| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 | Bowdoin College | Polar Bears 1 | 14–2 | |
| 2 | Tufts University | Jumbos | 14–2 | |
| 3 | Bowdoin College | Polar Bears 2 | 6–10 | |
| 4 | Boston University | Terriers | 4–12 | |
| 5 | University of Vermont | Catamounts | 2–14 |
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 20,149 team race matches sailed before 2023-03-25, using the line-ups that actually raced. It called 32 of 40 matches (80%) with a log-loss of 0.353 (a coin flip scores 0.693), so the event went well for the forecast. Orderliness +0.61 ± 0.31: an orderly event, more orderly than 97% 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 | Bowdoin College Polar Bears 1 | 1 | 7.86 school +0.06, home +0.40 | Sam Bonauto / Jondall Norris 2.99 Kyra Phelan / Brooke Asherman Lauren Russler / Luisa Louchheim | 80% | 14–2 (88%) | +1.2 |
| 2 | Tufts University Jumbos | 2 | 7.71 school +0.08 | Connor Rosow / Magdelaina Putnam 2.33 Shea Smith / Lilah Parker 2.31 John Eastman / Naomi Pedersen 2.94 | 76% | 14–2 (88%) | +1.8 |
| 3 | Bowdoin College Polar Bears 2 | 5 | 5.81 school +0.06, home +0.40 | Ethan Danielson / Isabelle Tsuchitori 2.15 Charlie Apolinsky / Katharine Kurtz Benjamin Stevens / Aidan Michelow 1.96 | 31% | 6–10 (38%) | +1.1 |
| 4 | Boston University Terriers | 3 | 5.85 | Gavin Monaghan / Sam Whipple 1.79 Nathan Selian / Yangyang Liu 2.03 Porter Bell / Carolyn Kravets 1.89 | 32% | 4–12 (25%) | −1.0 |
| 5 | University of Vermont Catamounts | 4 | 5.85 school −0.06 | Ryan Potter / Alex Forrest 2.07 Calvin Lamosse / Elizabeth Amelotte 2.02 Gavin Sanborn / Caitlin Derby 1.76 | 32% | 2–14 (12%) | −3.0 |
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 | Tufts University Jumbos | 7.71 | 8.62 | 14–2 | 12.2 | +0.91 | 1.1 | 21% |
| 1 | Bowdoin College Polar Bears 1 | 7.86 | 8.51 | 14–2 | 12.8 | +0.65 | 0.5 | 33% |
| 3 | Bowdoin College Polar Bears 2 | 5.81 | 6.17 | 6–10 | 4.9 | +0.36 | 0.3 | 36% |
| 4 | Boston University Terriers | 5.85 | 5.46 | 4–12 | 5.0 | −0.38 | 0.3 | 82% |
| 5 | University of Vermont Catamounts | 5.85 | 4.53 | 2–14 | 5.0 | −1.32 | 2.6 | 99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Connor Rosow | Tufts University | 2.33 | 3.24 | 14–2 | 12.2 | +0.91 | 1.1 | 21% |
| Shea Smith | Tufts University | 2.31 | 3.22 | 14–2 | 12.2 | +0.91 | 1.1 | 21% |
| Lauren Russler | Bowdoin College | 2.31 | 2.96 | 14–2 | 12.8 | +0.65 | 0.5 | 33% |
| Charlie Apolinsky | Bowdoin College | 0.95 | 1.31 | 6–10 | 4.9 | +0.36 | 0.3 | 36% |
| Gavin Monaghan | Boston University | 1.79 | 1.41 | 4–12 | 5.0 | −0.38 | 0.3 | 82% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | 1 | Jumbos | 2 | Terriers | Catamounts | |
|---|---|---|---|---|---|---|---|
| Bowdoin College Polar Bears 1 | 4–4 | ✕ | L | ✕ | W | W | |
| Tufts University Jumbos | 4–0 | W | ✕ | W | W | W | |
| Bowdoin College Polar Bears 2 | 4–4 | ✕ | L | ✕ | W | W | |
| Boston University Terriers | 2–2 | L | L | L | ✕ | W | |
| University of Vermont Catamounts | 0–4 | L | L | L | L | ✕ |
| Team | Rec. | 1 | Jumbos | 2 | Terriers | Catamounts | |
|---|---|---|---|---|---|---|---|
| Bowdoin College Polar Bears 1 | 4–4 | ✕ | L | ✕ | W | W | |
| Tufts University Jumbos | 4–0 | W | ✕ | W | W | W | |
| Bowdoin College Polar Bears 2 | 4–4 | ✕ | L | ✕ | W | W | |
| Boston University Terriers | 1–3 | L | L | L | ✕ | L | |
| University of Vermont Catamounts | 1–3 | L | L | L | W | ✕ |
| Team | Rec. | 1 | Jumbos | 2 | Terriers | Catamounts | |
|---|---|---|---|---|---|---|---|
| Bowdoin College Polar Bears 1 | 6–2 | ✕ | W | ✕ | W | W | |
| Tufts University Jumbos | 3–1 | L | ✕ | L | W | W | |
| Bowdoin College Polar Bears 2 | 6–2 | ✕ | W | ✕ | W | W | |
| Boston University Terriers | 1–3 | L | L | L | ✕ | W | |
| University of Vermont Catamounts | 0–4 | L | L | L | L | ✕ |
| Team | Rec. | 1 | Jumbos | 2 | Terriers | Catamounts | |
|---|---|---|---|---|---|---|---|
| Bowdoin College Polar Bears 1 | 6–2 | ✕ | L | ✕ | W | W | |
| Tufts University Jumbos | 3–1 | W | ✕ | W | W | W | |
| Bowdoin College Polar Bears 2 | 6–2 | ✕ | L | ✕ | W | W | |
| Boston University Terriers | 0–4 | L | L | L | ✕ | L | |
| University of Vermont Catamounts | 1–3 | L | L | L | W | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | Polar Bears 1 | 1-2-5 | def. | Polar Bears 2 | 3-4-6 | ||
| 2 | Jumbos | 2-3-4 | def. | Terriers | 1-5-6 | ||
| 3 | Polar Bears 1 | 1-2-4 | def. | Catamounts | 3-5-6 | ||
| 4 | Terriers | 2-3-4 | def. | Polar Bears 2 | 1-5-6 | ||
| 5 | Jumbos | 1-2-6 | def. | Polar Bears 1 | 3-4-5 | ||
| 6 | Polar Bears 2 | 1-3-5 | def. | Catamounts | 2-4-6 | ||
| 7 | Polar Bears 1 | 1-2-5 | def. | Terriers | 3-4-6 | ||
| 8 | Jumbos | 1-2-4 | def. | Catamounts | 3-5-6 | ||
| 9 | Terriers | 1-2-5 | def. | Catamounts | 3-4-6 | ||
| 10 | Jumbos | 1-2-3 | def. | Polar Bears 2 | 4-5-6 | ||
| 11 | Polar Bears 1 | 1-2-3 | def. | Catamounts | 4-5-6 | ||
| 12 | Jumbos | 1-2-5 | def. | Terriers | 3-4-6 | ||
| 13 | Terriers | 1-3-5 | def. | Polar Bears 2 | 2-4-6 | ||
| 14 | Jumbos | 1-2-3 | def. | Catamounts | 4-5-6 | ||
| 15 | Polar Bears 1 | 1-2-3 | def. | Polar Bears 2 | 4-5-6 | ||
| 16 | Catamounts | 1-3-6 | def. | Terriers | 2-4-5 | ||
| 17 | Polar Bears 2 | 1-4-5 | def. | Catamounts | 2-3-6 | ||
| 18 | Jumbos | 1-2-6 | def. | Polar Bears 1 | 3-4-5 | ||
| 19 | Polar Bears 1 | 1-2-5 | def. | Terriers | 3-4-6 | ||
| 20 | Jumbos | 1-2-5 | def. | Polar Bears 2 | 3-4-6 | ||
| 21 | Jumbos | 1-2-4 | def. | Catamounts | 3-5-6 | ||
| 22 | Polar Bears 2 | 1-3-6 | def. | Terriers | 2-4-5 | ||
| 23 | Polar Bears 1 | 1-2-4 | def. | Catamounts | 3-5-6 | ||
| 24 | Jumbos | 1-2-3 | def. | Polar Bears 2 | 4-5-6 | ||
| 25 | Polar Bears 1 | 1-2-3 | def. | Terriers | 4-5-6 | ||
| 26 | Polar Bears 2 | 1-2-4 | def. | Catamounts | 3-5-6 | ||
| 27 | Polar Bears 1 | 1-2-6 | def. | Jumbos | 3-4-5 | ||
| 28 | Terriers | 1-2-6 | def. | Catamounts | 3-4-5 | ||
| 29 | Polar Bears 1 | 1-2-3 | def. | Polar Bears 2 | 4-5-6 | ||
| 30 | Jumbos | 1-2-6 | def. | Terriers | 3-4-5 | ||
| 31 | Polar Bears 1 | 2-3-6 | def. | Jumbos | 1-4-5 DSQ | ||
| 32 | Polar Bears 2 | 1-2-6 | def. | Terriers | 3-4-5 | ||
| 33 | Jumbos | 1-2-4 | def. | Catamounts | 3-5-6 | ||
| 34 | Polar Bears 1 | 1-2-3 | def. | Terriers | 4-5-6 | ||
| 35 | Polar Bears 2 | 1-2-6 | def. | Catamounts | 3-4-5 | ||
| 36 | Jumbos | 1-2-3 | def. | Terriers | 4-5-6 | ||
| 37 | Polar Bears 1 | 1-3-4 | def. | Polar Bears 2 | 2-5-6 | ||
| 38 | Catamounts | 1-3-5 | def. | Terriers | 2-4-6 | ||
| 39 | Jumbos | 1-2-3 | def. | Polar Bears 2 | 4-5-6 | ||
| 40 | Polar Bears 1 | 1-3-5 | def. | Catamounts | 2-4-6 | ||
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
| Bowdoin College | Polar Bears 1 | Kyra Phelan '26 Lauren Russler '26 Sam Bonauto '25 |
Brooke Asherman '19 Luisa Louchheim '25 Jondall Norris '25 |
|
| Tufts University | Jumbos | Connor Rosow '24 John Eastman '25 Shea Smith '25 |
Magdelaina Putnam '25 Naomi Pedersen '25 Lilah Parker '26 |
|
| Bowdoin College | Polar Bears 2 | Benjamin Stevens '25 Charlie Apolinsky '26 Ethan Danielson '26 |
Aidan Michelow '25 Katharine Kurtz '24 Isabelle Tsuchitori '26 |
|
| Boston University | Terriers | Gavin Monaghan '26 Nathan Selian '25 Porter Bell '26 |
Sam Whipple '25 Yangyang Liu '25 Carolyn Kravets '25 |
|
| University of Vermont | Catamounts | Calvin Lamosse '25 Gavin Sanborn '25 Ryan Potter '24 |
Elizabeth Amelotte '24 Caitlin Derby '26 Alex Forrest '26 |
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.