| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
U. S. Coast Guard Academy | Bears | 6–3 |
| 2 |  |
SUNY Maritime College | Privateers | 6–3 |
| 3 |  |
Fordham University | Rams | 6–3 |
| 4 |  |
Washington College | Shoremen | 0–9 |
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 29,369 team race matches sailed before 2026-03-21, using the line-ups that actually raced. It called 14 of 18 matches (78%) with a log-loss of 0.493 (a coin flip scores 0.693), so the event went about as expected for the forecast. Orderliness −0.52 ± 0.41: an upset-heavy event, more orderly than 10% 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 | 6.28 school +0.10 | Caroline Odell / Will Bryant 1.92 David Vinogradov / Gabriella Fields 2.31 William Clay / Grace Boudreau | 88% | 6–3 (67%) | −1.9 |
| 2 | SUNY Maritime College Privateers | 2 | 5.18 home +0.35 | Nathaniel Hartwell / Michael Saharov 1.89 Jeremy Lunati / Juliana Sarich 1.44 Luke Barker / Amelia Tebbens 1.43 | 66% | 6–3 (67%) | +0.1 |
| 3 | Fordham University Rams | 3 | 4.08 school −0.06 | Michael Burns / Campbell Carr 1.60 Jonathan Wittick / Payton Buckley 1.45 Elfi Van Kuilenburg / John Bender | 43% | 6–3 (67%) | +2.1 |
| 4 | Washington College Shoremen | 4 | 1.32 school −0.14 | Alexander Salzberg / Rachel Morgen 1.32 George Wood / Nora Ciak 0.41 Matthew Nadolny / Soledad Fintel -0.36 | 3% | 0–9 (0%) | −0.3 |
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) |
|---|---|---|---|---|---|---|---|---|
| 3 | Fordham University | 4.08 | 5.76 | 6–3 | 3.9 | +1.67 | 2.7 | 6% |
| 2 | SUNY Maritime College | 5.18 | 5.23 | 6–3 | 5.9 | +0.05 | 0.0 | 67% |
| 1 | U. S. Coast Guard Academy | 6.28 | 4.71 | 6–3 | 7.9 | −1.58 | 2.4 | 99% |
| 4 | Washington College | 1.32 | -1.68 | 0–9 | 0.3 | ≤−3.00 | 0.4 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Michael Burns | Fordham University | 1.60 | 3.28 | 6–3 | 3.9 | +1.67 | 2.7 | 6% |
| Jonathan Wittick | Fordham University | 1.45 | 3.13 | 6–3 | 3.9 | +1.67 | 2.7 | 6% |
| Elfi Van Kuilenburg | Fordham University | 0.90 | 2.57 | 6–3 | 3.9 | +1.67 | 2.7 | 6% |
| Nathaniel Hartwell | SUNY Maritime College | 1.89 | 1.94 | 6–3 | 5.9 | +0.05 | 0.0 | 67% |
| Jeremy Lunati | SUNY Maritime College | 1.44 | 1.49 | 6–3 | 5.9 | +0.05 | 0.0 | 67% |
| Luke Barker | SUNY Maritime College | 1.43 | 1.48 | 6–3 | 5.9 | +0.05 | 0.0 | 67% |
| Caroline Odell | U. S. Coast Guard Academy | 1.92 | 0.34 | 6–3 | 7.9 | −1.58 | 2.4 | 99% |
| David Vinogradov | U. S. Coast Guard Academy | 2.31 | 0.73 | 6–3 | 7.9 | −1.58 | 2.4 | 99% |
| William Clay | U. S. Coast Guard Academy | 1.82 | 0.25 | 6–3 | 7.9 | −1.58 | 2.4 | 99% |
| Alexander Salzberg | Washington College | 1.32 | -1.68 | 0–9 | 0.3 | ≤−3.00 | 0.4 | >99% |
| George Wood | Washington College | 0.41 | -2.59 | 0–9 | 0.3 | ≤−3.00 | 0.4 | >99% |
| Matthew Nadolny | Washington College | -0.36 | -3.37 | 0–9 | 0.3 | ≤−3.00 | 0.4 | >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 | Privateers | Rams | Shoremen | |
|---|---|---|---|---|---|---|
| U. S. Coast Guard Academy Bears | 2–1 | ✕ | W | L | W |
| SUNY Maritime College Privateers | 2–1 | L | ✕ | W | W |
| Fordham University Rams | 2–1 | W | L | ✕ | W |
| Washington College Shoremen | 0–3 | L | L | L | ✕ |
| Team | Rec. | Bears | Privateers | Rams | Shoremen | |
|---|---|---|---|---|---|---|
| U. S. Coast Guard Academy Bears | 2–1 | ✕ | L | W | W |
| SUNY Maritime College Privateers | 2–1 | W | ✕ | L | W |
| Fordham University Rams | 2–1 | L | W | ✕ | W |
| Washington College Shoremen | 0–3 | L | L | L | ✕ |
| Team | Rec. | Bears | Privateers | Rams | Shoremen | |
|---|---|---|---|---|---|---|
| U. S. Coast Guard Academy Bears | 2–1 | ✕ | W | L | W |
| SUNY Maritime College Privateers | 2–1 | L | ✕ | W | W |
| Fordham University Rams | 2–1 | W | L | ✕ | W |
| Washington College Shoremen | 0–3 | L | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Privateers | 1-2-4 | def. | |
Rams | 3-5-6 |
| 2 | |
Bears | 1-2-3 | def. | |
Shoremen | 4-5-6 |
| 3 | |
Rams | 1-2-3 | def. | |
Shoremen | 4-5-6 |
| 4 | |
Bears | 1-4-5 | def. | |
Privateers | 2-3-6 |
| 5 | |
Privateers | 1-2-6 | def. | |
Shoremen | 3-4-5 |
| 6 | |
Rams | 1-3-5 | def. | |
Bears | 2-4-6 |
| 7 | |
Bears | 1-2-6 | def. | |
Rams | 3-4-5 |
| 8 | |
Privateers | 1-2-6 | def. | |
Shoremen | 3-4-5 |
| 9 | |
Privateers | 1-3-6 | def. | |
Bears | 2-4-5 |
| 10 | |
Rams | 1-2-5 | def. | |
Shoremen | 3-4-6 |
| 11 | |
Rams | 2-3-4 | def. | |
Privateers | 1-5-6 |
| 12 | |
Bears | 1-2-3 | def. | |
Shoremen | 4-5-6 |
| 13 | |
Privateers | 1-2-5 | def. | |
Shoremen | 3-4-6 |
| 14 | |
Rams | 2-3-5 | def. | |
Bears | 1-4-6 |
| 15 | |
Bears | 1-2-3 | def. | |
Privateers | 4-5-6 |
| 16 | |
Rams | 1-2-4 | def. | |
Shoremen | 3-5-6 |
| 17 | |
Bears | 1-2-4 | def. | |
Shoremen | 3-5-6 |
| 18 | |
Privateers | 1-2-6 | def. | |
Rams | 3-4-5 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
U. S. Coast Guard Academy | Bears | Caroline Odell '27 David Vinogradov '28 William Clay '29 |
Will Bryant '29 Gabriella Fields '28 Grace Boudreau '29 |
|
SUNY Maritime College | Privateers | Jeremy Lunati '27 Luke Barker '28 Nathaniel Hartwell '28 |
Juliana Sarich '28 Amelia Tebbens '29 Michael Saharov '28 |
|
Fordham University | Rams | Elfi Van Kuilenburg '29 Jonathan Wittick '29 Michael Burns '29 |
John Bender '29 Payton Buckley '28 Campbell Carr '29 |
|
Washington College | Shoremen | Alexander Salzberg '29 George Wood '27 Matthew Nadolny '27 |
Rachel Morgen '26 Nora Ciak '27 Soledad Fintel '29 |
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% 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.
