| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
Brown University | Bears 1 | 4–1 |
| 2 | |
Brown University | Bears 2 | 4–1 |
| 3 |  |
Boston University | Terriers | 3–2 |
| 4 |  |
Dartmouth College | Big Green | 2–3 |
| 5 |  |
U. S. Coast Guard Academy | Bears | 1–4 |
| 6 |  |
Roger Williams University | Hawks | 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 23,158 team race matches sailed before 2024-03-23, using the line-ups that actually raced. It called 8 of 11 matches (73%) with a log-loss of 0.490 (a coin flip scores 0.693), so the event went about as expected for the forecast. Orderliness +0.44 ± 0.70: a typical event, more orderly than 89% 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 | 8.71 | 11.71 | 4–0 | 2.9 | ≥+3.00 | 1.8 | 27% |
| 2 | Brown University Bears 2 | 8.24 | 8.95 | 3–1 | 2.5 | +0.72 | 0.3 | 50% |
| 5 | U. S. Coast Guard Academy Bears | 6.17 | 6.37 | 1–3 | 0.9 | +0.20 | 0.0 | 65% |
| 3 | Boston University Terriers | 7.83 | 7.53 | 2–2 | 2.3 | −0.30 | 0.1 | 79% |
| 6 | Roger Williams University Hawks | 6.99 | 6.02 | 1–4 | 1.8 | −0.98 | 0.5 | 92% |
| 4 | Dartmouth College Big Green | 7.21 | 4.21 | 0–1 | 0.6 | ≤−3.00 | 1.1 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Martins Atilla | Brown University | 2.72 | 3.44 | 3–1 | 2.5 | +0.72 | 0.3 | 50% |
| Allison Nystrom | U. S. Coast Guard Academy | 1.46 | 1.66 | 1–3 | 0.9 | +0.20 | 0.0 | 65% |
| Elliott Mendenhall | Boston University | 2.55 | 1.58 | 1–2 | 1.7 | −0.97 | 0.5 | 92% |
| Jack Crager | Roger Williams University | 1.90 | 0.92 | 1–4 | 1.8 | −0.98 | 0.5 | 92% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Rd | # | Winner | Loser | P(winner) | |
|---|---|---|---|---|---|
| 1 | 1 | Roger Williams University Hawks | Dartmouth College Big Green | 43% | upset |
| 1 | 2 | Boston University Terriers | U. S. Coast Guard Academy Bears | 62% | |
| 1 | 3 | Brown University Bears 1 | Brown University Bears 2 | 62% | |
| 1 | 5 | Brown University Bears 2 | Roger Williams University Hawks | 78% | |
| 1 | 6 | Brown University Bears 1 | Boston University Terriers | 56% | |
| 1 | 7 | Brown University Bears 2 | U. S. Coast Guard Academy Bears | 89% | |
| 1 | 9 | Brown University Bears 1 | Roger Williams University Hawks | 85% | |
| 1 | 10 | Brown University Bears 2 | Boston University Terriers | 45% | upset |
| 1 | 11 | U. S. Coast Guard Academy Bears | Roger Williams University Hawks | 30% | upset |
| 1 | 13 | Boston University Terriers | Roger Williams University Hawks | 68% | |
| 1 | 15 | Brown University Bears 1 | U. S. Coast Guard Academy Bears | 93% |
| Team | Rec. | 1 | 2 | Terriers | Green | Bears | Hawks | |
|---|---|---|---|---|---|---|---|---|
| Brown University Bears 1 | 8–2 | ✕ | ✕ | W | W | W | W |
| Brown University Bears 2 | 8–2 | ✕ | ✕ | W | W | W | W |
| Boston University Terriers | 3–2 | L | L | ✕ | W | W | W |
| Dartmouth College Big Green | 2–3 | L | L | L | ✕ | W | L |
| U. S. Coast Guard Academy Bears | 1–4 | L | L | L | L | ✕ | W |
| Roger Williams University Hawks | 1–4 | L | L | L | W | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Hawks | 1-2-5 | def. | |
Big Green | 3-4-6 |
| 2 | |
Terriers | 1-2-5 | def. | |
Bears | 3-4-6 |
| 3 | |
Bears 1 | 1-2-6 | def. | |
Bears 2 | 3-4-5 |
| 4 | |
Big Green | 1-3-6 | def. | |
Bears | 2-4-5 |
| 5 | |
Bears 2 | 1-2-5 | def. | |
Hawks | 3-4-6 |
| 6 | |
Bears 1 | 1-3-5 | def. | |
Terriers | 2-4-6 |
| 7 | |
Bears 2 | 1-2-3 | def. | |
Bears | 4-5-6 |
| 8 | |
Terriers | 1-2-5 | def. | |
Big Green | 3-4-6 |
| 9 | |
Bears 1 | 1-2-5 | def. | |
Hawks | 3-4-6 |
| 10 | |
Bears 2 | 1-2-3 | def. | |
Terriers | 4-5-6 |
| 11 | |
Bears | 1-3-6 | def. | |
Hawks | 2-4-5 |
| 12 | |
Big Green | 1-2-6 | def. | |
Bears 1 | 3-4-5 |
| 13 | |
Terriers | 1-3-5 | def. | |
Hawks | 2-4-6 |
| 14 | |
Bears 2 | 1-2-5 | def. | |
Big Green | 3-4-6 |
| 15 | |
Bears 1 | 1-2-3 | def. | |
Bears | 4-5-6 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
Brown University | Bears 1 | Blake Behrens '27 Connor Macken '24 Mason Stang '25 |
Catherine Morrissette '24 Sierra Bornheim '24 Olivia Miller '25 |
|
Brown University | Bears 2 | Ethan Simpson '27 Laura Hamilton '27 Martins Atilla '27 |
Janelle Li '25 Sidney Moyer '27 Talia Levine '26 |
|
Boston University | Terriers | Elliott Mendenhall '25 Gavin Monaghan '26 Nathan Selian '25 Noah Robitshek '25 Richard Kalich '26 |
Morgan Contrino '24 Nicholas Thibodeau '26 Lachlan Hooper '25 Adam Elci '26 Sam Whipple '25 |
|
Dartmouth College | Big Green | Ava Hurwitz '27 Ben Sheppard '27 Oliver Hurwitz '25 |
Xander Dalke '27 Sophie Menyhart '27 Vivian Heitkoetter '27 |
|
U. S. Coast Guard Academy | Bears | Allison Nystrom '26 Julia Conneely '26 Kevin Gosselin '27 |
Julian Henderson '27 Grace Austin '27 Caroline Odell '27 |
|
Roger Williams University | Hawks | Jack Crager '27 Luke Hosek '24 William Bailey '24 |
Demetry Spinola '26 Jakub Fuja '27 Frankie Sparrow '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 ±0.01 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.
