Course Fit 2.0?
In the version of course fit currently used in our model, we make adjustments based on which types of players have historically performed well at each course. Player "types" are characterized by five attributes: distance, accuracy, approach skill, around-the-green skill, and putting skill. When looking at our course fit profiles, it’s clear that distance and accuracy are the attributes that drive most of the differences across courses, which makes sense when you think of courses like Bethpage Black and Harbour Town.

However we’ve always thought that looking at whether a course favours “good” approach players or “good” putters is a bit too simplistic. For example, some courses require more wedge shots, and so even if they don’t favour better iron players overall, they might favour good wedge players. The same logic applies to around-the-green shots and putting. This provides the basic motivation behind this post: by estimating player skills in more narrowly-defined bins and combining that with the expected number of shots from each bin, we can get a different type of course fit estimate. Of course plenty of people already think about course fit in this way, but it’s useful to try to put some numbers down on paper.

Getting Fitted
The first thing needed for this analysis are skill levels by bin. The method here is fairly simple and consists of two steps: 1) get time-weighted averages of a player’s performance in each bin, and 2) use these weighted averages plus our broad measures of skill to predict performance in that bin. This second step is important: when estimating a player's skill from 50-100 yards, for example, we use past performance in that bin, as well their performance on shots from 100-150 yards and their overall around-the-green skill. This is a bit unsatisfying in that it makes it less likely to get distinctive skill profiles (e.g. a player who is great from 50-100 yards but terrible from 150-200), but the reality is that good approach players tend to be pretty good at all types of approach shots. However, two bins that did stand out as having a lot of predictive power on their own were short putting and bunker play.

The plots below show our shot-level skill estimates for every player in the Masters field with sufficient data. We've included driving for completeness, but that won't factor into the course fit analysis here. The broad skill categories are measured in SG per round, while the narrow categories for APP, ARG, and Putting are in units of SG per shot.

Note that the methodology used here differs slightly from the one currently used on our site to generate the approach skill radar plots on the profile pages. They’ll be updated in the near future.

The second thing we need for our course fit estimates are the expected shots in each bin at Augusta National. The table below shows the average number of shots hit from each bin from 2021-2023 (we exclude the ceremonial players at the Masters) along with PGA Tour course averages. For approach shots we remove recovery shots and layups, and also do not count "2nd" approaches (e.g. after a penalty drop). For putts, anything under 2 feet is excluded. When we actually do our fit estimates, we’ll allow the expected shots to vary based on a player’s skill profile—e.g. Bryson is expected to have shorter approach shots due to his distance off the tee—which really only makes a meaningful difference in the approach bins.

Bingo
Now we just have to put these two pieces of information together. A player will have good course fit when they are hitting more shots—compared to an average course—from locations where they have a skill advantage.

Here are a few things to think about:
1. Courses where more shots are hit (i.e. harder courses) will favour better players.
2. When swapping shots (e.g. a course that has 2 fewer wedge shots and 2 more long-iron shots than the average), it is a player's relative skill advantage that matters (e.g. their long iron skill relative to their wedge skill).
3. Some bins have more skill separation, e.g. 200+ vs. 100-150 approach shots. Therefore, all else equal, better approach players will have a bigger advantage at courses with more 200+ yard shots.

Based on the shot distributions above, we should expect players this week to be hitting roughly 2 more long(ish) irons and 1-2 fewer wedge shots, 2 more pitch shots from the fairway and 1-2 fewer from the rough, and 1-2 more putts than at a typical PGA Tour course. This should favour players who are (relatively) good long iron players with good short games.

Before giving the full list of adjustments, let's walk though an example using Xander Schauffele:

The (Not So) Big Reveal
The final adjustments for each player by category are listed in the table below. As you can see, they are pretty small. On average the adjustments are positive because the expected shot distribution at Augusta favours better players, and you have to be pretty good to get in the field this week. The largest approach adjustment belongs to Gary Woodland (+0.056), who is elite with his long irons but mediocre-to-poor with his wedges. The largest around-the-green adjustment is Justin Thomas' (+0.067), due to his exceptional short game (particularly from the short grass). The largest putting adjustment (+0.031) belongs to Denny McCarthy due to his all-around great putting ability.

An example might help with thinking about how large we could reasonably expect these estimates to be: if a course had 2 more 100-150 yard shots and 2 fewer 200+ yard shots, and a player was in the top 5% of wedge players but the bottom 5% of long iron players, their course fit adjustment from the swapping of these two shots would be about 0.13 strokes. This is a meaningful adjustment, but the problem is that this type of player doesn't really exist, or at least the data won't let us believe they exist. As was alluded to above, a player's overall approach skill (or ARG skill, or putting skill) is a good predictor of their performance in any approach bin; as a result, players' estimated bin skills are pulled towards their overall skill, which makes it hard to find a player like the one described above.

Another reason these adjustments are small is that shot distributions across PGA Tour courses don't differ that much—for the approach bins, the most extreme course usually has 2 or 3 extra shots in that bin compared to the average course. A final contributing factor to the adjustment size, which is specific to Augusta National, is that the correlation between the approach and ARG fit adjustments is negative; Augusta favours players who are relatively good with their long irons and poor with their wedges, but these players tend to be worse around the greens (which Augusta penalizes).