| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
Boston University | Terriers | 6–2 |
| 2 |  |
Roger Williams University | Hawks | 6–2 |
| 3 |  |
Harvard University | Crimson | 6–2 |
| 4 |  |
Boston College | Eagles | 5–3 |
| 5 |  |
Northeastern University | Huskies | 4–4 |
| 6 |  |
Bowdoin College | Polar Bears | 4–4 |
| 7 |  |
University of Vermont | Catamounts | 2–6 |
| 8 |  |
University of Rhode Island | Rams | 2–6 |
| 9 |  |
Massachusetts Institute of Technology | Engineers | 1–7 |
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 10,042 team race matches sailed before 2018-03-17, using the line-ups that actually raced. It called 15 of 31 matches (48%) with a log-loss of 0.746 (a coin flip scores 0.693), so the event went poorly for the forecast. Orderliness −0.93 ± 0.43: an upset-heavy event, more orderly than 4% 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) |
|---|---|---|---|---|---|---|---|---|
| 2 | Roger Williams University | 7.99 | 10.46 | 6–1 | 3.1 | +2.47 | 4.6 | 2% |
| 1 | Boston University | 8.06 | 9.56 | 6–2 | 3.5 | +1.50 | 2.6 | 6% |
| 5 | Northeastern University | 7.81 | 8.79 | 4–3 | 2.5 | +0.98 | 1.0 | 21% |
| 3 | Harvard University | 8.70 | 8.75 | 2–1 | 2.0 | +0.04 | 0.0 | 73% |
| 7 | University of Vermont | 7.32 | 7.26 | 2–6 | 2.1 | −0.05 | 0.0 | 66% |
| 6 | Bowdoin College | 8.11 | 7.81 | 3–4 | 3.4 | −0.30 | 0.1 | 78% |
| 4 | Boston College | 10.11 | 8.98 | 5–2 | 6.2 | −1.13 | 1.1 | 96% |
| 8 | University of Rhode Island | 8.57 | 7.24 | 2–5 | 4.0 | −1.33 | 1.9 | 98% |
| 9 | Massachusetts Institute of Technology | 8.50 | 6.20 | 1–7 | 4.3 | −2.30 | 4.8 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Spencer Cartwright | Roger Williams University | 2.74 | 5.21 | 6–1 | 3.1 | +2.47 | 4.6 | 2% |
| Ed Lebens | Roger Williams University | 2.38 | 4.46 | 4–1 | 2.2 | +2.08 | 2.4 | 10% |
| Kurran Singh | Northeastern University | 2.36 | 3.34 | 4–3 | 2.5 | +0.98 | 1.0 | 21% |
| Kyle Riggs | Northeastern University | 2.37 | 3.35 | 4–3 | 2.5 | +0.98 | 1.0 | 21% |
| Eli Burnes | Harvard University | 3.14 | 3.18 | 2–1 | 2.0 | +0.04 | 0.0 | 73% |
| Alden Grimes | Bowdoin College | 2.43 | 2.13 | 3–4 | 3.4 | −0.30 | 0.1 | 78% |
| Alexander Vasiliou | Bowdoin College | 2.60 | 2.30 | 3–4 | 3.4 | −0.30 | 0.1 | 78% |
| Vincent Yannelli | University of Vermont | 2.12 | 1.38 | 1–6 | 1.8 | −0.74 | 0.4 | 88% |
| Wade Waddell | Boston College | 3.67 | 2.54 | 5–2 | 6.2 | −1.13 | 1.1 | 96% |
| Patrick Isherwood | University of Rhode Island | 2.74 | 1.41 | 2–5 | 4.0 | −1.33 | 1.9 | 98% |
| Ivan Shestopalov | Massachusetts Institute of Technology | 2.82 | 0.75 | 1–6 | 3.6 | −2.07 | 3.5 | >99% |
| James Peraire-Bueno | Massachusetts Institute of Technology | 2.00 | -1.00 | 0–6 | 2.8 | ≤−3.00 | 5.5 | >99% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | Terriers | Hawks | Crimson | Eagles | Huskies | Bears | Catamounts | Rams | Engineers | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Boston University Terriers | 6–2 | ✕ | L | W | L | W | W | W | W | W |
| Roger Williams University Hawks | 6–2 | W | ✕ | L | W | L | W | W | W | W |
| Harvard University Crimson | 6–2 | L | W | ✕ | W | W | L | W | W | W |
| Boston College Eagles | 5–3 | W | L | L | ✕ | W | W | W | L | W |
| Northeastern University Huskies | 4–4 | L | W | L | L | ✕ | W | W | W | L |
| Bowdoin College Polar Bears | 4–4 | L | L | W | L | L | ✕ | W | W | W |
| University of Vermont Catamounts | 2–6 | L | L | L | L | L | L | ✕ | W | W |
| University of Rhode Island Rams | 2–6 | L | L | L | W | L | L | L | ✕ | W |
| Massachusetts Institute of Technology Engineers | 1–7 | L | L | L | L | W | L | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Crimson | 1-5-6 DNF | def. | |
Engineers | 2-3-4 DSQ |
| 2 | |
Terriers | 1-3-4 | def. | |
Huskies | 2-5-6 |
| 3 | |
Hawks | 1-2-5 | def. | |
Polar Bears | 3-4-6 |
| 4 | |
Rams | 1-2-6 | def. | |
Eagles | 3-4-5 |
| 5 | |
Crimson | 1-3-4 | def. | |
Catamounts | 2-5-6 |
| 6 | |
Terriers | 1-3-4 | def. | |
Engineers | 2-5-6 |
| 7 | |
Huskies | 1-2-5 | def. | |
Polar Bears | 3-4-6 |
| 8 | |
Hawks | 2-3-4 | def. | |
Eagles | 1-5-6 |
| 9 | |
Catamounts | 1-4-5 | def. | |
Rams | 2-3-6 |
| 10 | |
Terriers | 1-2-5 | def. | |
Crimson | 3-4-6 |
| 11 | |
Polar Bears | 1-2-3 | def. | |
Engineers | 4-5-6 |
| 12 | |
Eagles | 1-2-3 | def. | |
Huskies | 4-5-6 |
| 13 | |
Hawks | 1-2-6 | def. | |
Rams | 3-4-5 |
| 14 | |
Terriers | 2-3-5 | def. | |
Catamounts | 1-4-6 DNS |
| 15 | |
Polar Bears | 2-3-5 | def. | |
Crimson | 1-4-6 |
| 16 | |
Eagles | 1-2-5 | def. | |
Engineers | 3-4-6 |
| 17 | |
Huskies | 2-3-5 | def. | |
Rams | 1-4-6 |
| 18 | |
Hawks | 1-3-4 | def. | |
Catamounts | 2-5-6 |
| 19 | |
Terriers | 1-2-5 | def. | |
Polar Bears | 3-4-6 DNF |
| 20 | |
Crimson | 1-4-5 | def. | |
Eagles | 2-3-6 |
| 21 | |
Rams | 1-2-3 | def. | |
Engineers | 4-5-6 DNF |
| 22 | |
Huskies | 1-3-5 | def. | |
Hawks | 2-4-6 |
| 23 | |
Polar Bears | 1-2-3 | def. | |
Catamounts | 4-5-6 |
| 24 | |
Eagles | 2-3-4 | def. | |
Terriers | 1-5-6 |
| 25 | |
Crimson | 1-2-4 | def. | |
Rams | 3-5-6 |
| 26 | |
Hawks | 1-2-4 | def. | |
Engineers | 3-5-6 |
| 27 | |
Huskies | 2-3-4 | def. | |
Catamounts | 1-5-6 |
| 28 | |
Eagles | 1-2-3 | def. | |
Polar Bears | 4-5-6 |
| 29 | |
Terriers | 1-3-5 | def. | |
Rams | 2-4-6 |
| 30 | |
Crimson | 1-3-5 | def. | |
Hawks | 2-4-6 |
| 31 | |
Engineers | 1-2-5 | def. | |
Huskies | 3-4-6 |
| 32 | |
Eagles | 1-2-4 | def. | |
Catamounts | 3-5-6 |
| 33 | |
Polar Bears | 1-4-5 | def. | |
Rams | 2-3-6 |
| 34 | |
Hawks | 1-2-6 | def. | |
Terriers | 3-4-5 |
| 35 | |
Crimson | 1-3-4 | def. | |
Huskies | 2-5-6 |
| 36 | |
Catamounts | 1-2-4 | def. | |
Engineers | 3-5-6 |
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% 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.
