| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 | University of California at Santa Barbara | Gauchos 1 | 15–0 | |
| 2 | University of California at Santa Barbara | Gauchos 2 | 10–5 | |
| 3 | California Maritime Academy | Keelhaulers | 9–6 | |
| 4 | University of California at Santa Barbara | Gauchos 3 | 5–10 | |
| 5 | Cal Poly University S.L.O. | Mustangs | 3–12 | |
| 6 | University of California at Berkeley | Bears | 3–12 |
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 14,108 team race matches sailed before 2020-02-01, using the line-ups that actually raced. It called 38 of 45 matches (84%) with a log-loss of 0.459 (a coin flip scores 0.693), so the event went about as expected for the forecast. Orderliness −0.43 ± 0.26: an upset-heavy event, more orderly than 13% 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 | University of California at Santa Barbara Gauchos 1 | 9.81 | 12.81 | 15–0 | 13.4 | ≥+3.00 | 2.5 | 16% |
| 6 | University of California at Berkeley Bears | 4.13 | 5.78 | 3–12 | 0.9 | +1.65 | 2.8 | 5% |
| 4 | University of California at Santa Barbara Gauchos 3 | 5.46 | 6.59 | 5–10 | 3.3 | +1.12 | 1.4 | 14% |
| 3 | California Maritime Academy Keelhaulers | 8.32 | 7.82 | 9–6 | 9.9 | −0.51 | 0.3 | 86% |
| 2 | University of California at Santa Barbara Gauchos 2 | 8.90 | 8.14 | 10–5 | 11.4 | −0.76 | 0.8 | 93% |
| 5 | Cal Poly University S.L.O. Mustangs | 6.75 | 4.73 | 3–12 | 6.1 | −2.01 | 4.5 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Quinn Wilson | University of California at Santa Barbara | 3.43 | 6.43 | 15–0 | 13.4 | ≥+3.00 | 2.5 | 16% |
| Sidney Gathrid | University of California at Santa Barbara | 2.88 | 5.88 | 15–0 | 13.4 | ≥+3.00 | 2.5 | 16% |
| Luc Lamontagne | University of California at Berkeley | 0.95 | 2.60 | 3–12 | 0.9 | +1.65 | 2.8 | 5% |
| Kerri Luttrell | University of California at Santa Barbara | 2.17 | 3.30 | 5–10 | 3.3 | +1.12 | 1.4 | 14% |
| Margot Mason | University of California at Santa Barbara | 1.40 | 2.52 | 5–10 | 3.3 | +1.12 | 1.4 | 14% |
| Ariana Fernandez | University of California at Santa Barbara | 1.11 | 2.23 | 5–10 | 3.3 | +1.12 | 1.4 | 14% |
| Jacob Fisker-Andersen | California Maritime Academy | 2.59 | 2.08 | 9–6 | 9.9 | −0.51 | 0.3 | 86% |
| Storm Brown | California Maritime Academy | 2.19 | 1.68 | 9–6 | 9.9 | −0.51 | 0.3 | 86% |
| Sampson Reynolds | University of California at Santa Barbara | 2.39 | 1.63 | 10–5 | 11.4 | −0.76 | 0.8 | 93% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | 1 | 2 | Keelhaulers | 3 | Mustangs | Bears | |
|---|---|---|---|---|---|---|---|---|
| University of California at Santa Barbara Gauchos 1 | 10–5 | ✕ | ✕ | W | ✕ | L | W | |
| University of California at Santa Barbara Gauchos 2 | 10–5 | ✕ | ✕ | W | ✕ | L | W | |
| California Maritime Academy Keelhaulers | 3–2 | L | L | ✕ | L | W | W | |
| University of California at Santa Barbara Gauchos 3 | 10–5 | ✕ | ✕ | W | ✕ | L | W | |
| Cal Poly University S.L.O. Mustangs | 1–4 | W | W | L | W | ✕ | L | |
| University of California at Berkeley Bears | 1–4 | L | L | L | L | W | ✕ |
| Team | Rec. | 1 | 2 | Keelhaulers | 3 | Mustangs | Bears | |
|---|---|---|---|---|---|---|---|---|
| University of California at Santa Barbara Gauchos 1 | 8–7 | ✕ | ✕ | W | ✕ | W | W | |
| University of California at Santa Barbara Gauchos 2 | 8–7 | ✕ | ✕ | W | ✕ | W | W | |
| California Maritime Academy Keelhaulers | 4–1 | L | L | ✕ | L | W | W | |
| University of California at Santa Barbara Gauchos 3 | 8–7 | ✕ | ✕ | W | ✕ | W | W | |
| Cal Poly University S.L.O. Mustangs | 1–4 | L | L | L | L | ✕ | W | |
| University of California at Berkeley Bears | 2–3 | L | L | L | L | L | ✕ |
| Team | Rec. | 1 | 2 | Keelhaulers | 3 | Mustangs | Bears | |
|---|---|---|---|---|---|---|---|---|
| University of California at Santa Barbara Gauchos 1 | 12–3 | ✕ | ✕ | W | ✕ | W | W | |
| University of California at Santa Barbara Gauchos 2 | 12–3 | ✕ | ✕ | W | ✕ | W | W | |
| California Maritime Academy Keelhaulers | 2–3 | L | L | ✕ | L | W | W | |
| University of California at Santa Barbara Gauchos 3 | 12–3 | ✕ | ✕ | W | ✕ | W | W | |
| Cal Poly University S.L.O. Mustangs | 1–4 | L | L | L | L | ✕ | W | |
| University of California at Berkeley Bears | 0–5 | L | L | L | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | Gauchos 2 | 1-2-5 | def. | Bears | 3-4-6 | ||
| 2 | Keelhaulers | 1-2-4 | def. | Mustangs | 3-5-6 | ||
| 3 | Gauchos 1 | 1-2-4 | def. | Gauchos 3 | 3-5-6 | ||
| 4 | Gauchos 2 | 1-2-3 | def. | Keelhaulers | 4-5-6 | ||
| 5 | Gauchos 3 | 1-2-5 | def. | Bears | 3-4-6 | ||
| 6 | Gauchos 1 | 1-2-5 | def. | Mustangs | 3-4-6 | ||
| 7 | Keelhaulers | 2-3-4 | def. | Gauchos 3 | 1-5-6 | ||
| 8 | Gauchos 2 | 1-2-5 | def. | Mustangs | 3-4-6 | ||
| 9 | Gauchos 1 | 1-2-5 | def. | Bears | 3-4-6 | ||
| 10 | Mustangs | 1-2-5 | def. | Gauchos 3 | 3-4-6 | ||
| 11 | Keelhaulers | 1-2-4 | def. | Bears | 3-5-6 | ||
| 12 | Gauchos 1 | 2-3-4 | def. | Gauchos 2 | 1-5-6 | ||
| 13 | Bears | 1-2-5 | def. | Mustangs | 3-4-6 | ||
| 14 | Gauchos 2 | 1-2-5 | def. | Gauchos 3 | 3-4-6 | ||
| 15 | Gauchos 1 | 1-3-6 | def. | Keelhaulers | 2-4-5 | ||
| 16 | Bears | 2-3-5 | def. | Gauchos 2 | 1-4-6 | ||
| 17 | Keelhaulers | 1-2-3 | def. | Mustangs | 4-5-6 | ||
| 18 | Gauchos 1 | 1-2-5 | def. | Gauchos 3 | 3-4-6 | ||
| 19 | Keelhaulers | 2-3-4 | def. | Gauchos 2 | 1-5-6 | ||
| 20 | Bears | 2-3-5 | def. | Gauchos 3 | 1-4-6 | ||
| 21 | Gauchos 1 | 1-2-5 | def. | Mustangs | 3-4-6 | ||
| 22 | Keelhaulers | 1-2-6 | def. | Gauchos 3 | 3-4-5 | ||
| 23 | Gauchos 2 | 1-2-6 | def. | Mustangs | 3-4-5 | ||
| 24 | Gauchos 1 | 1-2-5 | def. | Bears | 3-4-6 | ||
| 25 | Gauchos 3 | 1-2-5 | def. | Mustangs | 3-4-6 | ||
| 26 | Keelhaulers | 1-2-5 | def. | Bears | 3-4-6 | ||
| 27 | Gauchos 1 | 1-3-5 | def. | Gauchos 2 | 2-4-6 | ||
| 28 | Mustangs | 1-3-5 | def. | Bears | 2-4-6 | ||
| 29 | Gauchos 2 | 1-2-5 | def. | Gauchos 3 | 3-4-6 | ||
| 30 | Gauchos 1 | 2-3-5 | def. | Keelhaulers | 1-4-6 | ||
| 31 | Gauchos 2 | 1-2-5 | def. | Bears | 3-4-6 | ||
| 32 | Keelhaulers | 1-2-5 | def. | Mustangs | 3-4-6 | ||
| 33 | Gauchos 1 | 2-3-4 | def. | Gauchos 3 | 1-5-6 | ||
| 34 | Gauchos 2 | 2-3-4 | def. | Keelhaulers | 1-5-6 | ||
| 35 | Gauchos 3 | 1-2-3 | def. | Bears | 4-5-6 | ||
| 36 | Gauchos 1 | 1-2-5 | def. | Mustangs | 3-4-6 | ||
| 37 | Gauchos 3 | 2-3-4 | def. | Keelhaulers | 1-5-6 | ||
| 38 | Gauchos 2 | 1-2-5 | def. | Mustangs | 3-4-6 | ||
| 39 | Gauchos 1 | 1-2-5 | def. | Bears | 3-4-6 | ||
| 40 | Gauchos 3 | 1-2-6 | def. | Mustangs | 3-4-5 | ||
| 41 | Keelhaulers | 1-2-3 | def. | Bears | 4-5-6 | ||
| 42 | Gauchos 1 | 1-3-5 | def. | Gauchos 2 | 2-4-6 | ||
| 43 | Mustangs | 1-2-4 | def. | Bears | 3-5-6 | ||
| 44 | Gauchos 2 | 1-2-6 | def. | Gauchos 3 | 3-4-5 | ||
| 45 | Gauchos 1 | 1-2-6 | def. | Keelhaulers | 3-4-5 | ||
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
| University of California at Santa Barbara | Gauchos 1 | David Eastwood '22 Quinn Wilson '21 Sidney Gathrid '22 |
Tristan Richmond '22 Elizabeth Moody '20 Jake Zieba '22 |
|
| University of California at Santa Barbara | Gauchos 2 | Chris Kayda '23 Lucas Pierce '21 Sampson Reynolds '21 |
Jack Holbrook '21 Julia Roudebush '23 Sophie Thompson '21 |
|
| California Maritime Academy | Keelhaulers | Brock Paquin '22 Jacob Fisker-Andersen '21 Storm Brown '20 |
Hailey Thompson '22 Kian Patrick '20 Justin Zmina '23 |
|
| University of California at Santa Barbara | Gauchos 3 | Ariana Fernandez '23 Kerri Luttrell '21 Margot Mason '21 |
Sophia Jacobs '23 Olivia Beers '21 Pyper Atkin '20 |
|
| Cal Poly University S.L.O. | Mustangs | Josselyn Verutti '21 Kai Ponting '23 Liam Hood '20 |
Maddie Roman '20 Peyton Nienaber '22 Alyson Crowley '20 |
|
| University of California at Berkeley | Bears | Drake Hayes '21 Luc Lamontagne '22 Sarah Paulsen '20 |
Alex Ferry '21 Rosie Ward '23 Lindsea Hayes '22 |
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 ±0.03 means a rating gap counts about 2% 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.