| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
Rice University | Owls | 5–1 |
| 2 |  |
University of Texas | Longhorns | 4–2 |
| 3 |  |
University of Central Oklahoma | Bronchos | 0–6 |
Each side is the sum of its three boats (the skipper's fleet rating times 0.72 plus a team race offset learned from their past matches, plus the crew's offset and a small experience term) plus the school's team race strength and a home-water edge for the host. The win probability is a logistic function of the difference. The forecast was made before the event: the model was refitted on every match sailed before the first day, with fleet ratings as of the week before.
Fitted on the 23,468 team race matches sailed before 2024-03-30, using the line-ups that actually raced. It called 7 of 9 matches (78%) with a log-loss of 0.305 (a coin flip scores 0.693), so the event went well for the forecast. vs forecast is a team's wins minus the wins the model expected from the matches it sailed: positive means it beat its rating.
| Place | School | Pred. | Strength | Boats (skipper / crew, boat value) | Pred. win % | Record | vs forecast |
|---|---|---|---|---|---|---|---|
| 1 | Rice University Owls | 2 | 2.26 school −0.54 | Ricky Miller / Aidan Murphy 1.62 Leo Marek / Taylor Griffeth 0.46 Arthur Unger / Ahitagni Das 0.67 | 65% | 5–1 (83%) | +1.1 |
| 2 | University of Texas Longhorns | 1 | 3.06 school +0.06, home +0.41 | Rohit Rajan / Dennis Kostjuhin 0.36 Oliver Fenner / Sophia Herrada 0.69 Reese Zebrowski / Reema Nannapaneni 1.46 | 84% | 4–2 (67%) | −1.1 |
| 3 | University of Central Oklahoma Bronchos | 3 | -2.39 school −0.05 | Caleb Vanderburg / Peyton Miller -0.91 Makenzie Benedict / Natalya Simmons-Elliot Ana Savva Garcia / Tristan Danielson | 1% | 0–6 (0%) | −0.0 |
For each side, the performance strength is the value that would have made its record least surprising: the strength at which the model's expected wins over exactly these opponents equal the actual wins, with every opponent fixed at its pre-event strength. For a skipper, the same shift is applied only to their boat in the matches they sailed, with team-mates and opponents fixed; team-mates who sailed every match together therefore get the same number. Surprise is in bits: -log2 of the probability of the record at the pre-event strength minus the same at the performance strength. P(this good or better) treats matches as independent coin flips at the model's pre-event probabilities, so it ignores form within the day; read it as a rough guide.
Pre-event strength against the strength that would have made each record least surprising, everything else fixed. Δ is in the model's logit units, the same scale as the boat values above (a side is three boats plus the school term); green is a clear over-performance, red a clear under-performance. Skippers need three or more matches.
| Place | School | Strength | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| 1 | Rice University | 2.26 | 3.77 | 5–1 | 3.9 | +1.51 | 1.2 | 22% |
| 2 | University of Texas | 3.06 | 1.64 | 4–2 | 5.1 | −1.42 | 1.1 | 97% |
| 3 | University of Central Oklahoma | -2.39 | -5.39 | 0–6 | 0.0 | ≤−3.00 | 0.1 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Arthur Unger | Rice University | 0.67 | 2.19 | 5–1 | 3.9 | +1.51 | 1.2 | 22% |
| Oliver Fenner | University of Texas | 0.69 | -0.73 | 4–2 | 5.1 | −1.42 | 1.1 | 97% |
| Reese Zebrowski | University of Texas | 1.46 | 0.04 | 4–2 | 5.1 | −1.42 | 1.1 | 97% |
| Caleb Vanderburg | University of Central Oklahoma | -0.91 | -3.91 | 0–6 | 0.0 | ≤−3.00 | 0.1 | >99% |
| Makenzie Benedict | University of Central Oklahoma | -1.08 | -4.08 | 0–6 | 0.0 | ≤−3.00 | 0.1 | >99% |
| Ana Savva Garcia | University of Central Oklahoma | -0.60 | -3.60 | 0–6 | 0.0 | ≤−3.00 | 0.1 | >99% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | Owls | Longhorns | Bronchos | |
|---|---|---|---|---|---|
| Rice University Owls | 2–0 | ✕ | W | W |
| University of Texas Longhorns | 1–1 | L | ✕ | W |
| University of Central Oklahoma Bronchos | 0–2 | L | L | ✕ |
| Team | Rec. | Owls | Longhorns | Bronchos | |
|---|---|---|---|---|---|
| Rice University Owls | 2–0 | ✕ | W | W |
| University of Texas Longhorns | 1–1 | L | ✕ | W |
| University of Central Oklahoma Bronchos | 0–2 | L | L | ✕ |
| Team | Rec. | Owls | Longhorns | Bronchos | |
|---|---|---|---|---|---|
| Rice University Owls | 1–1 | ✕ | L | W |
| University of Texas Longhorns | 2–0 | W | ✕ | W |
| University of Central Oklahoma Bronchos | 0–2 | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Owls | 1-2-3 | def. | |
Bronchos | 4-5-6 |
| 2 | |
Longhorns | 1-2-3 | def. | |
Bronchos | 4-5-6 |
| 3 | |
Owls | 1-2-5 | def. | |
Longhorns | 3-4-6 |
| 4 | |
Owls | 1-2-3 | def. | |
Bronchos | 4-5-6 |
| 5 | |
Longhorns | 1-2-3 | def. | |
Bronchos | 4-5-6 |
| 6 | |
Owls | 1-2-6 | def. | |
Longhorns | 3-4-5 |
| 7 | |
Owls | 1-2-3 | def. | |
Bronchos | 4-5-6 |
| 8 | |
Longhorns | 1-2-5 | def. | |
Bronchos | 3-4-6 |
| 9 | |
Longhorns | 1-2-6 | def. | |
Owls | 3-4-5 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
Rice University | Owls | Arthur Unger '25 Leo Marek '26 Ricky Miller '26 |
Ahitagni Das '27 Taylor Griffeth '25 Aidan Murphy '25 |
|
University of Texas | Longhorns | Oliver Fenner '26 Reese Zebrowski '26 Rohit Rajan '26 |
Sophia Herrada '24 Reema Nannapaneni '27 Dennis Kostjuhin '27 |
|
University of Central Oklahoma | Bronchos | Ana Savva Garcia '25 Caleb Vanderburg '24 Makenzie Benedict '26 |
Tristan Danielson '23 Peyton Miller '24 Natalya Simmons-Elliot '26 |
Orderliness is how tightly a fleet race’s finishing order follows the pre-regatta ratings, relative to a typical event (0): positive is the favourites’ day, negative the underdogs’. A value of ±0.07 means a rating gap counts about 6% more than on a typical day. This event is not a fleet race the rating model scores, so there is no actual value for it: this is what this venue’s fleet racing history says about the breeze on the day, read off the chart at the star. How this is estimated · compare venues.
Short write-ups of what the ratings and results show, with the numbers and how they were computed.
