| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
Tufts University | Jumbos | 7–0 |
| 2 |  |
Brown University | Bears | 6–1 |
| 3 |  |
Massachusetts Institute of Technology | Engineers | 5–2 |
| 4 |  |
Harvard University | Crimson | 3–4 |
| 5 |  |
Northeastern University | Huskies | 3–4 |
| 6 |  |
University of Rhode Island | Rams | 3–4 |
| 7 |  |
University of Vermont | Catamounts | 1–6 |
| 8 |  |
Northwestern University | Wildcats | 0–7 |
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 16,723 team race matches sailed before 2022-03-12, using the line-ups that actually raced. It called 24 of 28 matches (86%) with a log-loss of 0.265 (a coin flip scores 0.693), so the event went well for the forecast. Orderliness +0.33 ± 0.34: a typical event, more orderly than 78% 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 | Tufts University | 10.03 | 13.03 | 7–0 | 5.5 | ≥+3.00 | 2.8 | 13% |
| 3 | Massachusetts Institute of Technology | 8.48 | 9.84 | 5–2 | 3.6 | +1.35 | 1.4 | 18% |
| 5 | Northeastern University | 7.87 | 7.87 | 3–4 | 3.0 | +0.00 | 0.0 | 69% |
| 7 | University of Vermont | 5.75 | 5.67 | 1–6 | 1.0 | −0.08 | 0.0 | 82% |
| 2 | Brown University | 10.90 | 10.35 | 6–1 | 6.3 | −0.55 | 0.1 | 88% |
| 6 | University of Rhode Island | 8.52 | 7.83 | 3–4 | 3.7 | −0.69 | 0.3 | 89% |
| 4 | Harvard University | 9.10 | 7.71 | 3–4 | 4.4 | −1.39 | 1.4 | 97% |
| 8 | Northwestern University | 5.32 | 2.32 | 0–7 | 0.4 | ≤−3.00 | 0.6 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Alex Fasolo | Tufts University | 3.20 | 6.20 | 7–0 | 5.5 | ≥+3.00 | 2.8 | 13% |
| Ansgar Jordan | Tufts University | 3.35 | 6.35 | 7–0 | 5.5 | ≥+3.00 | 2.8 | 13% |
| Grace Gear | University of Vermont | 2.26 | 2.70 | 1–2 | 0.9 | +0.44 | 0.0 | 78% |
| Adrian Van Der Wal | Northeastern University | 2.61 | 2.61 | 3–4 | 3.0 | +0.00 | 0.0 | 69% |
| Olivia Belda | Brown University | 3.50 | 2.95 | 6–1 | 6.3 | −0.55 | 0.1 | 88% |
| Aidan Naughton | University of Rhode Island | 3.33 | 2.64 | 3–4 | 3.7 | −0.69 | 0.3 | 89% |
| Joshua Bartoszuk | University of Rhode Island | 2.41 | 1.72 | 3–4 | 3.7 | −0.69 | 0.3 | 89% |
| Sarah Burn | Harvard University | 2.93 | 2.05 | 3–3 | 3.7 | −0.88 | 0.4 | 92% |
| Eric Hansen | Harvard University | 2.68 | 1.29 | 3–4 | 4.4 | −1.39 | 1.4 | 97% |
| Dylan Ascencios | Harvard University | 3.09 | 1.69 | 3–4 | 4.4 | −1.39 | 1.4 | 97% |
| Marina Hutzler | Northwestern University | 1.53 | -1.47 | 0–7 | 0.4 | ≤−3.00 | 0.6 | >99% |
| Elizabeth Amelotte | University of Vermont | 1.28 | -1.72 | 0–5 | 0.2 | ≤−3.00 | 0.2 | >99% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | Jumbos | Bears | Engineers | Crimson | Huskies | Rams | Catamounts | Wildcats | |
|---|---|---|---|---|---|---|---|---|---|---|
| Tufts University Jumbos | 7–0 | ✕ | W | W | W | W | W | W | W |
| Brown University Bears | 6–1 | L | ✕ | W | W | W | W | W | W |
| Massachusetts Institute of Technology Engineers | 5–2 | L | L | ✕ | W | W | W | W | W |
| Harvard University Crimson | 3–4 | L | L | L | ✕ | L | W | W | W |
| Northeastern University Huskies | 3–4 | L | L | L | W | ✕ | L | W | W |
| University of Rhode Island Rams | 3–4 | L | L | L | L | W | ✕ | W | W |
| University of Vermont Catamounts | 1–6 | L | L | L | L | L | L | ✕ | W |
| Northwestern University Wildcats | 0–7 | L | L | L | L | L | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Huskies | 1-2-6 | def. | |
Wildcats | 3-4-5 |
| 2 | |
Jumbos | 1-2-3 | def. | |
Engineers | 4-5-6 |
| 3 | |
Crimson | 1-2-3 | def. | |
Rams | 4-5-6 |
| 4 | |
Bears | 1-2-3 | def. | |
Catamounts | 4-5-6 |
| 5 | |
Engineers | 1-2-4 | def. | |
Huskies | 3-5-6 |
| 6 | |
Crimson | 1-2-3 | def. | |
Wildcats | 4-5-6 |
| 7 | |
Jumbos | 1-2-4 | def. | |
Catamounts | 3-5-6 |
| 8 | |
Bears | 1-2-4 | def. | |
Rams | 3-5-6 |
| 9 | |
Engineers | 1-2-6 | def. | |
Crimson | 3-4-5 |
| 10 | |
Huskies | 1-3-5 | def. | |
Catamounts | 2-4-6 |
| 11 | |
Rams | 1-2-5 | def. | |
Wildcats | 3-4-6 |
| 12 | |
Jumbos | 1-2-5 | def. | |
Bears | 3-4-6 |
| 13 | |
Crimson | 1-2-5 | def. | |
Catamounts | 3-4-6 |
| 14 | |
Engineers | 1-2-5 | def. | |
Rams | 3-4-6 |
| 15 | |
Jumbos | 1-2-3 | def. | |
Huskies | 4-5-6 |
| 16 | |
Bears | 1-2-3 | def. | |
Wildcats | 4-5-6 |
| 17 | |
Rams | 1-2-3 | def. | |
Catamounts | 4-5-6 |
| 18 | |
Jumbos | 2-3-4 | def. | |
Crimson | 1-5-6 |
| 19 | |
Engineers | 1-2-5 | def. | |
Wildcats | 3-4-6 |
| 20 | |
Bears | 1-2-3 | def. | |
Huskies | 4-5-6 |
| 21 | |
Jumbos | 1-2-6 | def. | |
Rams | 3-4-5 |
| 22 | |
Catamounts | 1-2-3 | def. | |
Wildcats | 4-5-6 |
| 23 | |
Huskies | 1-3-6 | def. | |
Crimson | 2-4-5 |
| 24 | |
Bears | 1-2-4 | def. | |
Engineers | 3-5-6 |
| 25 | |
Jumbos | 1-2-3 | def. | |
Wildcats | 4-5-6 |
| 26 | |
Rams | 1-3-5 | def. | |
Huskies | 2-4-6 |
| 27 | |
Engineers | 1-2-5 | def. | |
Catamounts | 3-4-6 |
| 28 | |
Bears | 1-3-6 | def. | |
Crimson | 2-4-5 |
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.
