Course History Model
As many of you already know, I hate course history. The sample size for each player is too small to get a reliable sense of how a player fits at the course, or whether that fit even matters. However, what if we could see how numerous players perform at a given course in order to dramatically increase the sample size? Thanks to DataGolf’s Historic Event Data, we can.
To begin, I take the top 25 and bottom 25 golfers in CH Index (DG defines CH Index as “the average (adj. for field strength) strokes-gained at the course”). Next, I regress the CH Index of each player on their strokes gained profiles, bogey avoidance, and birdie or better percentage to find out which stats best translate into success at Augusta. This strategy makes the model predictive, whereas most course breakdowns are inherently more descriptive. The difference is simple; predictive tells us what will happen while descriptive tells us what did happen. DFS is all about the former. Put another way, what use does knowing iron play plays a key role if you can’t predict whose irons will be on that week? The CH Model combines what will be important with what we can best predict.
Augusta Key Stats
While many courses have shown a dominantly predictive stat, Augusta’s CH Model is pretty evenly balanced. We have five statistically significant metrics this week; all four SG stats are highly significant, as is bogey avoidance.
- SGOTT – 24%
- SGAPP – 18%
- SGATG – 23%
- SGP – 20%
- Bogey Avoidance – 15%
There’s one other key stat here: experience. DataGolf did a tremendous breakdown on the true effect of experience at Augusta. I highly recommend reading the whole thing, but here’s the tl;dr version:
The shaded red is a penalty for lack of experience while shaded green is a boost for experience. Consequently, I’m not really penalizing any of the first timers, but I will be on the lookout for underowned players with lots of Masters experience in tomorrow’s GPP Plays article, as the advantage gained from experience far outweighs the penalty for lack thereof.
I’m only seeing two plays who jump off the page as wildly underpriced. Thanks to the smaller field, I’m not seeing anyone that’s significantly underowned just yet. Consequently, it’s a small core…
Tommy has been a core play for me everytime he’s teed it up this season. He’s consistently been too cheap and this week is no different. His inability to close thus far has fortunately kept his price (and probably his ownership) right where we want it. As the tenth most expensive player in the field, here are Tommy’s ranks in each model:
- CH Model – 4th
- Recent Form Model – 7th
- Top 10 Odds – 6th
- Earnings Projection – 4th
- Made Cut Odds – 4th
He is essentially tied for fourth in my rankings with Justin Thomas. Too cheap!
Cantlay is another guy that we at FTA have been higher on than a lot of the industry, including DK. Let’s jump right into his rankings in each model, as they speak for themselves.
- CH Model – 12th
- Recent Form Model – 19th
- Top 10 Odds – 10th
- Earnings Projection – 10th
- Made Cut Odds – 6th (!)
At just $7700, Cantlay is a top ten player in my rankings (exactly 10th).
Both of these core plays will carry their fair share of ownership, but at an average cost of $8450, they give us tremendous flexibility with the rest of our lineups.
Other Plays Who Pop in the CH Model
Relative to their price, the CH Model also loves:
- Dustin Johnson – 1st
- Hideki Matsuyama – 10th
- Tony Finau – 14th
- Matt Kuchar – 11th
- Webb Simpson – 8th
- Charles Howell III – 7th
Find me on Twitter @alexblickle1, and my GPP article will be out tomorrow afternoon.