| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 | Yale University | Bulldogs | 11–4 | |
| 2 | Brown University | Bears | 11–4 | |
| 3 | Massachusetts Institute of Technology | Engineers 1 | 10–5 | |
| 4 | Harvard University | Crimson | 8–7 | |
| 5 | Dartmouth College | Big Green | 3–12 | |
| 6 | Massachusetts Institute of Technology | Engineers 2 | 2–13 |
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 16,158 team race matches sailed before 2022-03-05, using the line-ups that actually raced. It called 35 of 45 matches (78%) with a log-loss of 0.542 (a coin flip scores 0.693), so the event went about as expected for the forecast. Orderliness +0.15 ± 0.33: a typical event, more orderly than 59% 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) |
|---|---|---|---|---|---|---|---|---|
| 3 | Massachusetts Institute of Technology Engineers 1 | 8.27 | 9.30 | 10–5 | 6.8 | +1.03 | 2.3 | 6% |
| 2 | Brown University Bears | 9.09 | 9.45 | 11–4 | 10.0 | +0.36 | 0.3 | 39% |
| 4 | Harvard University Crimson | 8.29 | 8.60 | 8–7 | 7.0 | +0.31 | 0.2 | 39% |
| 1 | Yale University Bulldogs | 9.57 | 9.27 | 11–4 | 11.8 | −0.30 | 0.2 | 80% |
| 6 | Massachusetts Institute of Technology Engineers 2 | 7.15 | 6.72 | 2–13 | 2.8 | −0.43 | 0.3 | 81% |
| 5 | Dartmouth College Big Green | 8.15 | 6.85 | 3–12 | 6.6 | −1.30 | 3.1 | 99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Cordelia Burn | Harvard University | 2.06 | 2.37 | 8–7 | 7.0 | +0.31 | 0.2 | 39% |
| Sarah Burn | Harvard University | 2.84 | 3.15 | 8–7 | 7.0 | +0.31 | 0.2 | 39% |
| Emma Cowles | Yale University | 3.05 | 2.75 | 11–4 | 11.8 | −0.30 | 0.2 | 80% |
| Carmen Cowles | Yale University | 3.25 | 2.95 | 11–4 | 11.8 | −0.30 | 0.2 | 80% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | Bulldogs | Bears | 1 | Crimson | Green | 2 | |
|---|---|---|---|---|---|---|---|---|
| Yale University Bulldogs | 3–2 | ✕ | L | W | L | W | W | |
| Brown University Bears | 4–1 | W | ✕ | W | W | W | W | |
| Massachusetts Institute of Technology Engineers 1 | 4–6 | L | L | ✕ | W | L | ✕ | |
| Harvard University Crimson | 3–2 | W | L | L | ✕ | W | L | |
| Dartmouth College Big Green | 1–4 | L | L | W | L | ✕ | W | |
| Massachusetts Institute of Technology Engineers 2 | 4–6 | L | L | ✕ | W | L | ✕ |
| Team | Rec. | Bulldogs | Bears | 1 | Crimson | Green | 2 | |
|---|---|---|---|---|---|---|---|---|
| Yale University Bulldogs | 4–1 | ✕ | W | W | L | W | W | |
| Brown University Bears | 3–2 | L | ✕ | W | W | W | W | |
| Massachusetts Institute of Technology Engineers 1 | 4–6 | L | L | ✕ | L | W | ✕ | |
| Harvard University Crimson | 3–2 | W | L | W | ✕ | L | W | |
| Dartmouth College Big Green | 1–4 | L | L | L | W | ✕ | L | |
| Massachusetts Institute of Technology Engineers 2 | 4–6 | L | L | ✕ | L | W | ✕ |
| Team | Rec. | Bulldogs | Bears | 1 | Crimson | Green | 2 | |
|---|---|---|---|---|---|---|---|---|
| Yale University Bulldogs | 4–1 | ✕ | W | W | W | W | W | |
| Brown University Bears | 4–1 | L | ✕ | W | W | W | W | |
| Massachusetts Institute of Technology Engineers 1 | 4–6 | L | L | ✕ | L | W | ✕ | |
| Harvard University Crimson | 2–3 | L | L | W | ✕ | W | W | |
| Dartmouth College Big Green | 1–4 | L | L | L | L | ✕ | L | |
| Massachusetts Institute of Technology Engineers 2 | 4–6 | L | L | ✕ | L | W | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | Crimson | 2-3-4 | def. | Big Green | 1-5-6 | ||
| 2 | Engineers 1 | 1-2-6 | def. | Bears | 3-4-5 | ||
| 3 | Bulldogs | 1-2-3 | def. | Engineers 2 | 4-5-6 | ||
| 7 | Bears | 1-2-5 | def. | Crimson | 3-4-6 | ||
| 8 | Bulldogs | 1-2-3 | def. | Big Green | 4-5-6 | ||
| 9 | Engineers 1 | 1-2-5 | def. | Engineers 2 | 3-4-6 | ||
| 13 | Crimson | 1-3-4 | def. | Bulldogs | 2-5-6 | ||
| 14 | Bears | 1-3-4 | def. | Engineers 2 | 2-5-6 | ||
| 15 | Engineers 1 | 1-3-5 | def. | Big Green | 2-4-6 | ||
| 19 | Bears | 1-2-4 | def. | Bulldogs | 3-5-6 | ||
| 20 | Big Green | 1-2-5 | def. | Engineers 2 | 3-4-6 | ||
| 21 | Crimson | 1-2-6 | def. | Engineers 1 | 3-4-5 | ||
| 25 | Bears | 1-2-4 | def. | Big Green | 3-5-6 | ||
| 26 | Bulldogs | 1-2-3 | def. | Engineers 1 | 4-5-6 | ||
| 27 | Engineers 2 | 1-2-6 | def. | Crimson | 3-4-5 | ||
| 4 | Big Green | 1-2-4 | def. | Crimson | 3-5-6 | ||
| 5 | Engineers 1 | 2-3-4 | def. | Bears | 1-5-6 | ||
| 6 | Bulldogs | 1-3-4 | def. | Engineers 2 | 2-5-6 | ||
| 10 | Bears | 1-2-5 | def. | Crimson | 3-4-6 | ||
| 11 | Bulldogs | 1-2-6 | def. | Big Green | 3-4-5 | ||
| 12 | Engineers 1 | 1-2-5 | def. | Engineers 2 | 3-4-6 | ||
| 16 | Crimson | 1-3-5 | def. | Bulldogs | 2-4-6 | ||
| 17 | Bears | 1-2-3 | def. | Engineers 2 | 4-5-6 | ||
| 18 | Engineers 1 | 1-3-4 | def. | Big Green | 2-5-6 | ||
| 22 | Bulldogs | 1-2-6 | def. | Bears | 3-4-5 | ||
| 23 | Engineers 2 | 2-3-5 | def. | Big Green | 1-4-6 | ||
| 24 | Crimson | 1-2-4 | def. | Engineers 1 | 3-5-6 | ||
| 28 | Bears | 1-3-6 | def. | Big Green | 2-4-5 | ||
| 29 | Bulldogs | 1-2-5 | def. | Engineers 1 | 3-4-6 | ||
| 30 | Crimson | 2-3-4 | def. | Engineers 2 | 1-5-6 | ||
| 31 | Bulldogs | 1-2-3 | def. | Bears | 4-5-6 | ||
| 32 | Engineers 1 | 2-3-4 | def. | Crimson | 1-5-6 | ||
| 33 | Big Green | 1-2-4 | def. | Engineers 2 | 3-5-6 | ||
| 34 | Engineers 1 | 1-3-6 | def. | Bulldogs | 2-4-5 | ||
| 35 | Bears | 1-2-4 | def. | Engineers 2 | 3-5-6 | ||
| 36 | Crimson | 2-3-4 | def. | Big Green | 1-5-6 | ||
| 37 | Bulldogs | 1-2-3 | def. | Engineers 2 | 4-5-6 | ||
| 38 | Engineers 1 | 1-2-5 | def. | Big Green | 3-4-6 | ||
| 39 | Bears | 1-3-6 | def. | Crimson | 2-4-5 | ||
| 40 | Engineers 1 | 1-2-5 | def. | Engineers 2 | 3-4-6 | ||
| 41 | Bears | 1-4-5 | def. | Big Green | 2-3-6 | ||
| 42 | Bulldogs | 2-3-4 | def. | Crimson | 1-5-6 | ||
| 43 | Bears | 2-3-6 | def. | Engineers 1 | 1-4-5 DSQ | ||
| 44 | Crimson | 1-3-6 DNF | def. | Engineers 2 | 2-4-5 | ||
| 45 | Bulldogs | 2-3-4 | def. | Big Green | 1-5-6 | ||
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
| Yale University | Bulldogs | Carmen Cowles '25 Emma Cowles '25 Megan Grimes '24 |
Ximena Escobar '25 Helena Ware '23 Becca Rose '22 Meredith Ryan '23 |
|
| Brown University | Bears | Blaire Mccarthy '23 Brielle Willoughby '24 Caroline Bayless '23 |
Savannah Young '25 Catherine Morrissette '24 Nora Ong '23 |
|
| Massachusetts Institute of Technology | Engineers 1 | Brooke Schmelz '25 Dana Haig '22 Kaila Pfrang '21 |
Grace Mao '23 Shayne Bersin '25 Maile Jim '22 Veronika Silkin '22 |
|
| Harvard University | Crimson | Cordelia Burn '25 Marbella Marlo '24 Sarah Burn '24 |
Cindy Tian '23 Juliana Ruggieri '23 Corinne Lee '23 |
|
| Dartmouth College | Big Green | Gray Hemans '25 Sarah Young '25 Taylor Eastman '24 |
Caroline Oyster '24 Reese Bragg '25 Eliza Crocker '23 |
|
| Massachusetts Institute of Technology | Engineers 2 | Emily Scherer '23 Lucy Brock '25 Lulu Russell '22 |
Mercedes Escandon '25 Sophie Yang '24 Wendy Wu '23 Zoe Fisher '23 |
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% 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.