For years, I’ve made fantasy golf picks, power rankings and given betting tips about PGA Tour events.
I’ve looked at two main factors, current form and course history, and tried to bring those together to offer selections and my best guess as to who will contend in a given week.
However, for 2020, I wanted to become more sophisticated and develop a weekly rubric which offers a clear-cut ranking system based on data points weighted in a formula. After some weeks of testing, I’m have this rubric where I want it. Let me walk you through the rubric’s tenets and show off this week’s results.
[s2If !is_user_logged_in()]
FOR GNN PLUS MEMBERS ONLY!
For $5 per month or just $50 per year, GNN Plus members get access to our in-depth weekly DraftKings picks, searchable database of PGA Tour results from 2011-present and top-15 PGA Tour trends.
You’ll also have access to our course fit modeling, Quality Stokes Gained data, course demand insights, grass-specific performance and further tools, as well individual access to fantasy expert Ryan Ballengee for your unique questions.
ADDITIONAL TOOLS INCLUDE
-
- Searchable PGA Tour results
- PGA Tour top-15 finish trends
- Player-course fit modeling
- Course demands breakdown
- Quality Strokes Gained data
To access this research hidden below and MANY more fantasy golf tools, sign up for GNN+!
[/s2If] [s2If is_user_logged_in()]How the rubric works
The reason I’m calling this a rubric is because I think a model implies a guess about how the tournament will play out. I don’t think any model can do that. There are plenty of factors data can’t quantify, including luck of the draw, playing partners, hole locations, weather and sheer random stuff.
That’s why the rubric is rooted in things we can quantify more broadly without getting too into the weeds. I don’t believe specific statistics matter for each course. Each player is different, and they achieve their best results slightly differently, as our Course Fit tool indicates. A player can perform well on most PGA Tour courses doing things their way, and their past performance on a course is best indicative of their fit, not where they rank in certain categories.
That said, the biggest chunk of my rubric relies on strokes gained, as well our derivative, Quality Strokes Gained, which weights a player’s strokes gained against the depth of field they face. The rubric looks at this data over the longer term and medium term to derive a player’s quality across the tour and across different fields.
Next, the rubric accounts for two factors I look at every week: current form in the last five PGA Tour events played and their average strokes gained on the host course in the last three years.
All told, the model is designed to point out quality players and boost those middling players who have good current form or good course history.
2020 RBC Heritage rankings
You’ll see with the rubric that I’ve listed the top 50 in this field, as well their current betting odds and DraftKings price.
We’re looking for quality, of course, but also for some value.
This week, the ranking again feels pretty apparent. Rory is the best in the world by far. Bryson DeChambeau is moving up the rankings quickly. Gary Woodland is a good value play. Same with Brandt Snedeker.
There are some values out there, and fortunately the model relies very little on form anyhow, so form basically being a 3-month-old concept isn’t a big deal here.
Click header to sort; the better their position, the more the rubric likes them
POS | PLAYER | PTS | DK PRICE | DK RANK | ODDS | ODDS RANK |
---|---|---|---|---|---|---|
1 | McIlroy, Rory | 2.146 | 11300 | 1 | 1200 | 1 |
2 | DeChambeau, Bryson | 1.669 | 10700 | 3 | 1400 | 2 |
3 | Rahm, Jon | 1.664 | 10500 | 4 | 1600 | 4 |
4 | Thomas, Justin | 1.551 | 10900 | 2 | 1400 | 2 |
5 | Matsuyama, Hideki | 1.396 | 9500 | 8 | 3000 | 8 |
6 | Woodland, Gary | 1.372 | 8400 | 16 | 4000 | 13 |
7 | Simpson, Webb | 1.321 | 9000 | 10 | 2500 | 7 |
8 | Rose, Justin | 1.29 | 9200 | 9 | 3000 | 8 |
9 | Schauffele, Xander | 1.237 | 10200 | 5 | 2000 | 5 |
10 | Morikawa, Collin | 1.195 | 10000 | 6 | 2000 | 5 |
11 | Reed, Patrick | 1.065 | 8800 | 12 | 3000 | 8 |
12 | Johnson, Dustin | 1.043 | 8500 | 15 | 4000 | 13 |
13 | Berger, Daniel | 1.01 | 8900 | 11 | 5000 | 17 |
14 | Finau, Tony | 0.956 | 8200 | 18 | 5000 | 17 |
15 | Snedeker, Brandt | 0.916 | 7500 | 33 | 10000 | 35 |
16 | Im, Sungjae | 0.912 | 9700 | 7 | 3000 | 8 |
17 | Fowler, Rickie | 0.908 | 8100 | 19 | 5000 | 17 |
18 | Koepka, Brooks | 0.878 | 8600 | 14 | 4000 | 13 |
19 | Poulter, Ian | 0.865 | 7600 | 30 | 8000 | 28 |
20 | Dahmen, Joel | 0.829 | 7500 | 33 | 8000 | 28 |
21 | Bezuidenhout, Christiaan | 0.821 | 7200 | 47 | 15000 | 49 |
22 | McNealy, Maverick | 0.819 | 7100 | 53 | 15000 | 49 |
23 | NeSmith, Matthew | 0.814 | 6600 | 88 | 20000 | 65 |
24 | Horschel, Billy | 0.792 | 7700 | 27 | 8000 | 28 |
25 | Kuchar, Matt | 0.767 | 8300 | 17 | 4000 | 13 |
26 | Hatton, Tyrrell | 0.76 | 8000 | 20 | 5000 | 17 |
27 | Lowry, Shane | 0.753 | 7800 | 24 | 8000 | 28 |
28 | Cauley, Bud | 0.69 | 7200 | 47 | 12500 | 45 |
29 | Fitzpatrick, Matthew | 0.687 | 7900 | 22 | 6000 | 23 |
30 | Van Rooyen, Erik | 0.678 | 6600 | 88 | 20000 | 65 |
31 | Homa, Max | 0.672 | 7000 | 60 | 15000 | 49 |
32 | Hovland, Viktor | 0.651 | 7600 | 30 | 6000 | 23 |
33 | Scheffler, Scottie | 0.643 | 7800 | 24 | 6000 | 23 |
34 | Sabbatini, Rory | 0.64 | 7200 | 47 | 12500 | 45 |
35 | Wallace, Matt | 0.619 | 6800 | 74 | 20000 | 65 |
36 | Hadwin, Adam | 0.568 | 7300 | 42 | 12500 | 45 |
37 | Henley, Russell | 0.544 | 7100 | 53 | 20000 | 65 |
38 | Watson, Bubba | 0.543 | 7400 | 37 | 10000 | 35 |
39 | Higgs, Harry | 0.54 | 6900 | 67 | 20000 | 65 |
40 | Ancer, Abraham | 0.531 | 8000 | 20 | 6000 | 23 |
41 | English, Harris | 0.526 | 7300 | 42 | 10000 | 35 |
42 | Day, Jason | 0.525 | 7600 | 30 | 200000 | 152 |
43 | Spieth, Jordan | 0.516 | 8700 | 13 | 3000 | 8 |
44 | Moore, Ryan | 0.512 | 6900 | 67 | 15000 | 49 |
45 | Streelman, Kevin | 0.507 | 7000 | 60 | 20000 | 65 |
46 | Niemann, Joaquin | 0.503 | 7400 | 37 | 10000 | 35 |
47 | Furyk, Jim | 0.489 | 7100 | 53 | 15000 | 49 |
48 | Varner III, Harold | 0.478 | 7400 | 37 | 15000 | 49 |
49 | Lee, Danny | 0.469 | 7000 | 60 | 20000 | 65 |
50 | Kokrak, Jason | 0.45 | 7900 | 22 | 6000 | 23 |