| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
Jacksonville University | Fins | 8–0 |
| 2 |  |
Rollins College | Tars | 5–3 |
| 3 |  |
University of Miami | Hurricanes | 4–4 |
| 4 |  |
University of South Florida | Bulls | 3–5 |
| 5 |  |
University of Central Florida | Knights | 0–8 |
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 16 of 18 matches (89%) with a log-loss of 0.212 (a coin flip scores 0.693), so the event went well for the forecast. Orderliness +0.26 ± 0.43: a typical event, more orderly than 72% 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 | Jacksonville University Fins | 1 | 7.31 | Patrick Igoe / Roman Borger 2.55 Cole Schweda / Will Platten 2.35 Hank Seum / Conner Brandon 2.31 | 92% | 8–0 (100%) | +0.6 |
| 2 | Rollins College Tars | 3 | 5.00 school +0.26 | Julia Scott / Jane Mckenzie 1.41 Jackson Mcgeough / Maggie Burnham 1.44 Hilton Kamps / Angelina Khourisader 1.85 | 48% | 3–3 (50%) | +0.1 |
| 3 | University of Miami Hurricanes | 2 | 5.63 school +0.26 | Brendan Jay / Riley Heidenry 1.85 Danny Riano / Eva Klovatskiy 2.07 Oliver West / Olivia Campmany 1.31 | 63% | 4–4 (50%) | −1.0 |
| 4 | University of South Florida Bulls | 4 | 4.61 home +0.41 | Luke Justin / Colin Molnar 1.69 Blake March / Catherine Brennan Marina Dreyfuss / Saleta Powell 1.53 3–1 Marina Dreyfuss / Ochithya Fernando 1.46 0–2 | 43% | 3–3 (50%) | +0.4 |
| 5 | University of Central Florida Knights | 5 | 1.05 | Julian Larsen / Hudson Jenkins Charlie Eckert / Nathan Crovatto Joseph Mrazek / Nikolas Pantelis | 2% | 0–8 (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 | Jacksonville University | 7.31 | 10.31 | 8–0 | 7.4 | ≥+3.00 | 0.9 | 52% |
| 4 | University of South Florida | 4.61 | 5.25 | 3–3 | 2.6 | +0.64 | 0.2 | 50% |
| 2 | Rollins College | 5.00 | 5.21 | 3–3 | 2.9 | +0.21 | 0.0 | 64% |
| 3 | University of Miami | 5.63 | 4.74 | 4–4 | 5.1 | −0.90 | 0.7 | 93% |
| 5 | University of Central Florida | 1.05 | -1.95 | 0–8 | 0.1 | ≤−3.00 | 0.2 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Luke Justin | University of South Florida | 1.69 | 2.33 | 3–3 | 2.6 | +0.64 | 0.2 | 50% |
| Blake March | University of South Florida | 0.90 | 1.54 | 3–3 | 2.6 | +0.64 | 0.2 | 50% |
| Marina Dreyfuss | University of South Florida | 1.53 | 2.17 | 3–3 | 2.6 | +0.64 | 0.2 | 50% |
| Julia Scott | Rollins College | 1.41 | 1.62 | 3–3 | 2.9 | +0.21 | 0.0 | 64% |
| Jackson Mcgeough | Rollins College | 1.44 | 1.65 | 3–3 | 2.9 | +0.21 | 0.0 | 64% |
| Danny Riano | University of Miami | 2.07 | 1.17 | 4–4 | 5.1 | −0.90 | 0.7 | 93% |
| Julian Larsen | University of Central Florida | 0.70 | -2.30 | 0–8 | 0.1 | ≤−3.00 | 0.2 | >99% |
| Joseph Mrazek | University of Central Florida | -0.68 | -3.67 | 0–8 | 0.1 | ≤−3.00 | 0.2 | >99% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | Fins | Tars | Hurricanes | Bulls | Knights | |
|---|---|---|---|---|---|---|---|
| Jacksonville University Fins | 4–0 | ✕ | W | W | W | W |
| Rollins College Tars | 3–1 | L | ✕ | W | W | W |
| University of Miami Hurricanes | 2–2 | L | L | ✕ | W | W |
| University of South Florida Bulls | 1–3 | L | L | L | ✕ | W |
| University of Central Florida Knights | 0–4 | L | L | L | L | ✕ |
| Team | Rec. | Fins | Tars | Hurricanes | Bulls | Knights | |
|---|---|---|---|---|---|---|---|
| Jacksonville University Fins | 4–0 | ✕ | W | W | W | W |
| Rollins College Tars | 2–2 | L | ✕ | L | W | W |
| University of Miami Hurricanes | 2–2 | L | W | ✕ | L | W |
| University of South Florida Bulls | 2–2 | L | L | W | ✕ | W |
| University of Central Florida Knights | 0–4 | L | L | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Tars | 1-2-4 | def. | |
Hurricanes | 3-5-6 |
| 2 | |
Fins | 1-2-3 | def. | |
Knights | 4-5-6 |
| 3 | |
Hurricanes | 1-3-4 | def. | |
Bulls | 2-5-6 |
| 4 | |
Fins | 1-2-5 | def. | |
Tars | 3-4-6 |
| 5 | |
Bulls | 1-2-3 | def. | |
Knights | 4-5-6 |
| 6 | |
Fins | 1-4-5 | def. | |
Hurricanes | 2-3-6 |
| 7 | |
Tars | 2-3-4 | def. | |
Knights | 1-5-6 |
| 8 | |
Fins | 1-2-4 | def. | |
Bulls | 3-5-6 |
| 9 | |
Hurricanes | 1-2-4 | def. | |
Knights | 3-5-6 |
| 10 | |
Tars | 2-3-4 | def. | |
Bulls | 1-5-6 |
| 11 | |
Hurricanes | 1-2-4 | def. | |
Tars | 3-5-6 |
| 12 | |
Fins | 1-2-4 | def. | |
Knights | 3-5-6 |
| 13 | |
Bulls | 1-5-6 | def. | |
Hurricanes | 2-3-4 DSQ |
| 14 | |
Fins | 1-4-5 | def. | |
Tars | 2-3-6 |
| 15 | |
Bulls | 1-2-6 | def. | |
Knights | 3-4-5 |
| 16 | |
Fins | 1-2-3 | def. | |
Hurricanes | 4-5-6 |
| 17 | |
Tars | 1-2-6 DNS | def. | |
Knights | 3-4-5 |
| 18 | |
Fins | 1-2-5 | def. | |
Bulls | 3-4-6 |
| 19 | |
Hurricanes | 1-2-4 | def. | |
Knights | 3-5-6 |
| 20 | |
Tars | 1-2-6 | def. | |
Bulls | 3-4-5 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
Jacksonville University | Fins | Cole Schweda '27 Hank Seum '27 Patrick Igoe '26 |
Will Platten '27 Conner Brandon '27 Roman Borger '26 |
|
Rollins College | Tars | Hilton Kamps '25 Jackson Mcgeough '25 Julia Scott '27 |
Angelina Khourisader '23 Maggie Burnham '26 Jane Mckenzie '27 |
|
University of Miami | Hurricanes | Brendan Jay '27 Danny Riano '27 Oliver West '27 |
Riley Heidenry '27 Eva Klovatskiy '26 Olivia Campmany '25 |
|
University of South Florida | Bulls | Blake March '27 Luke Justin '25 Marina Dreyfuss '27 |
Catherine Brennan '26 Colin Molnar '25 Saleta Powell '25 Ochithya Fernando '25 |
|
University of Central Florida | Knights | Charlie Eckert '27 Joseph Mrazek '24 Julian Larsen '26 |
Nathan Crovatto '25 Nikolas Pantelis '24 Hudson Jenkins '25 |
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 ±0.04 means a rating gap counts about 4% 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.
