launch
A guided tour of the Rowing Power Index
Welcome. If this is your first visit, the homepage is the Starting 10, but it isn't the whole 1500 meters. The site has a few dozen pages, all built on the same model — a hierarchical Bayesian fit to…
Apr 29, 2026·Read full analysis →
rankings
The British rankings, and a preview of National Schools'
rankings
The Power 10: Women's V8+ and 2V8+, after Stotesbury
rankings
The Power 10: Men's V8+ and 2V8+, after Stotesbury
analysis
Stotesbury 2026: St. Joseph's Prep owns the Schuylkill, Montclair owns the weekend's story
rankings
The Power 10: Women's 2V8+, entering championship season
Recent ResultsAll regattas →
womens 2v8May 24
MPSRA Spring Championship
Petite Final
| 1 | 6:08.340 |
| 2 | 6:24.671 |
womens 2v8May 24
MPSRA Spring Championship
Grand Final
| 1 | 5:55.741 |
| 2 | 5:57.643 |
| 3 | 6:03.339 |
| 4 | 6:07.131 |
| 5 | 6:14.537 |
womens 2v8May 24
MPSRA Spring Championship
| 1 | 5:48.983 |
| 2 | 5:53.098 |
| 3 | 5:53.502 |
| 4 | 5:54.047 |
| 5 | 5:59.762 |
womens 2v8May 24
National Schools Regatta
| 1 | 6:46.70 |
| 2 | 6:49.17 |
| 3 | 7:13.11 |
| 4 | 7:21.16 |
mens 2v8May 24
MPSRA Spring Championship
Grand Final
| 1 | 5:03.847 |
| 2 | 5:08.546 |
| 3 | 5:08.636 |
| 4 | 5:21.765 |
| 5 | 5:24.154 |
mens 2v8May 24
National Schools Regatta
| 1 | 6:00.08 |
| 2 | 6:04.75 |
| 3 | 6:05.05 |
| 4 | 6:05.72 |
| 5 | 6:10.33 |
mens 2v8May 24
National Schools Regatta
| 1 | 6:07.61 |
| 2 | 6:09.08 |
| 3 | 6:15.97 |
| 4 | 6:22.23 |
| 5 | 6:30.54 |
W V8+May 24
MPSRA Spring Championship
Grand Final
| 1 | 5:33.306 |
| 2 | 5:34.535 |
| 3 | 5:41.989 |
| 4 | 5:44.803 |
| 5 | 5:45.846 |
How RPI Works
Every rating is a full probability distribution, not a single number — and every race updates all of them at once.
The Algorithm
1
Every rating is a best guess with an uncertainty band
Each crew has a most-likely speed plus a credible interval— the range we're 90% confident covers the truth. Crews with many recent races have tight intervals; new crews start wide and tighten as data accumulates.
Wayland-Weston 1713 · 90% CI 1669–1756
2
Win probability comes from comparing two distributions
When two crews race, we compare their full posterior distributions — not just the means. A wide gap between curves means a confident pick; overlapping curves mean a coin-flip race.
Wayland-Weston 1713 vs Duxbury 1640
Predicted: 95% chance for Wayland-Weston
Margin: -0.1 to +8.8 sec (90% interval at 1500m)
Posterior comparison
3
All races inform every rating at once
Rather than nudging one rating after each race, the model fits every team's skill jointly using hierarchical pooling. Beating a strong-league crew counts more — automatically — because each league's overall strength is itself learned from the races within it.
One race result → every team's posterior shifts
League means recalibrate jointly with team ratings
No K-factor — points per race emerge from the data
Accuracy & Calibration
90%
Calls correct
1,600+
H2H pairs tested
PredictedActual win rate
50–55%67%
55–60%65%
60–65%82%
65–70%78%
70–80%86%
80–90%89%
90–100%95%
When the model predicts X%, teams win ~X% of the time.
Measured on the last ~3 months of real races against the live Bayesian engine's current posterior.
Measured on the last ~3 months of real races against the live Bayesian engine's current posterior.