| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
Yale University | Bulldogs | 5–0 |
| 2 |  |
Brown University | Bears | 4–1 |
| 3 |  |
Massachusetts Institute of Technology | Engineers | 2–3 |
| 4 |  |
Tufts University | Jumbos | 2–3 |
| 5 |  |
Roger Williams University | Hawks | 1–4 |
| 6 |  |
Harvard University | Crimson | 1–4 |
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 25,143 team race matches sailed before 2025-03-01, using the line-ups that actually raced. It called 10 of 15 matches (67%) with a log-loss of 0.536 (a coin flip scores 0.693), so the event went about as expected for the forecast. Orderliness −0.36 ± 0.45: a typical event, more orderly than 15% 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 | Yale University | 10.64 | 13.64 | 5–0 | 4.6 | ≥+3.00 | 0.6 | 63% |
| 3 | Massachusetts Institute of Technology | 6.51 | 8.13 | 2–3 | 0.8 | +1.63 | 1.6 | 16% |
| 4 | Tufts University | 6.95 | 8.05 | 2–3 | 1.1 | +1.11 | 0.7 | 30% |
| 2 | Brown University | 9.38 | 10.09 | 4–1 | 3.6 | +0.71 | 0.2 | 57% |
| 5 | Roger Williams University | 7.96 | 6.40 | 1–4 | 2.1 | −1.56 | 1.2 | 97% |
| 6 | Harvard University | 8.56 | 6.28 | 1–4 | 2.8 | −2.28 | 2.9 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Carmen Cowles | Yale University | 3.64 | 6.64 | 5–0 | 4.6 | ≥+3.00 | 0.6 | 63% |
| Emma Cowles | Yale University | 3.73 | 6.73 | 5–0 | 4.6 | ≥+3.00 | 0.6 | 63% |
| Brooke Barry | Massachusetts Institute of Technology | 2.11 | 3.74 | 2–3 | 0.8 | +1.63 | 1.6 | 16% |
| Meredith Broadus | Tufts University | 2.08 | 3.19 | 2–3 | 1.1 | +1.11 | 0.7 | 30% |
| Lucy Meagher | Roger Williams University | 3.08 | 1.53 | 1–4 | 2.1 | −1.56 | 1.2 | 97% |
| Katherine Mcgagh | Roger Williams University | 2.08 | 0.52 | 1–4 | 2.1 | −1.56 | 1.2 | 97% |
| Sophia Montgomery | Harvard University | 2.62 | 0.34 | 1–4 | 2.8 | −2.28 | 2.9 | >99% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | Bulldogs | Bears | Engineers | Jumbos | Hawks | Crimson | |
|---|---|---|---|---|---|---|---|---|
| Yale University Bulldogs | 5–0 | ✕ | W | W | W | W | W |
| Brown University Bears | 4–1 | L | ✕ | W | W | W | W |
| Massachusetts Institute of Technology Engineers | 2–3 | L | L | ✕ | W | W | L |
| Tufts University Jumbos | 2–3 | L | L | L | ✕ | W | W |
| Roger Williams University Hawks | 1–4 | L | L | L | L | ✕ | W |
| Harvard University Crimson | 1–4 | L | L | W | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Crimson | 1-2-5 | def. | |
Engineers | 3-4-6 |
| 2 | |
Bears | 1-3-4 | def. | |
Hawks | 2-5-6 |
| 3 | |
Bulldogs | 1-2-4 | def. | |
Jumbos | 3-5-6 |
| 4 | |
Bears | 1-2-3 | def. | |
Engineers | 4-5-6 |
| 5 | |
Bulldogs | 1-2-4 | def. | |
Crimson | 3-5-6 |
| 6 | |
Jumbos | 2-3-4 | def. | |
Hawks | 1-5-6 |
| 7 | |
Bulldogs | 1-2-3 | def. | |
Engineers | 4-5-6 |
| 8 | |
Bears | 1-3-4 | def. | |
Jumbos | 2-5-6 |
| 9 | |
Hawks | 1-3-6 | def. | |
Crimson | 2-4-5 |
| 10 | |
Bulldogs | 1-2-5 | def. | |
Bears | 3-4-6 |
| 11 | |
Jumbos | 1-3-6 | def. | |
Crimson | 2-4-5 |
| 12 | |
Engineers | 1-2-5 | def. | |
Hawks | 3-4-6 |
| 13 | |
Bears | 1-2-5 | def. | |
Crimson | 3-4-6 |
| 14 | |
Bulldogs | 1-2-5 | def. | |
Hawks | 3-4-6 |
| 15 | |
Engineers | 1-2-5 | def. | |
Jumbos | 3-4-6 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
Yale University | Bulldogs | Carmen Cowles '25 Dorothy Mendelblatt '28 Emma Cowles '25 |
Carmen Berg '26 Ximena Escobar '25 Jessi Avila-Shah '25 |
|
Brown University | Bears | Katharine Doble '26 Katherine Mcnamara '26 Laura Hamilton '27 |
Savannah Young '25 Menasha Leport '25 Francesca Edmands '25 Reese Corckran '28 |
|
Massachusetts Institute of Technology | Engineers | Brooke Barry '26 Emma Wang '27 Sophia Woodbury '28 |
Christine Keedy Brown '28 Elliott Chalcraft '27 Shayne Bersin '25 |
|
Tufts University | Jumbos | Maisie Macgillivray '25 Meredith Broadus '25 Sophia Hubbard '28 |
Eloise Goedkoop '27 Nicole Morikawa '25 Annabel De Alessi '25 Lilly Ladd '26 |
|
Roger Williams University | Hawks | Katherine Mcgagh '26 Lucy Meagher '27 Tavia Smith '27 |
Elyssa Franklin '26 Maren Matthews '26 Jancy Grayson '28 |
|
Harvard University | Crimson | Kate Danielson '28 Sophia Montgomery '25 Zoey Ziskind '27 |
Amelie Zucker '28 Daniella Bregman '25 Peyton Hadfield '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 ±0.07 means a rating gap counts about 6% 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.
