10), US Open (Solo 5th), Open Championship (T9). At 40-1 he’d be the steal of this year’s Championship if not for Phil.
Kyle Stanley (80/1 odds). The Gig Harbor, Washington native has been playing sneaky good golf this year, including an impressive 2nd place finish last week at the star-studded WGC: Bridgestone. Can he ride the hot-hand into this week’s equally dense field and finish with another top 10?
The rest of the projections include some other surprising results like Tiger outside of the top 25 and Aaron Wise in it. Below is the entire list of all the golfers playing in the PGA Championship and their rank according to the engine.
|32||Rafa Cabrera Bello||150|
|40||Byeong Hun An||125|
|69||Charles Howell III||250|
|116||Ted Potter Jr.||400|
|117||Andrew D. Putnam|
|127||Davis Love III||500|
Odds Courtesy of Bovada
How the Golf Engine makes its picks
In golf, a pro matches up as much with the golf course as another competitor. Which is why any attempt to predict the outcome of a golf tournament must account for the nuances of the course. Analyzing past and present data through the use of math can more accurately project future performance.
In this model, we use machine learning to evaluate 1,500 different statistics for every golfer on the PGA Tour over each tournament since 2004. The analysis of this massive dataset allows gives us an opportunity to predict players that are due to go low.
The machine learns how these statistics can become a unique strength or glaring weakness for each golfer by comparing tens of thousands of different combinations and separating the patterns from the noise. The resulting ‘model’ is able to ‘deep dive’ and determine when to expect low rounds from a pro, given their unique style of play. These calculations are next to impossible to do quickly and certainly without personal and subjective biases, until now.
This is the second tournament the golf engine has been used to predict on the site. Here are its predictions for the Open Championship.
Contributor to National Football Post & sports nut with training in statistics, machine learning, and data analysis from Galvanize – Seattle campus. Alumni of University of Colorado and University of Washington. Occasional boater, skier, and golfer.