| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
Tulane University | Green Wave | 9–0 |
| 2 |  |
Texas A&M University | Aggies | 5–4 |
| 3 |  |
Texas A&M University at Galveston | Sea Aggies | 4–5 |
| 4 |  |
University of Texas | Longhorns | 0–9 |
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 30,300 team race matches sailed before 2026-04-03, using the line-ups that actually raced. It called 12 of 12 matches (100%) with a log-loss of 0.005 (a coin flip scores 0.693), so the event went well for the forecast. Orderliness +0.21 ± 0.82: a typical event, more orderly than 68% of college team race events (temperature of the forecast log-odds that best fits the results, relative to the league average; nothing about the field, venue or wind predicts it, see the Weather tab). 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 | Tulane University Green Wave | 1 | 11.46 school +0.08, home +0.35 | Kelly Holthus / Jonah Wozniak 3.54 Christian Ebbin / Hope Breslin 3.64 Hamilton Barclay / Taylor Bartell 3.83 | 100% | 9–0 (100%) | +0.0 |
| 2 | Texas A&M University Aggies | 2 | 5.50 school −0.09 | Jack Riley / Kate Hennig 1.86 Korbin Bendele / Sydney Krahulik 1.99 Scott Mather / Evelyn Dubois | 49% | 3–3 (50%) | +0.0 |
| 3 | Texas A&M University at Galveston Sea Aggies | 4 | 3.89 school −0.08 | Blake Polsen / Jake Boss 1.14 Ethan Polsen / Colton Oliver 1.22 Maclaren Bell / Matthew Diaz | 0% | 0–3 (0%) | +0.0 |
| 4 | University of Texas Longhorns | 3 | 1.49 school −0.21 | Elizabeth Cleaves / Karina Davis 0.72 Mark Carella / Connor Nihem Dennis Kostjuhin / Reema Nannapaneni 0.61 | 1% | 0–6 (0%) | −0.1 |
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 | Tulane University | 11.46 | 14.46 | 9–0 | 9.0 | ≥+3.00 | 0.0 | 99% |
| 2 | Texas A&M University | 5.50 | 6.46 | 3–3 | 3.0 | +0.97 | 0.0 | 95% |
| 3 | Texas A&M University at Galveston | 3.89 | 0.89 | 0–3 | 0.0 | ≤−3.00 | 0.0 | >99% |
| 4 | University of Texas | 1.49 | -1.51 | 0–6 | 0.1 | ≤−3.00 | 0.1 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Kelly Holthus | Tulane University | 3.54 | 6.54 | 9–0 | 9.0 | ≥+3.00 | 0.0 | 99% |
| Christian Ebbin | Tulane University | 3.64 | 6.64 | 9–0 | 9.0 | ≥+3.00 | 0.0 | 99% |
| Hamilton Barclay | Tulane University | 3.83 | 6.83 | 9–0 | 9.0 | ≥+3.00 | 0.0 | 99% |
| Jack Riley | Texas A&M University | 1.86 | 2.83 | 3–3 | 3.0 | +0.97 | 0.0 | 95% |
| Korbin Bendele | Texas A&M University | 1.99 | 2.95 | 3–3 | 3.0 | +0.97 | 0.0 | 95% |
| Scott Mather | Texas A&M University | 1.66 | 2.63 | 3–3 | 3.0 | +0.97 | 0.0 | 95% |
| Blake Polsen | Texas A&M University at Galveston | 1.14 | -1.86 | 0–3 | 0.0 | ≤−3.00 | 0.0 | >99% |
| Ethan Polsen | Texas A&M University at Galveston | 1.22 | -1.78 | 0–3 | 0.0 | ≤−3.00 | 0.0 | >99% |
| Maclaren Bell | Texas A&M University at Galveston | 1.52 | -1.48 | 0–3 | 0.0 | ≤−3.00 | 0.0 | >99% |
| Elizabeth Cleaves | University of Texas | 0.72 | -2.28 | 0–6 | 0.1 | ≤−3.00 | 0.1 | >99% |
| Mark Carella | University of Texas | 0.21 | -2.79 | 0–6 | 0.1 | ≤−3.00 | 0.1 | >99% |
| Dennis Kostjuhin | University of Texas | 0.61 | -2.38 | 0–6 | 0.1 | ≤−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. | Wave | Aggies | Aggies | Longhorns | |
|---|---|---|---|---|---|---|
| Tulane University Green Wave | 3–0 | ✕ | W | W | W |
| Texas A&M University Aggies | 2–1 | L | ✕ | W | W |
| Texas A&M University at Galveston Sea Aggies | 1–2 | L | L | ✕ | W |
| University of Texas Longhorns | 0–3 | L | L | L | ✕ |
| Team | Rec. | Wave | Aggies | Aggies | Longhorns | |
|---|---|---|---|---|---|---|
| Tulane University Green Wave | 3–0 | ✕ | W | W | W |
| Texas A&M University Aggies | 1–2 | L | ✕ | L | W |
| Texas A&M University at Galveston Sea Aggies | 2–1 | L | W | ✕ | W |
| University of Texas Longhorns | 0–3 | L | L | L | ✕ |
| Team | Rec. | Wave | Aggies | Aggies | Longhorns | |
|---|---|---|---|---|---|---|
| Tulane University Green Wave | 3–0 | ✕ | W | W | W |
| Texas A&M University Aggies | 2–1 | L | ✕ | W | W |
| Texas A&M University at Galveston Sea Aggies | 1–2 | L | L | ✕ | W |
| University of Texas Longhorns | 0–3 | L | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Green Wave | 1-2-3 | def. | |
Sea Aggies | 4-5-6 |
| 2 | |
Aggies | 1-2-6 | def. | |
Longhorns | 3-4-5 |
| 3 | |
Sea Aggies | 1-2-4 | def. | |
Longhorns | 3-5-6 |
| 4 | |
Green Wave | 1-2-3 | def. | |
Aggies | 4-5-6 |
| 5 | |
Green Wave | 1-2-3 | def. | |
Longhorns | 4-5-6 |
| 6 | |
Aggies | 2-3-5 | def. | |
Sea Aggies | 1-4-6 |
| 7 | |
Green Wave | 1-2-3 | def. | |
Sea Aggies | 4-5-6 |
| 8 | |
Aggies | 1-2-4 | def. | |
Longhorns | 3-5-6 |
| 9 | |
Green Wave | 1-2-4 | def. | |
Aggies | 3-5-6 |
| 10 | |
Sea Aggies | 1-2-3 | def. | |
Longhorns | 4-5-6 |
| 11 | |
Sea Aggies | 1-2-3 | def. | |
Aggies | 4-5-6 |
| 12 | |
Green Wave | 1-2-3 | def. | |
Longhorns | 4-5-6 |
| 13 | |
Aggies | 1-2-4 | def. | |
Longhorns | 3-5-6 |
| 14 | |
Green Wave | 1-2-3 | def. | |
Sea Aggies | 4-5-6 |
| 15 | |
Sea Aggies | 1-2-5 | def. | |
Longhorns | 3-4-6 |
| 16 | |
Green Wave | 1-2-3 | def. | |
Aggies | 4-5-6 |
| 17 | |
Aggies | 1-2-5 | def. | |
Sea Aggies | 3-4-6 |
| 18 | |
Green Wave | 1-2-3 | def. | |
Longhorns | 4-5-6 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
Tulane University | Green Wave | Christian Ebbin '28 Hamilton Barclay '27 Kelly Holthus '27 |
Hope Breslin '26 Taylor Bartell '28 Jonah Wozniak '29 |
|
Texas A&M University | Aggies | Jack Riley '29 Korbin Bendele '29 Scott Mather '27 |
Kate Hennig '26 Sydney Krahulik '27 Evelyn Dubois '27 |
|
Texas A&M University at Galveston | Sea Aggies | Blake Polsen '29 Ethan Polsen '27 Maclaren Bell '29 |
Jake Boss '28 Colton Oliver '28 Matthew Diaz '29 |
|
University of Texas | Longhorns | Dennis Kostjuhin '27 Elizabeth Cleaves '29 Mark Carella '27 |
Reema Nannapaneni '27 Karina Davis '29 Connor Nihem '27 |
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.
