| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
Texas A&M University | Aggies 1 | 3–0 |
| 2 |  |
Rice University | Owls | 2–1 |
| 3 |  |
University of Texas | Longhorns | 1–2 |
| 4 | |
Texas A&M University | Aggies 2 | 0–3 |
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 28,104 team race matches sailed before 2026-01-31, using the line-ups that actually raced. It called 6 of 6 matches (100%) with a log-loss of 0.271 (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 | Texas A&M University Aggies 1 | 1 | 3.96 | Kate Hennig / Evelyn Dubois 1.07 Sofia Pando / Menya Bird Korbin Bendele / Sydney Krahulik | 76% | 3–0 (100%) | +0.7 |
| 2 | Rice University Owls | 2 | 3.59 school −0.30 | Joe Slipper / Leo Marek 1.74 Adeline Thompson / Lindsey Frost Lewis Forrest / Naayaa Mehta | 67% | 2–1 (67%) | −0.0 |
| 3 | University of Texas Longhorns | 3 | 2.71 school −0.17, home +0.36 | Elizabeth Cleaves / Mark Carella Logan Downey / Luka Vukmirica Dennis Kostjuhin / Reema Nannapaneni | 42% | 1–2 (33%) | −0.3 |
| 4 | Texas A&M University Aggies 2 | 4 | 1.55 | Zachary Aronson / Charlotte Heaney 0.21 Canaan Cortes / Elisabeth Ritenour Jack Riley / Jeffrey Souto | 15% | 0–3 (0%) | −0.4 |
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 | Texas A&M University Aggies 1 | 3.96 | 6.96 | 3–0 | 2.3 | ≥+3.00 | 1.2 | 42% |
| 2 | Rice University Owls | 3.59 | 3.57 | 2–1 | 2.0 | −0.02 | 0.0 | 77% |
| 3 | University of Texas Longhorns | 2.71 | 2.20 | 1–2 | 1.3 | −0.51 | 0.1 | 87% |
| 4 | Texas A&M University Aggies 2 | 1.55 | -1.45 | 0–3 | 0.4 | ≤−3.00 | 0.7 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Kate Hennig | Texas A&M University | 1.07 | 4.07 | 3–0 | 2.3 | ≥+3.00 | 1.2 | 42% |
| Sofia Pando | Texas A&M University | 0.92 | 3.92 | 3–0 | 2.3 | ≥+3.00 | 1.2 | 42% |
| Korbin Bendele | Texas A&M University | 1.96 | 4.96 | 3–0 | 2.3 | ≥+3.00 | 1.2 | 42% |
| Joe Slipper | Rice University | 1.74 | 1.72 | 2–1 | 2.0 | −0.02 | 0.0 | 77% |
| Adeline Thompson | Rice University | 1.38 | 1.36 | 2–1 | 2.0 | −0.02 | 0.0 | 77% |
| Lewis Forrest | Rice University | 0.69 | 0.67 | 2–1 | 2.0 | −0.02 | 0.0 | 77% |
| Elizabeth Cleaves | University of Texas | 0.55 | 0.04 | 1–2 | 1.3 | −0.51 | 0.1 | 87% |
| Logan Downey | University of Texas | 1.28 | 0.77 | 1–2 | 1.3 | −0.51 | 0.1 | 87% |
| Dennis Kostjuhin | University of Texas | 0.61 | 0.10 | 1–2 | 1.3 | −0.51 | 0.1 | 87% |
| Zachary Aronson | Texas A&M University | 0.21 | -2.79 | 0–3 | 0.4 | ≤−3.00 | 0.7 | >99% |
| Canaan Cortes | Texas A&M University | -0.48 | -3.48 | 0–3 | 0.4 | ≤−3.00 | 0.7 | >99% |
| Jack Riley | Texas A&M University | 1.75 | -1.25 | 0–3 | 0.4 | ≤−3.00 | 0.7 | >99% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Rd | # | Winner | Loser | P(winner) | |
|---|---|---|---|---|---|
| 1 | 1 | Texas A&M University Aggies 1 | Texas A&M University Aggies 2 | 92% | |
| 1 | 2 | Rice University Owls | University of Texas Longhorns | 71% | |
| 1 | 3 | Texas A&M University Aggies 1 | University of Texas Longhorns | 78% | |
| 1 | 4 | Rice University Owls | Texas A&M University Aggies 2 | 88% | |
| 1 | 5 | University of Texas Longhorns | Texas A&M University Aggies 2 | 76% | |
| 1 | 6 | Texas A&M University Aggies 1 | Rice University Owls | 58% |
| Team | Rec. | 1 | Owls | Longhorns | 2 | |
|---|---|---|---|---|---|---|
| Texas A&M University Aggies 1 | 3–3 | ✕ | W | L | ✕ |
| Rice University Owls | 2–1 | L | ✕ | W | L |
| University of Texas Longhorns | 1–2 | W | L | ✕ | W |
| Texas A&M University Aggies 2 | 3–3 | ✕ | W | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Aggies 1 | 1-2-6 | def. | |
Aggies 2 | 3-4-5 |
| 2 | |
Owls | 1-2-5 | def. | |
Longhorns | 3-4-6 |
| 3 | |
Aggies 1 | 2-3-4 | def. | |
Longhorns | 1-5-6 |
| 4 | |
Owls | 1-2-5 | def. | |
Aggies 2 | 3-4-6 |
| 5 | |
Longhorns | 1-2-6 | def. | |
Aggies 2 | 3-4-5 |
| 6 | |
Aggies 1 | 1-2-6 | def. | |
Owls | 3-4-5 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
Texas A&M University | Aggies 1 | Kate Hennig '26 Korbin Bendele '29 Sofia Pando '29 |
Evelyn Dubois '27 Sydney Krahulik '27 Menya Bird '26 |
|
Rice University | Owls | Adeline Thompson '29 Joe Slipper '28 Lewis Forrest '29 |
Lindsey Frost '26 Leo Marek '26 Naayaa Mehta '27 |
|
University of Texas | Longhorns | Dennis Kostjuhin '27 Elizabeth Cleaves '29 Logan Downey '29 |
Reema Nannapaneni '27 Mark Carella '27 Luka Vukmirica '29 |
|
Texas A&M University | Aggies 2 | Canaan Cortes '26 Jack Riley '29 Zachary Aronson '27 |
Elisabeth Ritenour '27 Jeffrey Souto '27 Charlotte Heaney '28 |
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 ±0.09 means a rating gap counts about 8% 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.
