| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
Boston College | Eagles | 4–1 |
| 2 |  |
Boston University | Terriers | 4–1 |
| 3 |  |
Brown University | Bears | 3–2 |
| 4 |  |
Harvard University | Crimson | 2–3 |
| 5 |  |
Massachusetts Institute of Technology | Engineers | 1–4 |
| 6 |  |
Tufts University | Jumbos | 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 9,186 team race matches sailed before 2017-11-04, using the line-ups that actually raced. It called 8 of 10 matches (80%) with a log-loss of 0.587 (a coin flip scores 0.693), so the event went about as expected for the forecast. Orderliness −0.31 ± 0.60: a typical event, more orderly than 19% 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 | Boston College Eagles | 1 | 8.60 school +0.24 | Lily Mcgrath / Tara Ferraris 2.77 Colin Brego / Elizabeth Matheson 2.81 Thomas Walden / Caroline Downey 2.78 | 82% | 3–1 (75%) | −0.3 |
| 2 | Boston University Terriers | 2 | 7.77 school −0.32 | Kyle Dochoda / Emily Walker 3.02 Michael Sabourin / Megan Blagden 2.44 Grace Mooradian / Caroline Salas | 64% | 3–1 (75%) | +0.4 |
| 3 | Brown University Bears | 4 | 7.09 school +0.26 | Rachel Foster / Grace Gagnon Nathan Housberg / Sawyer Balint John Mastrandrea / Lauren Haller 2.36 | 45% | 2–2 (50%) | +0.2 |
| 4 | Harvard University Crimson | 5 | 5.43 school −0.13, home +0.41 | Andrew Puopolo / Zennie Wey 2.14 Owen Schafer / Emily Malpass Emma Jakobson / Tancredi Castellano Pucci Di Barsento | 11% | 1–3 (25%) | +0.6 |
| 6 | Tufts University Jumbos | 3 | 7.17 school +0.11 | Wells Drayton / Marisa Permatteo 2.34 Leyla Senocak / Sabina Van Mell Austen Freda / Duke Saunders 2.59 | 48% | 1–3 (25%) | −0.9 |
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) |
|---|---|---|---|---|---|---|---|---|
| 4 | Harvard University | 5.43 | 6.44 | 1–3 | 0.4 | +1.01 | 0.4 | 38% |
| 2 | Boston University | 7.77 | 8.40 | 3–1 | 2.6 | +0.63 | 0.2 | 55% |
| 3 | Brown University | 7.09 | 7.36 | 2–2 | 1.8 | +0.27 | 0.0 | 62% |
| 1 | Boston College | 8.60 | 8.18 | 3–1 | 3.3 | −0.42 | 0.1 | 85% |
| 6 | Tufts University | 7.17 | 5.81 | 1–3 | 1.9 | −1.37 | 0.9 | 96% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Andrew Puopolo | Harvard University | 2.14 | 3.15 | 1–3 | 0.4 | +1.01 | 0.4 | 38% |
| Grace Mooradian | Boston University | 2.51 | 3.14 | 3–1 | 2.6 | +0.63 | 0.2 | 55% |
| Rachel Foster | Brown University | 1.90 | 2.17 | 2–2 | 1.8 | +0.27 | 0.0 | 62% |
| Nathan Housberg | Brown University | 2.43 | 2.70 | 2–2 | 1.8 | +0.27 | 0.0 | 62% |
| Leyla Senocak | Tufts University | 2.04 | 0.67 | 1–3 | 1.9 | −1.37 | 0.9 | 96% |
| Austen Freda | Tufts University | 2.59 | 1.22 | 1–3 | 1.9 | −1.37 | 0.9 | 96% |
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 | Boston University | Harvard University | 92% | |
| 1 | 3 | Boston College | Tufts University | 82% | |
| 1 | 4 | Boston University | Brown University | 69% | |
| 1 | 5 | Harvard University | Tufts University | 15% | upset |
| 1 | 7 | Tufts University | Brown University | 54% | |
| 1 | 9 | Boston College | Harvard University | 96% | |
| 1 | 11 | Brown University | Harvard University | 82% | |
| 1 | 12 | Boston College | Boston University | 69% | |
| 1 | 14 | Boston University | Tufts University | 65% | |
| 1 | 15 | Brown University | Boston College | 20% | upset |
| Team | Rec. | Eagles | Terriers | Bears | Crimson | Engineers | Jumbos | |
|---|---|---|---|---|---|---|---|---|
| Boston College Eagles | 4–1 | ✕ | W | L | W | W | W |
| Boston University Terriers | 4–1 | L | ✕ | W | W | W | W |
| Brown University Bears | 3–2 | W | L | ✕ | W | W | L |
| Harvard University Crimson | 2–3 | L | L | L | ✕ | W | W |
| Massachusetts Institute of Technology Engineers | 1–4 | L | L | L | L | ✕ | W |
| Tufts University Jumbos | 1–4 | L | L | W | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Terriers | 1-2-3 | def. | |
Crimson | 4-5-6 |
| 2 | |
Bears | 1-2-5 | def. | |
Engineers | 3-4-6 |
| 3 | |
Eagles | 1-2-3 | def. | |
Jumbos | 4-5-6 |
| 4 | |
Terriers | 1-3-4 | def. | |
Bears | 2-5-6 |
| 5 | |
Crimson | 2-3-5 | def. | |
Jumbos | 1-4-6 |
| 6 | |
Eagles | 1-2-6 | def. | |
Engineers | 3-4-5 |
| 7 | |
Jumbos | 2-3-4 | def. | |
Bears | 1-5-6 |
| 8 | |
Terriers | 1-2-5 | def. | |
Engineers | 3-4-6 |
| 9 | |
Eagles | 1-2-3 | def. | |
Crimson | 4-5-6 |
| 10 | |
Engineers | 1-2-4 | def. | |
Jumbos | 3-5-6 |
| 11 | |
Bears | 1-2-5 | def. | |
Crimson | 3-4-6 |
| 12 | |
Eagles | 1-2-6 | def. | |
Terriers | 3-4-5 |
| 13 | |
Crimson | 1-2-3 | def. | |
Engineers | 4-5-6 |
| 14 | |
Terriers | 1-2-6 | def. | |
Jumbos | 3-4-5 |
| 15 | |
Bears | 1-2-6 | def. | |
Eagles | 3-4-5 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
Boston College | Eagles | Colin Brego '20 Lily Mcgrath '18 Thomas Walden '20 |
Elizabeth Matheson '20 Tara Ferraris '19 Caroline Downey '19 |
|
Boston University | Terriers | Grace Mooradian '21 Kyle Dochoda '21 Michael Sabourin '20 |
Caroline Salas '18 Emily Walker '19 Megan Blagden '20 |
|
Brown University | Bears | John Mastrandrea '20 Nathan Housberg '19 Rachel Foster '20 |
Lauren Haller '18 Sawyer Balint '20 Grace Gagnon '18 |
|
Harvard University | Crimson | Andrew Puopolo '19 Emma Jakobson '21 Owen Schafer '21 |
Zennie Wey '20 Tancredi Castellano Pucci Di Barsento '20 Emily Malpass '21 |
|
Tufts University | Jumbos | Austen Freda '21 Leyla Senocak '21 Wells Drayton '21 |
Duke Saunders '20 Sabina Van Mell '19 Marisa Permatteo '19 |
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 ±0.04 means a rating gap counts about 4% 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.
