Ranking college sailors with a model that actually gets validated
Existing sailing rankings are mostly eyeballed win totals. This project fits a real statistical model — a temporal Plackett–Luce ranking — to 600k+ scraped race results, and checks it against races it never saw.
This week's predictions
Live output from the model above — win probability from 5,000 Monte Carlo race simulations per regatta, built on the predicted lineups. Check back after results post to see how it did.
- 1Brown University18% win
- 2Roger Williams University15% win
- 3Dartmouth College13% win
- 4Harvard University12% win
- 5Yale University10% win
- 6Boston College6% win
See predicted lineups — not just who wins, but who sails — or full simulated standings for every regatta.
What the model produces
Real output from the live model — weekly top‑3‑sailor average rating for four ICSA programs, June 2024–April 2026. Ratings drift gradually week to week instead of swinging on a single regatta, because that drift is exactly what the model's smoothness prior constrains.
How it works
Every sailor gets a latent skill value θ[sailor, week] for every ISO week they've been active — a full trajectory, not one static number. It's fit by maximum likelihood under the Plackett–Luce distribution (the standard way to model a full finishing order, not just win/loss), with an L2 anchor plus log–cosh penalties on week‑to‑week velocity and acceleration so skill can drift realistically instead of chasing noise — trained in PyTorch on GPU, batched by fleet size, over roughly 13.7 million parameters.
A second tool, the Boat Speed Analyzer, is built on the same data: it compares a school's fleet-race performance across two time windows and flags whether the difference is statistically significant, so a real speed change isn't confused with normal race-to-race noise.
Validated, not just fit
The model is trained on 100,000+ races across 24,000+ sailors, and checked against a random 1-in-20 sample of regattas held out before fitting — races it never saw during training.
Why I built this
- College sailing rankings are usually eyeballed or based on raw win totals — I wanted something that could actually be validated against held-out results.
- As Club Captain of Brown Sailing, I use this data to make real decisions about lineups and scheduling, not just as a portfolio piece.
- It's the project that convinced me I want to work somewhere that rewards building real statistical models against real data.