| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
Massachusetts Institute of Technology | Engineers | 8–1 |
| 2 |  |
Brown University | Bears | 4–5 |
| 3 |  |
Northeastern University | Huskies | 4–5 |
| 4 |  |
Tufts University | Jumbos | 2–7 |
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 19,442 team race matches sailed before 2023-03-11, using the line-ups that actually raced. It called 11 of 18 matches (61%) with a log-loss of 0.584 (a coin flip scores 0.693), so the event went about as expected for the forecast. Orderliness −0.32 ± 0.43: a typical event, more orderly than 18% 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 | Massachusetts Institute of Technology Engineers | 1 | 8.54 home +0.39 | Emily Scherer / Yitian Zhu 2.34 Brooke Schmelz / Samantha Karlson 3.02 Lucy Brock / Grace Mao 2.82 | 86% | 8–1 (89%) | +0.3 |
| 2 | Brown University Bears | 2 | 7.50 school +0.21 | Katherine Mcnamara / Talia Levine 2.62 Katharine Doble / Janelle Li Charlotte Costikyan / Catherine Morrissette 2.44 2–4 Emily Mueller / Sierra Bornheim 2.34 2–1 | 62% | 4–5 (44%) | −1.6 |
| 3 | Northeastern University Huskies | 4 | 5.94 school +0.21 | Lucia Loosbrock / Emma Cole 2.32 Eva Ermlich / Lila Coffman 2.11 Lauren Krim / Abigail Thress 1.24 | 23% | 4–5 (44%) | +2.0 |
| 4 | Tufts University Jumbos | 3 | 6.27 school +0.13 | Maisie Macgillivray / Annabel De Alessi Ella Hubbard / Lilly Ladd Meredith Broadus / Lilah Parker | 30% | 2–7 (22%) | −0.7 |
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) |
|---|---|---|---|---|---|---|---|---|
| 3 | Northeastern University | 5.94 | 7.14 | 4–5 | 2.0 | +1.20 | 1.8 | 11% |
| 1 | Massachusetts Institute of Technology | 8.54 | 8.84 | 8–1 | 7.7 | +0.31 | 0.1 | 62% |
| 4 | Tufts University | 6.27 | 5.79 | 2–7 | 2.7 | −0.48 | 0.2 | 83% |
| 2 | Brown University | 7.50 | 6.55 | 4–5 | 5.6 | −0.95 | 1.1 | 95% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Lucy Brock | Massachusetts Institute of Technology | 2.82 | 4.01 | 4–5 | 2.0 | +1.20 | 1.8 | 11% |
| Meredith Broadus | Tufts University | 1.67 | 1.18 | 2–7 | 2.7 | −0.48 | 0.2 | 83% |
| Emily Mueller | Brown University | 2.34 | 1.53 | 2–1 | 2.5 | −0.80 | 0.3 | 91% |
| Charlotte Costikyan | Brown University | 2.44 | 1.40 | 2–4 | 3.1 | −1.05 | 0.8 | 95% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | Engineers | Bears | Huskies | Jumbos | |
|---|---|---|---|---|---|---|
| Massachusetts Institute of Technology Engineers | 2–1 | ✕ | L | W | W |
| Brown University Bears | 3–0 | W | ✕ | W | W |
| Northeastern University Huskies | 1–2 | L | L | ✕ | W |
| Tufts University Jumbos | 0–3 | L | L | L | ✕ |
| Team | Rec. | Engineers | Bears | Huskies | Jumbos | |
|---|---|---|---|---|---|---|
| Massachusetts Institute of Technology Engineers | 3–0 | ✕ | W | W | W |
| Brown University Bears | 0–3 | L | ✕ | L | L |
| Northeastern University Huskies | 2–1 | L | W | ✕ | W |
| Tufts University Jumbos | 1–2 | L | W | L | ✕ |
| Team | Rec. | Engineers | Bears | Huskies | Jumbos | |
|---|---|---|---|---|---|---|
| Massachusetts Institute of Technology Engineers | 3–0 | ✕ | W | W | W |
| Brown University Bears | 1–2 | L | ✕ | W | L |
| Northeastern University Huskies | 1–2 | L | L | ✕ | W |
| Tufts University Jumbos | 1–2 | L | W | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Bears | 1-2-5 | def. | |
Engineers | 3-4-6 |
| 2 | |
Huskies | 2-3-4 | def. | |
Jumbos | 1-5-6 |
| 3 | |
Bears | 1-3-4 | def. | |
Jumbos | 2-5-6 |
| 4 | |
Engineers | 1-2-4 | def. | |
Huskies | 3-5-6 |
| 5 | |
Engineers | 1-2-5 | def. | |
Jumbos | 3-4-6 |
| 6 | |
Bears | 1-2-6 | def. | |
Huskies | 3-4-5 |
| 7 | |
Huskies | 1-2-5 | def. | |
Bears | 3-4-6 |
| 8 | |
Engineers | 2-3-4 | def. | |
Jumbos | 1-5-6 |
| 9 | |
Engineers | 1-2-5 | def. | |
Huskies | 3-4-6 |
| 10 | |
Jumbos | 1-2-6 | def. | |
Bears | 3-4-5 |
| 11 | |
Engineers | 1-2-4 | def. | |
Bears | 3-5-6 |
| 12 | |
Huskies | 1-2-5 | def. | |
Jumbos | 3-4-6 |
| 13 | |
Huskies | 1-2-6 | def. | |
Jumbos | 3-4-5 |
| 14 | |
Engineers | 1-2-6 | def. | |
Bears | 3-4-5 |
| 15 | |
Jumbos | 1-2-5 | def. | |
Bears | 3-4-6 |
| 16 | |
Engineers | 1-2-5 | def. | |
Huskies | 3-4-6 |
| 17 | |
Bears | 1-2-5 | def. | |
Huskies | 3-4-6 |
| 18 | |
Engineers | 1-2-4 | def. | |
Jumbos | 3-5-6 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
Massachusetts Institute of Technology | Engineers | Brooke Schmelz '25 Emily Scherer '23 Lucy Brock '25 |
Samantha Karlson '25 Yitian Zhu '25 Grace Mao '23 |
|
Brown University | Bears | Charlotte Costikyan '24 Emily Mueller '25 Katharine Doble '26 Katherine Mcnamara '26 |
Catherine Morrissette '24 Sierra Bornheim '24 Janelle Li '25 Talia Levine '26 |
|
Northeastern University | Huskies | Eva Ermlich '26 Lauren Krim '24 Lucia Loosbrock '25 |
Lila Coffman '26 Abigail Thress '24 Emma Cole '25 |
|
Tufts University | Jumbos | Ella Hubbard '26 Maisie Macgillivray '25 Meredith Broadus '25 |
Lilly Ladd '26 Annabel De Alessi '25 Lilah Parker '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 ±0.07 means a rating gap counts about 6% 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.
