Tee to Green, Part 4: The Model
Pulling back the curtain
This is the fourth article in our Tee to Green Series, designed to make professional golf and the stats behind it easier to understand. To view the whole series, click here.
If you’ve read our Pin Sheets, you’ve seen us talk about “Ideal Player Profiles” and highlight the best course fits each week. But what actually goes into building those profiles? And how do we decide who fits? We’ve kept the details quiet to protect our work, but it’s time to give you a real look at what we do at Pinseeker.
The Origin Story
Pinseeker didn’t start with a newsletter in mind. It began as a passion project built around a simple question: How true is the adage “horses for courses” in golf?
In the lead-up to events like the Masters, you’ll hear things like “Augusta is a second-shot course” or “iron play matters most here.” But how true are those narratives? Is SG: APP really that predictive at Augusta? Do certain player types consistently outperform at specific events?
That curiosity kicked off a year-long process of planning, building, testing, and refining a model that could answer those questions. It was finally ready for a test run just in time for the PLAYERS Championship earlier this season. And what we got out of it is a model that doesn’t just predict who might play well, but why certain players tend to thrive at certain courses.
The Model
So, how does it even work? At its core, the model uses historical data to build an ideal player profile, then finds the players in the field who best match it, but that explanation doesn't give the full picture.
We use strokes gained data from the past five years (only four years of data for the 2025 season because of the COVID-affected season in 2020) to look at the top performers for a given event on Tour. From that group of performers, we aggregate their statistics to build our ideal player profile, accounting for consistency and finishing position with proper weighting. The SG numbers you see in our Pin Sheets come from this work and show how players who succeeded at past editions of the event gained on the field.
Why strokes gained? Because it’s the cleanest and most intuitive way to compare player performance relative to each other, and we’re able to see not just who’s performing well but why they’re performing, thanks to the individual categories.
The Best Fits
Of course, that’s only half of what the Pinseeker Model does. Once we’ve built the ideal profile, we need to find matches in this year’s field.
To answer that, we use each player’s current-season strokes gained data and run it through a few different similarity measures. We’re not just comparing raw numbers, we’re looking at profile shape, too.
Profile shape refers to the distribution of a player’s strengths. Imagine an ideal profile that gains +0.8 SG: APP and +0.2 SG: ARG. Now imagine a player who flips that: a +0.2 SG: APP and +0.8 SG: ARG. On paper, the total difference might look similar. But stylistically, that player doesn’t match our blueprint. Our model picks up on that, penalizing players whose games differ from the shape of the ideal. In other words, we’re not just finding who’s hot, we’re finding who’s built to succeed on this course.
Final Thoughts
Like any model, we’re not perfect. You can’t account for weather, course changes, or the general unpredictability of golf. And we’re constantly working on it, trying to make it as accurate as possible. But it gives us a foundation: one driven by data, which could make all the difference.
Now that you know what’s going on, why not take a look at this week’s Pin Sheet with a new understanding of how we generate these picks? As always, thanks for reading, subscribe if you’re new to Pinseeker, and see you next time.