| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
University of Miami | Hurricanes | 4–0 |
| 2 |  |
University of South Florida | Bulls | 3–1 |
| 3 | |
University of South Florida | Bulls Women | 2–2 |
| 4 |  |
University of California at Berkeley | Bears | 1–3 |
| 5 |  |
Texas A&M University | Aggies | 0–4 |
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 23,136 team race matches sailed before 2024-03-17, using the line-ups that actually raced. It called 10 of 10 matches (100%) with a log-loss of 0.346 (a coin flip scores 0.693), so the event went well for the forecast. Orderliness +1.07 ± 0.70: an orderly event, more orderly than 100% 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.
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 | University of Miami Hurricanes | 5.97 | 8.97 | 4–0 | 2.9 | ≥+3.00 | 1.9 | 25% |
| 2 | University of South Florida Bulls | 5.65 | 6.35 | 3–1 | 2.5 | +0.70 | 0.3 | 50% |
| 3 | University of South Florida Bulls Women | 5.42 | 5.09 | 2–2 | 2.3 | −0.33 | 0.1 | 80% |
| 4 | University of California at Berkeley Bears | 5.02 | 3.94 | 1–3 | 1.8 | −1.08 | 0.6 | 94% |
| 5 | Texas A&M University Aggies | 3.71 | 0.71 | 0–4 | 0.6 | ≤−3.00 | 0.9 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Danny Riano | University of Miami | 2.15 | 5.15 | 4–0 | 2.9 | ≥+3.00 | 1.9 | 25% |
| Andreas Keswater | University of South Florida | 2.35 | 3.05 | 3–1 | 2.5 | +0.70 | 0.3 | 50% |
| Jordan Byrd | University of South Florida | 2.18 | 2.88 | 3–1 | 2.5 | +0.70 | 0.3 | 50% |
| Garrett Floerchinger | University of South Florida | 0.68 | 1.39 | 3–1 | 2.5 | +0.70 | 0.3 | 50% |
| Kalea Woodard | University of South Florida | 1.60 | 1.27 | 2–2 | 2.3 | −0.33 | 0.1 | 80% |
| Marina Dreyfuss | University of South Florida | 1.28 | 0.95 | 2–2 | 2.3 | −0.33 | 0.1 | 80% |
| Katherine Olsen | University of California at Berkeley | 1.55 | 0.48 | 1–3 | 1.8 | −1.08 | 0.6 | 94% |
| Sydney Krahulik | Texas A&M University | 0.74 | -2.26 | 0–4 | 0.6 | ≤−3.00 | 0.9 | >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 | University of Miami Hurricanes | Texas A&M University Aggies | 89% | |
| 1 | 2 | University of South Florida Bulls | University of California at Berkeley Bears | 65% | |
| 1 | 3 | University of Miami Hurricanes | University of South Florida Bulls Women | 60% | |
| 1 | 4 | University of California at Berkeley Bears | Texas A&M University Aggies | 79% | |
| 1 | 5 | University of South Florida Bulls | University of South Florida Bulls Women | 56% | |
| 1 | 6 | University of Miami Hurricanes | University of California at Berkeley Bears | 75% | |
| 1 | 7 | University of South Florida Bulls | Texas A&M University Aggies | 87% | |
| 1 | 8 | University of South Florida Bulls Women | University of California at Berkeley Bears | 60% | |
| 1 | 9 | University of Miami Hurricanes | University of South Florida Bulls | 62% | |
| 1 | 10 | University of South Florida Bulls Women | Texas A&M University Aggies | 85% |
| Team | Rec. | Hurricanes | Bulls | Women | Bears | Aggies | |
|---|---|---|---|---|---|---|---|
| University of Miami Hurricanes | 4–0 | ✕ | W | W | W | W |
| University of South Florida Bulls | 5–3 | L | ✕ | ✕ | W | W |
| University of South Florida Bulls Women | 5–3 | L | ✕ | ✕ | W | W |
| University of California at Berkeley Bears | 1–3 | L | L | L | ✕ | W |
| Texas A&M University Aggies | 0–4 | L | L | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Hurricanes | 1-2-6 | def. | |
Aggies | 3-4-5 |
| 2 | |
Bulls | 1-2-6 | def. | |
Bears | 3-4-5 |
| 3 | |
Hurricanes | 1-2-5 | def. | |
Bulls Women | 3-4-6 |
| 4 | |
Bears | 1-2-5 | def. | |
Aggies | 3-4-6 |
| 5 | |
Bulls | 1-4-5 | def. | |
Bulls Women | 2-3-6 |
| 6 | |
Hurricanes | 2-3-4 | def. | |
Bears | 1-5-6 |
| 7 | |
Bulls | 1-2-6 | def. | |
Aggies | 3-4-5 |
| 8 | |
Bulls Women | 1-2-4 | def. | |
Bears | 3-5-6 |
| 9 | |
Hurricanes | 1-2-3 | def. | |
Bulls | 4-5-6 |
| 10 | |
Bulls Women | 1-2-4 | def. | |
Aggies | 3-5-6 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
University of Miami | Hurricanes | Andrew Engel '26 Danny Riano '27 Oliver West '27 Steven Hardee '26 |
Danielle Ketner '26 Brady Briggs '27 Gizela Kohan '27 |
|
University of South Florida | Bulls | Andreas Keswater '24 Garrett Floerchinger '26 Jordan Byrd '24 |
Melissa Janes '26 Emmy Digiacomo '24 Dana Aljasir '27 |
|
University of South Florida | Bulls Women | Kailey Warrior '27 Kalea Woodard '24 Marina Dreyfuss '27 |
Saleta Powell '25 Madisen Hamai '26 Ochithya Fernando '25 Catherine Brennan '26 |
|
University of California at Berkeley | Bears | Katherine Olsen '25 Will Cornell '25 Wilton Lawton '26 |
Alyssa Cunningham Barkus '26 Emerson Marquez '27 Aliyah Al-Khafaji '27 |
|
Texas A&M University | Aggies | Kate Hennig '26 Michael Morse '25 Sydney Krahulik '27 |
Evelyn Dubois '27 Maggie Menefee '25 Emily Davison '24 |
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 ±0.00 means a rating gap counts about 1% 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.
