| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
Harvard University | Crimson | 9–1 |
| 2 |  |
Boston College | Eagles | 7–3 |
| 3 |  |
Roger Williams University | Hawks | 7–3 |
| 4 |  |
Brown University | Bears | 3–7 |
| 4 |  |
Tufts University | Jumbos | 3–7 |
| 6 |  |
Boston University | Terriers | 1–9 |
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 3,965 team race matches sailed before 2014-10-18, using the line-ups that actually raced. It called 24 of 31 matches (77%) with a log-loss of 0.413 (a coin flip scores 0.693), so the event went well for the forecast. Orderliness +0.25 ± 0.34: a typical event, more orderly than 72% 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 | Harvard University | 8.89 | 10.43 | 9–1 | 7.0 | +1.54 | 1.9 | 12% |
| 2 | Boston College | 9.02 | 9.14 | 8–3 | 7.8 | +0.12 | 0.0 | 59% |
| 4 | Brown University | 7.06 | 7.04 | 3–7 | 3.0 | −0.02 | 0.0 | 66% |
| 6 | Boston University | 5.94 | 5.83 | 1–9 | 1.1 | −0.11 | 0.0 | 70% |
| 3 | Roger Williams University | 9.04 | 8.59 | 7–4 | 7.9 | −0.46 | 0.3 | 85% |
| 4 | Tufts University | 7.65 | 6.92 | 3–7 | 4.2 | −0.73 | 0.6 | 91% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Gram Slattery | Harvard University | 3.59 | 5.14 | 9–1 | 7.0 | +1.54 | 1.9 | 12% |
| Matthew Mollerus | Harvard University | 3.08 | 4.62 | 9–1 | 7.0 | +1.54 | 1.9 | 12% |
| Bennett Capozzi | Harvard University | 2.28 | 3.82 | 9–1 | 7.0 | +1.54 | 1.9 | 12% |
| Henry Dumke | Boston College | 3.00 | 3.12 | 8–3 | 7.8 | +0.12 | 0.0 | 59% |
| Jacob Martz | Boston College | 2.77 | 2.89 | 8–3 | 7.8 | +0.12 | 0.0 | 59% |
| Ryan Schmitz | Boston College | 2.91 | 3.03 | 8–3 | 7.8 | +0.12 | 0.0 | 59% |
| Franco Bilik | Brown University | 2.01 | 1.99 | 3–7 | 3.0 | −0.02 | 0.0 | 66% |
| Solomon Tarlin | Boston University | 1.35 | 1.24 | 1–9 | 1.1 | −0.11 | 0.0 | 70% |
| Martim Anderson | Roger Williams University | 2.77 | 2.31 | 7–4 | 7.9 | −0.46 | 0.3 | 85% |
| Viktor Wettergren | Tufts University | 2.31 | 1.58 | 3–7 | 4.2 | −0.73 | 0.6 | 91% |
| James Moody | Tufts University | 2.56 | 1.83 | 3–7 | 4.2 | −0.73 | 0.6 | 91% |
| Colin Meade | Tufts University | 2.32 | 1.59 | 3–7 | 4.2 | −0.73 | 0.6 | 91% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | Crimson | Eagles | Hawks | Bears | Jumbos | Terriers | |
|---|---|---|---|---|---|---|---|---|
| Harvard University Crimson | 4–1 | ✕ | W | L | W | W | W |
| Boston College Eagles | 4–1 | L | ✕ | W | W | W | W |
| Roger Williams University Hawks | 4–1 | W | L | ✕ | W | W | W |
| Brown University Bears | 1–4 | L | L | L | ✕ | L | W |
| Tufts University Jumbos | 2–3 | L | L | L | W | ✕ | W |
| Boston University Terriers | 0–5 | L | L | L | L | L | ✕ |
| Team | Rec. | Crimson | Eagles | Hawks | Bears | Jumbos | Terriers | |
|---|---|---|---|---|---|---|---|---|
| Harvard University Crimson | 5–0 | ✕ | W | W | W | W | W |
| Boston College Eagles | 4–2 | L | ✕ | W | W | W | W |
| Roger Williams University Hawks | 3–3 | L | L | ✕ | L | W | W |
| Brown University Bears | 2–3 | L | L | W | ✕ | W | L |
| Tufts University Jumbos | 1–4 | L | L | L | L | ✕ | W |
| Boston University Terriers | 1–4 | L | L | L | W | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Crimson | 1-3-4 | def. | |
Bears | 2-5-6 |
| 2 | |
Hawks | 1-4-5 | def. | |
Terriers | 2-3-6 |
| 3 | |
Eagles | 1-2-5 | def. | |
Jumbos | 3-4-6 |
| 4 | |
Bears | 1-2-6 | def. | |
Terriers | 3-4-5 |
| 5 | |
Crimson | 1-2-5 | def. | |
Jumbos | 3-4-6 |
| 6 | |
Eagles | 2-3-4 | def. | |
Hawks | 1-5-6 |
| 7 | |
Jumbos | 1-2-4 | def. | |
Terriers | 3-5-6 |
| 8 | |
Hawks | 1-2-6 | def. | |
Bears | 3-4-5 |
| 9 | |
Crimson | 1-2-6 | def. | |
Eagles | 3-4-5 |
| 10 | |
Hawks | 1-2-6 | def. | |
Jumbos | 3-4-5 |
| 11 | |
Crimson | 1-2-3 | def. | |
Terriers | 4-5-6 |
| 12 | |
Eagles | 1-2-3 | def. | |
Bears | 4-5-6 |
| 13 | |
Hawks | 1-3-6 | def. | |
Crimson | 2-4-5 |
| 14 | |
Jumbos | 1-3-5 | def. | |
Bears | 2-4-6 |
| 15 | |
Eagles | 1-2-5 | def. | |
Terriers | 3-4-6 |
| 16 | |
Hawks | 1-2-6 | def. | |
Jumbos | 3-4-5 |
| 17 | |
Crimson | 1-2-5 | def. | |
Eagles | 3-4-6 |
| 18 | |
Terriers | 1-2-6 | def. | |
Bears | 3-4-5 |
| 19 | |
Eagles | 2-3-4 | def. | |
Jumbos | 1-5-6 |
| 20 | |
Bears | 1-3-6 | def. | |
Hawks | 2-4-5 |
| 21 | |
Crimson | 1-2-6 | def. | |
Terriers | 3-4-5 |
| 22 | |
Eagles | 1-2-3 | def. | |
Bears | 4-5-6 |
| 23 | |
Crimson | 2-3-5 | def. | |
Jumbos | 1-4-6 |
| 24 | |
Hawks | 1-2-3 | def. | |
Terriers | 4-5-6 |
| 25 | |
Crimson | 1-2-3 | def. | |
Bears | 4-5-6 |
| 26 | |
Hawks | 1-2-3 | def. | |
Eagles | 4-5-6 |
| 27 | |
Jumbos | 1-2-6 | def. | |
Terriers | 3-4-5 |
| 28 | |
Crimson | 1-3-6 | def. | |
Hawks | 2-4-5 |
| 29 | |
Bears | 2-3-4 | def. | |
Jumbos | 1-5-6 |
| 30 | |
Eagles | 1-2-5 | def. | |
Terriers | 3-4-6 |
| 31 | |
Eagles | 1-2-5 | def. | |
Hawks | 3-4-6 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
Harvard University | Crimson | Bennett Capozzi '17 Gram Slattery '15 Matthew Mollerus '17 |
Nomin-Erdene Jagdagdorj '17 Julia Lord '17 Olivia Kjorlien '17 |
|
Boston College | Eagles | Henry Dumke '17 Jacob Martz '17 Ryan Schmitz '15 |
Riley Calhoun '16 Lily Mcgrath '18 Kaitlin Driscoll '16 |
|
Roger Williams University | Hawks | Dirk Johnson '18 Mackenzie Bryan '18 Martim Anderson '18 |
Megan Roach '18 Breanne Baldino '16 Morgan Wheeler '15 |
|
Brown University | Bears | Franco Bilik '18 Kevin Eve '18 Sam Alexander '18 |
James Woodwell '18 Nathan Housberg '19 Eric Xiao '17 |
|
Tufts University | Jumbos | Colin Meade '16 James Moody '16 Viktor Wettergren '14 |
Natalie Danziger '17 Caroline Ambros '17 Emily Shanley-Roberts '18 |
|
Boston University | Terriers | Nicholas Arabia '16 Reed Lorimer '18 Solomon Tarlin '17 |
Katherine Bienkowski '17 Lily Lichtenstein '18 Madison Sparber '17 |
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 ±0.05 means a rating gap counts about 4% 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.
