| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
Brown University | Bears | 8–0 |
| 2 |  |
Harvard University | Crimson | 6–2 |
| 3 |  |
Bentley University | Falcons | 4–4 |
| 4 |  |
Fairfield University | Stags | 2–6 |
| 5 |  |
Massachusetts Maritime Academy | Buccaneers | 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 27,794 team race matches sailed before 2025-04-13, using the line-ups that actually raced. It called 19 of 20 matches (95%) with a log-loss of 0.213 (a coin flip scores 0.693), so the event went well for the forecast. Orderliness +0.73 ± 0.52: an orderly event, more orderly than 99% 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 | Brown University Bears | 1 | 8.85 school +0.38 | Tyler Lamm / Janelle Li 3.38 Emery Diemar / Phoebe Hackett 2.62 Julian Dahiya / Sasha Gordon 2.42 | 100% | 8–0 (100%) | +0.0 |
| 2 | Harvard University Crimson | 2 | 4.20 school +0.30 | Sarra Guezguez / Catherine Mignone William Bourell / Hanah Youn Xavier Ayala Vermont / Julia Downey | 63% | 6–2 (75%) | +1.0 |
| 3 | Bentley University Falcons | 4 | 2.69 home +0.37 | Andrew Blagden / Grace Kokkotos 0.67 John O'Connell / Isabel Walchli 0.88 Wilfred Hynes / Lucy Butterfield 0.76 | 33% | 4–4 (50%) | +1.4 |
| 4 | Fairfield University Stags | 3 | 2.75 school −0.18 | Nolan Cooper / Sawyer Obrien 1.66 Timothy Cronin / Morgan Martucci 0.64 Santiago Pastor / Mason Macuch | 34% | 2–6 (25%) | −0.7 |
| 5 | Massachusetts Maritime Academy Buccaneers | 5 | 2.11 school +0.38 | Kevin Mcneill / Hugo Langlais 0.82 Joseph Gedraitis / Henry Steadman Cole Capizzo / Gabriella-Marie Diez | 20% | 0–8 (0%) | −1.6 |
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 | 8.85 | 11.85 | 8–0 | 8.0 | ≥+3.00 | 0.0 | 97% |
| 2 | Harvard University | 4.20 | 6.28 | 6–2 | 5.0 | +2.08 | 1.1 | 35% |
| 3 | Bentley University | 2.69 | 3.78 | 4–4 | 2.7 | +1.09 | 1.0 | 22% |
| 4 | Fairfield University | 2.75 | 2.17 | 2–6 | 2.7 | −0.58 | 0.3 | 86% |
| 5 | Massachusetts Maritime Academy | 2.11 | -0.89 | 0–8 | 1.6 | ≤−3.00 | 2.7 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Sarra Guezguez | Harvard University | 1.02 | 3.09 | 6–2 | 5.0 | +2.08 | 1.1 | 35% |
| Xavier Ayala Vermont | Harvard University | 1.22 | 3.30 | 6–2 | 5.0 | +2.08 | 1.1 | 35% |
| Andrew Blagden | Bentley University | 0.67 | 1.76 | 4–4 | 2.7 | +1.09 | 1.0 | 22% |
| John O'Connell | Bentley University | 0.88 | 1.97 | 4–4 | 2.7 | +1.09 | 1.0 | 22% |
| Wilfred Hynes | Bentley University | 0.76 | 1.85 | 4–4 | 2.7 | +1.09 | 1.0 | 22% |
| Santiago Pastor | Fairfield University | 0.39 | -0.18 | 2–6 | 2.7 | −0.58 | 0.3 | 86% |
| Kevin Mcneill | Massachusetts Maritime Academy | 0.82 | -2.18 | 0–8 | 1.6 | ≤−3.00 | 2.7 | >99% |
| Joseph Gedraitis | Massachusetts Maritime Academy | -0.04 | -3.04 | 0–8 | 1.6 | ≤−3.00 | 2.7 | >99% |
| Cole Capizzo | Massachusetts Maritime Academy | 0.70 | -2.30 | 0–8 | 1.6 | ≤−3.00 | 2.7 | >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 | Falcons | Stags | Buccaneers | |
|---|---|---|---|---|---|---|---|
| Brown University Bears | 4–0 | ✕ | W | W | W | W |
| Harvard University Crimson | 3–1 | L | ✕ | W | W | W |
| Bentley University Falcons | 2–2 | L | L | ✕ | W | W |
| Fairfield University Stags | 1–3 | L | L | L | ✕ | W |
| Massachusetts Maritime Academy Buccaneers | 0–4 | L | L | L | L | ✕ |
| Team | Rec. | Bears | Crimson | Falcons | Stags | Buccaneers | |
|---|---|---|---|---|---|---|---|
| Brown University Bears | 4–0 | ✕ | W | W | W | W |
| Harvard University Crimson | 3–1 | L | ✕ | W | W | W |
| Bentley University Falcons | 2–2 | L | L | ✕ | W | W |
| Fairfield University Stags | 1–3 | L | L | L | ✕ | W |
| Massachusetts Maritime Academy Buccaneers | 0–4 | L | L | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Bears | 1-3-4 | def. | |
Stags | 2-5-6 |
| 2 | |
Crimson | 1-2-4 | def. | |
Falcons | 3-5-6 |
| 3 | |
Bears | 1-2-3 | def. | |
Buccaneers | 4-5-6 |
| 4 | |
Falcons | 1-2-3 | def. | |
Stags | 4-5-6 |
| 5 | |
Crimson | 1-2-6 | def. | |
Buccaneers | 3-4-5 |
| 6 | |
Bears | 1-2-3 | def. | |
Falcons | 4-5-6 |
| 7 | |
Crimson | 1-2-4 | def. | |
Stags | 3-5-6 |
| 8 | |
Falcons | 1-2-4 | def. | |
Buccaneers | 3-5-6 |
| 9 | |
Bears | 1-2-3 | def. | |
Crimson | 4-5-6 |
| 10 | |
Stags | 1-2-6 | def. | |
Buccaneers | 3-4-5 |
| 11 | |
Falcons | 2-3-4 | def. | |
Stags | 1-5-6 |
| 12 | |
Bears | 1-2-3 | def. | |
Crimson | 4-5-6 |
| 13 | |
Falcons | 1-2-5 | def. | |
Buccaneers | 3-4-6 |
| 14 | |
Bears | 1-2-4 | def. | |
Stags | 3-5-6 |
| 15 | |
Crimson | 1-2-3 | def. | |
Buccaneers | 4-5-6 |
| 16 | |
Bears | 1-2-4 | def. | |
Falcons | 3-5-6 |
| 17 | |
Crimson | 1-2-5 | def. | |
Stags | 3-4-6 |
| 18 | |
Bears | 1-2-3 | def. | |
Buccaneers | 4-5-6 |
| 19 | |
Crimson | 1-3-6 | def. | |
Falcons | 2-4-5 |
| 20 | |
Stags | 1-2-3 | def. | |
Buccaneers | 4-5-6 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
Brown University | Bears | Emery Diemar '28 Julian Dahiya '28 Tyler Lamm '28 |
Phoebe Hackett '28 Sasha Gordon '28 Janelle Li '25 |
|
Harvard University | Crimson | Sarra Guezguez '27 William Bourell '28 Xavier Ayala Vermont '27 |
Catherine Mignone '26 Hanah Youn '28 Julia Downey '27 |
|
Bentley University | Falcons | Andrew Blagden '28 John O'Connell '28 Wilfred Hynes '26 |
Grace Kokkotos '27 Isabel Walchli '28 Lucy Butterfield '28 |
|
Fairfield University | Stags | Nolan Cooper '25 Santiago Pastor '27 Timothy Cronin '27 |
Sawyer Obrien '27 Mason Macuch '28 Morgan Martucci '27 |
|
Massachusetts Maritime Academy | Buccaneers | Cole Capizzo '27 Joseph Gedraitis '27 Kevin Mcneill '28 |
Gabriella-Marie Diez '28 Henry Steadman '28 Hugo Langlais '28 |
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 ±0.14 means a rating gap counts about 12% 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.
