Course Profiles / Sea Island Golf Club (Seaside)
Course Profile

Sea Island Golf Club (Seaside):
what wins here.

The RSM Classic, The RSM Classic #2 · 7 editions, 2019–2025 · 2,084 rounds analysed

Course-fit weights
OTT 24% · less decisive
APP 26% · rewarded
ARG 26% · rewarded
PUTT 24% · less decisive
Derived by regressing finishing position on strokes gained at this course, measured against the tour-wide baseline, then tested on seasons the model had never seen. Based on 583 player-events.

What the data says

Across 7 editions (2019–2025), approach play is the category that most separates players who contend here from those who do not — 26% of the model's weighting, against 24% for putting.

The spread is narrow, which is itself the finding: this course does not strongly favour one kind of player. Weights this close to even mean the field sorts mostly on overall quality, and any course-fit edge here is small.

The winners tell a partly separate story: in 4 of the last 6, the champion's best category for the week was putting. That differs from what the model weights, and the gap is instructive — winning weeks are outlier weeks, usually powered by one skill running hot on top of the baseline the course demands.

How the winners won

Strokes gained per round for every champion here since 2019. The green figure is that winner's best category for the week.

YearWinnerTo ParOTTAPPARGPUTTTotal
2025Sami Valimaki-17+0.58+0.76+0.42+1.69+3.45
2024Maverick McNealy-14+0.78+1.26+0.25+1.23+3.52
2023Ludvig Aberg-24+1.63+0.80+0.54+2.14+5.10
2022Adam Svensson-20+0.94+0.84+0.29+3.05+5.12
2021Talor Gooch-14-0.18+2.09+0.96+1.68+4.55
2020Robert Streb-10+0.17+0.12+0.17+1.50+1.96
Method. Weights come from regressing top-20 finishes on players' strokes gained in each category, using only rounds played before each event, and comparing each course against the tour-wide baseline — so a weight measures what this course rewards more than golf generally does, not simply which skills help. Weights are kept only where they predicted results out of sample; where they did not, we say so. We tested our previous method, found it did not predict, and published that too: how the model works.

This week's field, ranked.

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