| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 | Brown University | Bears | 13–2 | |
| 2 | Harvard University | Crimson | 11–4 | |
| 3 | Massachusetts Institute of Technology | Engineers | 11–4 | |
| 4 | Boston College | Eagles | 6–9 | |
| 5 | Tufts University | Jumbos | 4–11 | |
| 6 | U. S. Coast Guard Academy | Bears | 0–15 |
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 25,847 team race matches sailed before 2025-03-15, using the line-ups that actually raced. It called 40 of 45 matches (89%) with a log-loss of 0.382 (a coin flip scores 0.693), so the event went well for the forecast. Orderliness +0.56 ± 0.30: an orderly event, more orderly than 95% 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) |
|---|---|---|---|---|---|---|---|---|
| 3 | Massachusetts Institute of Technology | 8.46 | 9.36 | 11–4 | 8.6 | +0.90 | 1.5 | 13% |
| 2 | Harvard University | 8.79 | 9.32 | 11–4 | 9.7 | +0.53 | 0.5 | 31% |
| 1 | Brown University | 9.91 | 9.94 | 13–2 | 12.9 | +0.03 | 0.0 | 66% |
| 4 | Boston College | 7.81 | 7.76 | 6–9 | 6.1 | −0.05 | 0.0 | 65% |
| 5 | Tufts University | 7.21 | 7.13 | 4–11 | 4.2 | −0.07 | 0.0 | 66% |
| 6 | U. S. Coast Guard Academy | 7.01 | 4.01 | 0–15 | 3.5 | ≤−3.00 | 5.7 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Brooke Barry | Massachusetts Institute of Technology | 2.33 | 3.23 | 11–4 | 8.6 | +0.90 | 1.5 | 13% |
| Cordelia Burn | Harvard University | 3.09 | 3.62 | 11–4 | 9.7 | +0.53 | 0.5 | 31% |
| Meredith Broadus | Tufts University | 2.22 | 2.14 | 4–11 | 4.2 | −0.07 | 0.0 | 66% |
| Laura Slovensky | U. S. Coast Guard Academy | 2.45 | -0.55 | 0–15 | 3.5 | ≤−3.00 | 5.7 | >99% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | Bears | Crimson | Engineers | Eagles | Jumbos | Bears | |
|---|---|---|---|---|---|---|---|---|
| Brown University Bears | 4–1 | ✕ | W | L | W | W | W | |
| Harvard University Crimson | 3–2 | L | ✕ | W | L | W | W | |
| Massachusetts Institute of Technology Engineers | 4–1 | W | L | ✕ | W | W | W | |
| Boston College Eagles | 2–3 | L | W | L | ✕ | L | W | |
| Tufts University Jumbos | 2–3 | L | L | L | W | ✕ | W | |
| U. S. Coast Guard Academy Bears | 0–5 | L | L | L | L | L | ✕ |
| Team | Rec. | Bears | Crimson | Engineers | Eagles | Jumbos | Bears | |
|---|---|---|---|---|---|---|---|---|
| Brown University Bears | 5–0 | ✕ | W | W | W | W | W | |
| Harvard University Crimson | 4–1 | L | ✕ | W | W | W | W | |
| Massachusetts Institute of Technology Engineers | 3–2 | L | L | ✕ | W | W | W | |
| Boston College Eagles | 2–3 | L | L | L | ✕ | W | W | |
| Tufts University Jumbos | 1–4 | L | L | L | L | ✕ | W | |
| U. S. Coast Guard Academy Bears | 0–5 | L | L | L | L | L | ✕ |
| Team | Rec. | Bears | Crimson | Engineers | Eagles | Jumbos | Bears | |
|---|---|---|---|---|---|---|---|---|
| Brown University Bears | 4–1 | ✕ | L | W | W | W | W | |
| Harvard University Crimson | 4–1 | W | ✕ | L | W | W | W | |
| Massachusetts Institute of Technology Engineers | 4–1 | L | W | ✕ | W | W | W | |
| Boston College Eagles | 2–3 | L | L | L | ✕ | W | W | |
| Tufts University Jumbos | 1–4 | L | L | L | L | ✕ | W | |
| U. S. Coast Guard Academy Bears | 0–5 | L | L | L | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | Crimson | 1-2-5 | def. | Bears | 3-4-6 | ||
| 2 | Engineers | 1-3-5 | def. | Bears | 2-4-6 | ||
| 3 | Jumbos | 1-2-6 | def. | Eagles | 3-4-5 | ||
| 4 | Bears | 1-2-3 | def. | Bears | 4-5-6 | ||
| 5 | Crimson | 1-3-5 | def. | Jumbos | 2-4-6 | ||
| 6 | Engineers | 1-2-5 | def. | Eagles | 3-4-6 | ||
| 7 | Bears | 1-3-6 | def. | Jumbos | 2-4-5 | ||
| 8 | Engineers | 1-2-3 | def. | Bears | 4-5-6 | ||
| 9 | Eagles | 1-2-6 | def. | Crimson | 3-4-5 | ||
| 10 | Engineers | 1-2-6 | def. | Jumbos | 3-4-5 | ||
| 11 | Bears | 1-2-3 | def. | Crimson | 4-5-6 | ||
| 12 | Eagles | 1-2-5 | def. | Bears | 3-4-6 | ||
| 13 | Crimson | 1-3-5 | def. | Engineers | 2-4-6 | ||
| 14 | Jumbos | 1-3-4 | def. | Bears | 2-5-6 | ||
| 15 | Bears | 1-4-5 | def. | Eagles | 2-3-6 | ||
| 16 | Crimson | 1-2-6 | def. | Bears | 3-4-5 | ||
| 17 | Bears | 1-2-3 | def. | Engineers | 4-5-6 | ||
| 18 | Eagles | 2-3-4 | def. | Jumbos | 1-5-6 | ||
| 19 | Bears | 1-2-3 | def. | Bears | 4-5-6 | ||
| 20 | Crimson | 1-2-5 | def. | Jumbos | 3-4-6 | ||
| 21 | Engineers | 2-3-4 | def. | Eagles | 1-5-6 | ||
| 22 | Bears | 1-2-3 | def. | Jumbos | 4-5-6 | ||
| 23 | Engineers | 1-2-4 | def. | Bears | 3-5-6 | ||
| 24 | Crimson | 1-2-5 | def. | Eagles | 3-4-6 | ||
| 25 | Engineers | 1-2-4 | def. | Jumbos | 3-5-6 | ||
| 26 | Bears | 1-2-5 | def. | Crimson | 3-4-6 | ||
| 27 | Eagles | 2-3-4 | def. | Bears | 1-5-6 | ||
| 28 | Crimson | 1-2-5 | def. | Engineers | 3-4-6 | ||
| 29 | Jumbos | 1-3-4 | def. | Bears | 2-5-6 | ||
| 30 | Bears | 1-2-6 | def. | Eagles | 3-4-5 | ||
| 31 | Crimson | 1-2-5 | def. | Bears | 3-4-6 | ||
| 32 | Bears | 1-2-3 | def. | Engineers | 4-5-6 | ||
| 33 | Eagles | 2-3-4 | def. | Jumbos | 1-5-6 | ||
| 34 | Bears | 1-2-3 | def. | Bears | 4-5-6 | ||
| 35 | Crimson | 1-2-5 | def. | Jumbos | 3-4-6 | ||
| 36 | Engineers | 1-2-5 | def. | Eagles | 3-4-6 | ||
| 37 | Bears | 2-3-4 | def. | Jumbos | 1-5-6 | ||
| 38 | Engineers | 2-3-4 | def. | Bears | 1-5-6 | ||
| 39 | Crimson | 1-2-5 | def. | Eagles | 3-4-6 | ||
| 40 | Engineers | 2-3-5 | def. | Jumbos | 1-4-6 | ||
| 41 | Crimson | 1-3-5 | def. | Bears | 2-4-6 | ||
| 42 | Eagles | 2-3-4 | def. | Bears | 1-5-6 | ||
| 43 | Engineers | 2-3-4 | def. | Crimson | 1-5-6 | ||
| 44 | Jumbos | 1-3-5 | def. | Bears | 2-4-6 | ||
| 45 | Bears | 1-3-5 | def. | Eagles | 2-4-6 | ||
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
| Brown University | Bears | Katharine Doble '26 Katherine Mcnamara '26 Laura Hamilton '27 |
Francesca Edmands '25 Menasha Leport '25 Katherine Majette '27 |
|
| Harvard University | Crimson | Cordelia Burn '25 Kate Danielson '28 Zoey Ziskind '27 |
Peyton Hadfield '27 Amelie Zucker '28 Katherine Shin '27 |
|
| Massachusetts Institute of Technology | Engineers | Brooke Barry '26 Brooke Schmelz '25 Lucy Brock '25 |
Mercedes Escandon '25 Tamsin Horne '28 Sophia Woodbury '28 Elliott Chalcraft '27 Shayne Bersin '25 |
|
| Boston College | Eagles | Caroline Sibilly '26 Kate Joslin '28 Sara Schumann '25 |
Kennedy Laureigh '26 Sophia Hacket '25 Ainsley West '28 |
|
| Tufts University | Jumbos | Maisie Macgillivray '25 Meredith Broadus '25 Sophia Hubbard '28 |
Eloise Goedkoop '27 Nicole Morikawa '25 Lilly Ladd '26 |
|
| U. S. Coast Guard Academy | Bears | Ella Demand '28 Julia Conneely '26 Laura Slovensky '25 |
Celia Houston '25 Allison Nystrom '26 Charlotte Mccarthy '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 ±0.06 means a rating gap counts about 6% less 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.