| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
Stanford University | Cardinal | 4–0 |
| 2 |  |
University of Hawaii | Rainbows | 3–1 |
| 3 |  |
University of California at Berkeley | Bears | 2–2 |
| 4 |  |
University of California at Los Angeles | Bruins | 1–3 |
| 5 |  |
University of California at Santa Barbara | Gauchos | 0–4 |
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 15,867 team race matches sailed before 2021-10-31, using the line-ups that actually raced. It called 9 of 10 matches (90%) with a log-loss of 0.338 (a coin flip scores 0.693), so the event went well for the forecast. Orderliness −0.44 ± 0.62: a typical event, more orderly than 12% 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 | Stanford University Cardinal | 1 | 10.20 school +0.43, home +0.36 | Michelle Lahrkamp / Patricia Gerli 3.52 Stephanie Houck / Abigail Tindall 3.10 Hannah Freeman / Sammy Pickell | 100% | 4–0 (100%) | +0.0 |
| 2 | University of Hawaii Rainbows | 2 | 5.63 | Morgan Carew / Sophia Shaeffer Mercy Tangredi / Kelsie Grant Hayden Lahr / Jemma Baird | 69% | 3–1 (75%) | +0.2 |
| 3 | University of California at Berkeley Bears | 3 | 3.65 | Katherine Olsen / Audrey Turcotte Kingsley Ehrich / Lindsea Hayes 1.36 Mackenzie Berwick / Marlena Tavernier-Fine | 41% | 2–2 (50%) | +0.4 |
| 4 | University of California at Los Angeles Bruins | 5 | 1.18 school −0.43 | Mary Jane Howland / Emma Worthington Marianna Shand / Fadia Nitrosso Josephine Galvin / Siobhan Dornfried | 4% | 1–3 (25%) | +0.8 |
| 5 | University of California at Santa Barbara Gauchos | 4 | 3.54 | Katelon Egan / Eliana Tucker Molly Coghlin / Kaitlin Chan Harriet Jessup / Kate Gallagher | 36% | 0–4 (0%) | −1.4 |
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 | Stanford University | 10.20 | 13.20 | 4–0 | 4.0 | ≥+3.00 | 0.0 | 99% |
| 4 | University of California at Los Angeles | 1.18 | 3.41 | 1–3 | 0.2 | +2.23 | 1.6 | 16% |
| 2 | University of Hawaii | 5.63 | 7.29 | 3–1 | 2.8 | +1.66 | 0.2 | 77% |
| 3 | University of California at Berkeley | 3.65 | 4.57 | 2–2 | 1.6 | +0.92 | 0.2 | 60% |
| 5 | University of California at Santa Barbara | 3.54 | 0.54 | 0–4 | 1.4 | ≤−3.00 | 3.8 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Hannah Freeman | Stanford University | 2.69 | 5.69 | 4–0 | 4.0 | ≥+3.00 | 0.0 | 99% |
| Josephine Galvin | University of California at Los Angeles | -0.13 | 2.10 | 1–3 | 0.2 | +2.23 | 1.6 | 16% |
| Hayden Lahr | University of Hawaii | 1.68 | 3.34 | 3–1 | 2.8 | +1.66 | 0.2 | 77% |
| Katherine Olsen | University of California at Berkeley | 1.30 | 2.22 | 2–2 | 1.6 | +0.92 | 0.2 | 60% |
| Katelon Egan | University of California at Santa Barbara | 0.82 | -2.18 | 0–4 | 1.4 | ≤−3.00 | 3.8 | >99% |
| Harriet Jessup | University of California at Santa Barbara | 1.12 | -1.88 | 0–4 | 1.4 | ≤−3.00 | 3.8 | >99% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | Cardinal | Rainbows | Bears | Bruins | Gauchos | |
|---|---|---|---|---|---|---|---|
| Stanford University Cardinal | 4–0 | ✕ | W | W | W | W |
| University of Hawaii Rainbows | 3–1 | L | ✕ | W | W | W |
| University of California at Berkeley Bears | 2–2 | L | L | ✕ | W | W |
| University of California at Los Angeles Bruins | 1–3 | L | L | L | ✕ | W |
| University of California at Santa Barbara Gauchos | 0–4 | L | L | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Rainbows | 1-2-4 | def. | |
Bears | 3-5-6 |
| 2 | |
Cardinal | 1-2-3 | def. | |
Bruins | 4-5-6 |
| 3 | |
Bears | 1-3-5 | def. | |
Gauchos | 2-4-6 |
| 4 | |
Rainbows | 1-2-6 | def. | |
Bruins | 3-4-5 |
| 5 | |
Cardinal | 1-2-3 | def. | |
Gauchos | 4-5-6 |
| 6 | |
Bears | 2-3-4 | def. | |
Bruins | 1-5-6 |
| 7 | |
Rainbows | 1-2-6 | def. | |
Gauchos | 3-4-5 |
| 8 | |
Cardinal | 1-2-4 | def. | |
Bears | 3-5-6 |
| 9 | |
Bruins | 1-2-5 | def. | |
Gauchos | 3-4-6 |
| 10 | |
Cardinal | 1-2-3 | def. | |
Rainbows | 4-5-6 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
Stanford University | Cardinal | Hannah Freeman '24 Michelle Lahrkamp '23 Stephanie Houck '21 |
Sammy Pickell '22 Patricia Gerli '23 Abigail Tindall '23 |
|
University of Hawaii | Rainbows | Hayden Lahr '22 Mercy Tangredi '25 Morgan Carew '25 |
Jemma Baird '22 Kelsie Grant '23 Sophia Shaeffer '25 |
|
University of California at Berkeley | Bears | Katherine Olsen '25 Kingsley Ehrich '23 Mackenzie Berwick '22 |
Audrey Turcotte '25 Lindsea Hayes '22 Marlena Tavernier-Fine '22 |
|
University of California at Los Angeles | Bruins | Josephine Galvin '25 Marianna Shand '24 Mary Jane Howland '22 |
Siobhan Dornfried '25 Fadia Nitrosso '24 Emma Worthington '23 |
|
University of California at Santa Barbara | Gauchos | Harriet Jessup '22 Katelon Egan '24 Molly Coghlin '25 |
Kate Gallagher '25 Eliana Tucker '23 Kaitlin Chan '23 |
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 ±0.18 means a rating gap counts about 15% 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.
