| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
U. S. Coast Guard Academy | Bears | 7–2 |
| 2 |  |
University of Rhode Island | Rams | 6–3 |
| 3 |  |
Northeastern University | Huskies | 3–6 |
| 4 |  |
Connecticut College | Camels | 2–7 |
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 29,944 team race matches sailed before 2026-03-28, using the line-ups that actually raced. It called 14 of 18 matches (78%) with a log-loss of 0.585 (a coin flip scores 0.693), so the event went about as expected for the forecast. Orderliness +0.19 ± 0.59: a typical event, more orderly than 65% 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 | U. S. Coast Guard Academy Bears | 1 | 7.75 school +0.11, home +0.36 | Julia Conneely / Olivia Spada 2.84 Meara Conley / Allison Nystrom 2.10 Madeline Murphy / Grace Boudreau 2.27 5–1 Madeline Murphy / Ella Demand 2.40 2–1 | 77% | 7–2 (78%) | +0.0 |
| 2 | University of Rhode Island Rams | 2 | 6.67 | Ariana Schwartz / Harper Kelley 1.84 Emaline Ouellette / Savannah Miner 1.96 Kytalin Hendrickson / Erin Little 2.88 | 46% | 6–3 (67%) | +1.9 |
| 3 | Northeastern University Huskies | 3 | 6.58 school +0.24 | Eva Ermlich / Nathalie Caudron 3.00 Lydia Frost / Kaelyn Holmes 1.90 Gabrielle Ahitow / Kasia Piekielniak 1.38 | 43% | 3–6 (33%) | −0.9 |
| 4 | Connecticut College Camels | 4 | 6.28 school +0.27 | Natalie Fear / Madeleine Jarbeau 2.15 Julia Scipione / Alexandra Daigle 2.11 Lilly Saffer / Cecilia Roe 1.68 1–5 Lilly Saffer / June Roe 1.69 1–2 | 33% | 2–7 (22%) | −1.0 |
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) |
|---|---|---|---|---|---|---|---|---|
| 2 | University of Rhode Island | 6.67 | 7.61 | 6–3 | 4.1 | +0.94 | 1.3 | 17% |
| 1 | U. S. Coast Guard Academy | 7.75 | 7.77 | 7–2 | 7.0 | +0.02 | 0.0 | 67% |
| 3 | Northeastern University | 6.58 | 6.12 | 3–6 | 3.9 | −0.46 | 0.3 | 84% |
| 4 | Connecticut College | 6.28 | 5.68 | 2–7 | 3.0 | −0.60 | 0.4 | 87% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Ariana Schwartz | University of Rhode Island | 1.84 | 2.78 | 6–3 | 4.1 | +0.94 | 1.3 | 17% |
| Emaline Ouellette | University of Rhode Island | 1.96 | 2.90 | 6–3 | 4.1 | +0.94 | 1.3 | 17% |
| Kytalin Hendrickson | University of Rhode Island | 2.88 | 3.82 | 6–3 | 4.1 | +0.94 | 1.3 | 17% |
| Julia Conneely | U. S. Coast Guard Academy | 2.84 | 2.87 | 7–2 | 7.0 | +0.02 | 0.0 | 67% |
| Meara Conley | U. S. Coast Guard Academy | 2.10 | 2.12 | 7–2 | 7.0 | +0.02 | 0.0 | 67% |
| Madeline Murphy | U. S. Coast Guard Academy | 2.27 | 2.29 | 7–2 | 7.0 | +0.02 | 0.0 | 67% |
| Eva Ermlich | Northeastern University | 3.00 | 2.54 | 3–6 | 3.9 | −0.46 | 0.3 | 84% |
| Lydia Frost | Northeastern University | 1.90 | 1.44 | 3–6 | 3.9 | −0.46 | 0.3 | 84% |
| Gabrielle Ahitow | Northeastern University | 1.38 | 0.92 | 3–6 | 3.9 | −0.46 | 0.3 | 84% |
| Lilly Saffer | Connecticut College | 1.68 | 1.09 | 2–7 | 3.0 | −0.60 | 0.4 | 87% |
| Natalie Fear | Connecticut College | 2.15 | 1.55 | 2–7 | 3.0 | −0.60 | 0.4 | 87% |
| Julia Scipione | Connecticut College | 2.11 | 1.52 | 2–7 | 3.0 | −0.60 | 0.4 | 87% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | Bears | Rams | Huskies | Camels | |
|---|---|---|---|---|---|---|
| U. S. Coast Guard Academy Bears | 3–0 | ✕ | W | W | W |
| University of Rhode Island Rams | 1–2 | L | ✕ | L | W |
| Northeastern University Huskies | 1–2 | L | W | ✕ | L |
| Connecticut College Camels | 1–2 | L | L | W | ✕ |
| Team | Rec. | Bears | Rams | Huskies | Camels | |
|---|---|---|---|---|---|---|
| U. S. Coast Guard Academy Bears | 1–2 | ✕ | L | W | L |
| University of Rhode Island Rams | 3–0 | W | ✕ | W | W |
| Northeastern University Huskies | 1–2 | L | L | ✕ | W |
| Connecticut College Camels | 1–2 | W | L | L | ✕ |
| Team | Rec. | Bears | Rams | Huskies | Camels | |
|---|---|---|---|---|---|---|
| U. S. Coast Guard Academy Bears | 3–0 | ✕ | W | W | W |
| University of Rhode Island Rams | 2–1 | L | ✕ | W | W |
| Northeastern University Huskies | 1–2 | L | L | ✕ | W |
| Connecticut College Camels | 0–3 | L | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Bears | 1-4-5 | def. | |
Rams | 2-3-6 |
| 2 | |
Camels | 1-2-6 | def. | |
Huskies | 3-4-5 |
| 3 | |
Bears | 1-3-4 | def. | |
Huskies | 2-5-6 |
| 4 | |
Rams | 2-3-4 | def. | |
Camels | 1-5-6 |
| 5 | |
Huskies | 1-2-5 | def. | |
Rams | 3-4-6 |
| 6 | |
Bears | 1-2-5 | def. | |
Camels | 3-4-6 |
| 7 | |
Rams | 1-2-5 | def. | |
Bears | 3-4-6 |
| 8 | |
Huskies | 1-2-6 | def. | |
Camels | 3-4-5 |
| 9 | |
Bears | 1-2-5 | def. | |
Huskies | 3-4-6 |
| 10 | |
Rams | 2-3-4 | def. | |
Camels | 1-5-6 |
| 11 | |
Rams | 1-2-3 | def. | |
Huskies | 4-5-6 |
| 12 | |
Camels | 1-3-5 | def. | |
Bears | 2-4-6 |
| 13 | |
Bears | 1-2-5 | def. | |
Rams | 3-4-6 |
| 14 | |
Huskies | 1-2-5 | def. | |
Camels | 3-4-6 |
| 15 | |
Bears | 1-2-5 | def. | |
Huskies | 3-4-6 |
| 16 | |
Rams | 1-2-5 | def. | |
Camels | 3-4-6 |
| 17 | |
Rams | 2-3-5 | def. | |
Huskies | 1-4-6 |
| 18 | |
Bears | 1-3-4 | def. | |
Camels | 2-5-6 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
U. S. Coast Guard Academy | Bears | Julia Conneely '26 Madeline Murphy '29 Meara Conley '28 |
Olivia Spada '28 Grace Boudreau '29 Ella Demand '28 Allison Nystrom '26 |
|
University of Rhode Island | Rams | Ariana Schwartz '27 Emaline Ouellette '28 Kytalin Hendrickson '26 |
Harper Kelley '29 Savannah Miner '28 Erin Little '28 |
|
Northeastern University | Huskies | Eva Ermlich '26 Gabrielle Ahitow '28 Lydia Frost '29 |
Nathalie Caudron '29 Kasia Piekielniak '29 Kaelyn Holmes '29 |
|
Connecticut College | Camels | Julia Scipione '29 Lilly Saffer '27 Natalie Fear '28 |
Alexandra Daigle '29 June Roe '28 Cecilia Roe '26 Madeleine Jarbeau '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 ±0.05 means a rating gap counts about 5% 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.
