| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
Brown University | Bears 1 | 7–1 |
| 2 |  |
Tufts University | Jumbos 1 | 6–2 |
| 3 | |
Brown University | Bears 2 | 6–2 |
| 4 |  |
Massachusetts Institute of Technology | Engineers 1 | 5–3 |
| 5 |  |
Connecticut College | Camels 1 | 5–3 |
| 6 |  |
Northeastern University | Huskies | 4–4 |
| 7 | |
Tufts University | Jumbos 2 | 2–6 |
| 8 | |
Connecticut College | Camels 2 | 1–7 |
| 9 | |
Massachusetts Institute of Technology | Engineers 2 | 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 25,440 team race matches sailed before 2025-03-08, using the line-ups that actually raced. It called 28 of 33 matches (85%) with a log-loss of 0.383 (a coin flip scores 0.693), so the event went well for the forecast. Orderliness +0.61 ± 0.35: an orderly event, more orderly than 97% 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 | Brown University Bears 1 | 8.65 | 11.65 | 7–0 | 5.5 | ≥+3.00 | 2.4 | 17% |
| 3 | Brown University Bears 2 | 7.45 | 8.86 | 6–2 | 3.8 | +1.40 | 2.1 | 9% |
| 5 | Connecticut College Camels 1 | 7.51 | 8.01 | 5–3 | 4.2 | +0.50 | 0.3 | 41% |
| 2 | Tufts University Jumbos 1 | 8.42 | 8.61 | 6–2 | 5.7 | +0.19 | 0.0 | 60% |
| 6 | Northeastern University Huskies | 7.89 | 7.33 | 4–4 | 4.9 | −0.56 | 0.4 | 87% |
| 8 | Connecticut College Camels 2 | 6.03 | 5.32 | 1–6 | 1.6 | −0.70 | 0.3 | 88% |
| 7 | Tufts University Jumbos 2 | 6.97 | 6.18 | 2–5 | 2.9 | −0.79 | 0.5 | 91% |
| 4 | Massachusetts Institute of Technology Engineers 1 | 8.08 | 6.97 | 2–3 | 3.1 | −1.11 | 0.9 | 95% |
| 9 | Massachusetts Institute of Technology Engineers 2 | 5.61 | 2.61 | 0–8 | 1.2 | ≤−3.00 | 1.8 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| William Hurd | Connecticut College | 2.14 | 3.88 | 5–1 | 3.3 | +1.74 | 1.9 | 13% |
| Charles Gish | Brown University | 2.67 | 4.07 | 6–2 | 3.8 | +1.40 | 2.1 | 9% |
| Charles Wilkinson | Northeastern University | 1.93 | 1.40 | 3–3 | 3.6 | −0.53 | 0.2 | 86% |
| Adrian Winkelman | Northeastern University | 2.78 | 2.22 | 4–4 | 4.9 | −0.56 | 0.4 | 87% |
| Duncan Craine | Connecticut College | 2.27 | 1.56 | 1–6 | 1.6 | −0.70 | 0.3 | 88% |
| Meredith Broadus | Tufts University | 2.09 | 1.30 | 2–5 | 2.9 | −0.79 | 0.5 | 91% |
| Joshua Dillon | Northeastern University | 2.51 | 1.60 | 2–3 | 3.0 | −0.91 | 0.7 | 93% |
| Brooke Barry | Massachusetts Institute of Technology | 2.33 | 1.22 | 2–3 | 3.1 | −1.11 | 0.9 | 95% |
| Max Katz-Christy | Massachusetts Institute of Technology | 2.65 | 1.54 | 2–3 | 3.1 | −1.11 | 0.9 | 95% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | 1 | 1 | 2 | 1 | 1 | Huskies | 2 | 2 | 2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Brown University Bears 1 | 13–3 | ✕ | L | ✕ | W | W | W | L | W | W |
| Tufts University Jumbos 1 | 8–8 | W | ✕ | W | L | L | L | ✕ | L | L |
| Brown University Bears 2 | 13–3 | ✕ | L | ✕ | W | W | W | L | W | W |
| Massachusetts Institute of Technology Engineers 1 | 5–11 | L | W | L | ✕ | W | L | W | W | ✕ |
| Connecticut College Camels 1 | 6–10 | L | W | L | L | ✕ | L | W | ✕ | L |
| Northeastern University Huskies | 4–4 | L | W | L | W | W | ✕ | W | W | W |
| Tufts University Jumbos 2 | 8–8 | W | ✕ | W | L | L | L | ✕ | L | L |
| Connecticut College Camels 2 | 6–10 | L | W | L | L | ✕ | L | W | ✕ | L |
| Massachusetts Institute of Technology Engineers 2 | 5–11 | L | W | L | ✕ | W | L | W | W | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Engineers 1 | 1-3-5 | def. | |
Camels 1 | 2-4-6 |
| 2 | |
Camels 2 | 1-4-5 | def. | |
Engineers 2 | 2-3-6 |
| 3 | |
Jumbos 1 | 1-2-3 | def. | |
Huskies | 4-5-6 |
| 4 | |
Bears 1 | 1-2-4 | def. | |
Jumbos 2 | 3-5-6 |
| 5 | |
Bears 2 | 1-3-5 | def. | |
Camels 1 | 2-4-6 |
| 6 | |
Engineers 1 | 1-2-5 | def. | |
Engineers 2 | 3-4-6 |
| 7 | |
Bears 1 | 1-2-4 | def. | |
Jumbos 1 | 3-5-6 |
| 8 | |
Jumbos 2 | 1-2-4 | def. | |
Camels 2 | 3-5-6 |
| 9 | |
Bears 2 | 2-3-5 | def. | |
Engineers 1 | 1-4-6 |
| 10 | |
Camels 1 | 2-3-4 | def. | |
Huskies | 1-5-6 |
| 11 | |
Jumbos 2 | 1-3-4 | def. | |
Engineers 2 | 2-5-6 |
| 12 | |
Jumbos 1 | 1-2-3 | def. | |
Camels 2 | 4-5-6 |
| 13 | |
Bears 1 | 1-2-3 | def. | |
Camels 1 | 4-5-6 |
| 14 | |
Huskies | 1-2-4 | def. | |
Engineers 1 | 3-5-6 |
| 15 | |
Bears 2 | 1-2-5 | def. | |
Jumbos 2 | 3-4-6 |
| 16 | |
Bears 1 | 1-2-3 | def. | |
Camels 2 | 4-5-6 |
| 17 | |
Jumbos 1 | 1-2-4 | def. | |
Engineers 2 | 3-5-6 |
| 18 | |
Bears 2 | 1-3-4 | def. | |
Huskies | 2-5-6 |
| 19 | |
Camels 1 | 1-2-5 | def. | |
Jumbos 2 | 3-4-6 |
| 20 | |
Jumbos 1 | 1-2-3 | def. | |
Engineers 1 | 4-5-6 |
| 21 | |
Bears 2 | 1-3-5 | def. | |
Camels 2 | 2-4-6 |
| 22 | |
Bears 1 | 1-2-3 | def. | |
Engineers 2 | 4-5-6 |
| 23 | |
Huskies | 1-2-3 | def. | |
Jumbos 2 | 4-5-6 |
| 24 | |
Camels 1 | 1-2-4 | def. | |
Jumbos 1 | 3-5-6 |
| 25 | |
Engineers 1 | 1-2-6 | def. | |
Bears 1 | 3-4-5 |
| 26 | |
Bears 2 | 1-2-6 | def. | |
Engineers 2 | 3-4-5 |
| 27 | |
Huskies | 1-2-3 | def. | |
Camels 2 | 4-5-6 |
| 28 | |
Jumbos 1 | 1-3-6 DNF | def. | |
Jumbos 2 | 2-4-5 |
| 29 | |
Camels 1 | 1-2-3 | def. | |
Engineers 2 | 4-5-6 |
| 30 | |
Bears 1 | 1-2-3 | def. | |
Bears 2 | 4-5-6 |
| 31 | |
Engineers 1 | 1-2-3 | def. | |
Jumbos 2 | 4-5-6 |
| 32 | |
Camels 1 | 1-2-3 | def. | |
Camels 2 | 4-5-6 DNF |
| 33 | |
Huskies | 1-2-3 | def. | |
Engineers 2 | 4-5-6 |
| 34 | |
Jumbos 1 | 1-2-4 | def. | |
Bears 2 | 3-5-6 DNF |
| 35 | |
Engineers 1 | 1-2-4 | def. | |
Camels 2 | 3-5-6 |
| 36 | |
Bears 1 | 1-2-4 | def. | |
Huskies | 3-5-6 |
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.
