| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 | Boston College | Eagles | 12–0 | |
| 2 | Brown University | Bears | 8–4 | |
| 3 | U. S. Coast Guard Academy | Bears | 5–7 | |
| 4 | Massachusetts Institute of Technology | Engineers | 5–7 | |
| 5 | University of Rhode Island | Rams | 0–12 |
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 22,767 team race matches sailed before 2024-03-16, using the line-ups that actually raced. It called 27 of 30 matches (90%) with a log-loss of 0.277 (a coin flip scores 0.693), so the event went well for the forecast. Orderliness +0.56 ± 0.32: 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.
| Place | School | Pred. | Strength | Boats (skipper / crew, boat value) | Pred. win % | Record | vs forecast |
|---|---|---|---|---|---|---|---|
| 1 | Boston College Eagles | 1 | 10.07 school +0.25 | Colleen O'Brien / Libby Redmond 3.36 Michaela O'Brien / Sophia Hacket 3.27 Caroline Sibilly / Katelyn Mcgauley 3.18 | 89% | 12–0 (100%) | +1.3 |
| 2 | Brown University Bears | 2 | 8.60 school +0.25 | Brielle Willoughby / Brianna Ross 3.21 Charlotte Costikyan / Lily Shore 2.61 Katharine Doble / Katherine Majette 2.68 3–3 Laura Hamilton / Katherine Majette 2.34 5–1 | 61% | 8–4 (67%) | +0.7 |
| 3 | U. S. Coast Guard Academy Bears | 4 | 7.54 | Emily Bornarth / Sarah Krajewski 2.97 Laura Slovensky / Mia Quinlan Julia Conneely / Charlotte Mccarthy 2.21 | 35% | 5–7 (42%) | +0.8 |
| 4 | Massachusetts Institute of Technology Engineers | 3 | 8.25 school +0.06 | Brooke Barry / Mercedes Escandon 2.03 Brooke Schmelz / Samantha Karlson 3.17 Lucy Brock / Shayne Bersin 2.98 | 54% | 5–7 (42%) | −1.4 |
| 5 | University of Rhode Island Rams | 5 | 6.28 school −0.09 | Kytalin Hendrickson / Lily Moore 2.47 Katie Coughlin / Kaylee Good 1.84 Leah Rickard / Ursula Budd 2.01 | 12% | 0–12 (0%) | −1.4 |
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 | Boston College | 10.07 | 13.07 | 12–0 | 10.6 | ≥+3.00 | 2.0 | 23% |
| 3 | U. S. Coast Guard Academy | 7.54 | 7.93 | 5–7 | 4.2 | +0.39 | 0.2 | 41% |
| 2 | Brown University | 8.60 | 8.96 | 8–4 | 7.3 | +0.36 | 0.2 | 45% |
| 4 | Massachusetts Institute of Technology | 8.25 | 7.57 | 5–7 | 6.4 | −0.68 | 0.7 | 91% |
| 5 | University of Rhode Island | 6.28 | 3.28 | 0–12 | 1.4 | ≤−3.00 | 2.1 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Laura Slovensky | U. S. Coast Guard Academy | 2.31 | 2.70 | 5–7 | 4.2 | +0.39 | 0.2 | 41% |
| Charlotte Costikyan | Brown University | 2.61 | 2.97 | 8–4 | 7.3 | +0.36 | 0.2 | 45% |
| Brooke Barry | Massachusetts Institute of Technology | 2.03 | 1.35 | 5–7 | 6.4 | −0.68 | 0.7 | 91% |
| Katie Coughlin | University of Rhode Island | 1.84 | -1.16 | 0–12 | 1.4 | ≤−3.00 | 2.1 | >99% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | Eagles | Bears | Bears | Engineers | Rams | |
|---|---|---|---|---|---|---|---|
| Boston College Eagles | 4–0 | ✕ | W | W | W | W | |
| Brown University Bears | 2–2 | L | ✕ | W | L | W | |
| U. S. Coast Guard Academy Bears | 1–3 | L | L | ✕ | L | W | |
| Massachusetts Institute of Technology Engineers | 3–1 | L | W | W | ✕ | W | |
| University of Rhode Island Rams | 0–4 | L | L | L | L | ✕ |
| Team | Rec. | Eagles | Bears | Bears | Engineers | Rams | |
|---|---|---|---|---|---|---|---|
| Boston College Eagles | 4–0 | ✕ | W | W | W | W | |
| Brown University Bears | 3–1 | L | ✕ | W | W | W | |
| U. S. Coast Guard Academy Bears | 2–2 | L | L | ✕ | W | W | |
| Massachusetts Institute of Technology Engineers | 1–3 | L | L | L | ✕ | W | |
| University of Rhode Island Rams | 0–4 | L | L | L | L | ✕ |
| Team | Rec. | Eagles | Bears | Bears | Engineers | Rams | |
|---|---|---|---|---|---|---|---|
| Boston College Eagles | 4–0 | ✕ | W | W | W | W | |
| Brown University Bears | 3–1 | L | ✕ | W | W | W | |
| U. S. Coast Guard Academy Bears | 2–2 | L | L | ✕ | W | W | |
| Massachusetts Institute of Technology Engineers | 1–3 | L | L | L | ✕ | W | |
| University of Rhode Island Rams | 0–4 | L | L | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | Eagles | 1-2-6 | def. | Bears | 3-4-5 | ||
| 2 | Bears | 1-2-6 | def. | Rams | 3-4-5 | ||
| 3 | Eagles | 1-2-3 | def. | Engineers | 4-5-6 | ||
| 4 | Bears | 1-2-5 | def. | Bears | 3-4-6 | ||
| 5 | Engineers | 2-3-4 | def. | Rams | 1-5-6 | ||
| 6 | Eagles | 1-2-5 | def. | Bears | 3-4-6 | ||
| 7 | Bears | 1-2-3 | def. | Rams | 4-5-6 | ||
| 8 | Engineers | 1-2-5 | def. | Bears | 3-4-6 | ||
| 9 | Eagles | 1-2-3 | def. | Rams | 4-5-6 | ||
| 10 | Engineers | 1-2-4 | def. | Bears | 3-5-6 | ||
| 11 | Eagles | 1-2-5 | def. | Bears | 3-4-6 | ||
| 12 | Bears | 1-3-5 | def. | Rams | 2-4-6 | ||
| 13 | Eagles | 1-2-5 | def. | Engineers | 3-4-6 | ||
| 14 | Bears | 2-3-5 | def. | Bears | 1-4-6 | ||
| 15 | Engineers | 1-2-3 | def. | Rams | 4-5-6 | ||
| 16 | Eagles | 1-2-3 | def. | Bears | 4-5-6 | ||
| 17 | Bears | 1-2-3 | def. | Rams | 4-5-6 | ||
| 18 | Bears | 1-2-6 | def. | Engineers | 3-4-5 | ||
| 19 | Eagles | 1-2-3 | def. | Rams | 4-5-6 | ||
| 20 | Bears | 1-4-5 | def. | Engineers | 2-3-6 | ||
| 21 | Eagles | 1-2-5 | def. | Bears | 3-4-6 | ||
| 22 | Bears | 1-2-5 | def. | Rams | 3-4-6 | ||
| 23 | Eagles | 1-2-5 | def. | Engineers | 3-4-6 | ||
| 24 | Bears | 1-2-3 | def. | Bears | 4-5-6 | ||
| 25 | Engineers | 1-2-6 | def. | Rams | 3-4-5 | ||
| 26 | Eagles | 1-2-4 | def. | Bears | 3-5-6 | ||
| 27 | Bears | 1-2-5 | def. | Rams | 3-4-6 | ||
| 28 | Bears | 2-3-4 | def. | Engineers | 1-5-6 | ||
| 29 | Eagles | 1-2-5 | def. | Rams | 3-4-6 | ||
| 30 | Bears | 1-3-5 | def. | Engineers | 2-4-6 | ||
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
| Boston College | Eagles | Caroline Sibilly '26 Colleen O'Brien '24 Michaela O'Brien '24 |
Katelyn Mcgauley '24 Libby Redmond '24 Sophia Hacket '25 |
|
| Brown University | Bears | Brielle Willoughby '24 Charlotte Costikyan '24 Katharine Doble '26 Laura Hamilton '27 |
Brianna Ross '26 Lily Shore '24 Katherine Majette '27 |
|
| U. S. Coast Guard Academy | Bears | Emily Bornarth '24 Julia Conneely '26 Laura Slovensky '25 |
Sarah Krajewski '26 Charlotte Mccarthy '25 Mia Quinlan '24 |
|
| Massachusetts Institute of Technology | Engineers | Brooke Barry '26 Brooke Schmelz '25 Lucy Brock '25 |
Mercedes Escandon '25 Samantha Karlson '25 Shayne Bersin '25 |
|
| University of Rhode Island | Rams | Katie Coughlin '24 Kytalin Hendrickson '26 Leah Rickard '26 |
Kaylee Good '26 Lily Moore '26 Ursula Budd '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 ±0.11 means a rating gap counts about 10% 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.