| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
Stanford University | Cardinal | 5–0 |
| 2 |  |
University of California at Santa Barbara | Gauchos | 4–1 |
| 3 |  |
University of Hawaii | Rainbows | 3–2 |
| 4 |  |
University of California at Berkeley | Bears | 2–3 |
| 5 |  |
University of Southern California | Trojans | 1–4 |
| 6 |  |
Cal Poly University S.L.O. | Mustang | 0–5 |
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 7,077 team race matches sailed before 2016-04-24, using the line-ups that actually raced. It called 12 of 15 matches (80%) with a log-loss of 0.328 (a coin flip scores 0.693), so the event went well for the forecast. Orderliness −0.14 ± 0.50: a typical event, more orderly than 28% 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 | Stanford University | 12.39 | 15.39 | 5–0 | 5.0 | ≥+3.00 | 0.0 | 96% |
| 2 | University of California at Santa Barbara | 7.80 | 10.57 | 4–1 | 2.8 | +2.77 | 2.0 | 19% |
| 4 | University of California at Berkeley | 5.65 | 7.28 | 2–3 | 1.1 | +1.63 | 1.2 | 21% |
| 3 | University of Hawaii | 8.02 | 8.00 | 3–2 | 3.0 | −0.02 | 0.0 | 75% |
| 5 | University of Southern California | 7.81 | 4.89 | 1–4 | 2.8 | −2.92 | 3.7 | >99% |
| 6 | Cal Poly University S.L.O. | 4.47 | 1.47 | 0–5 | 0.3 | ≤−3.00 | 0.5 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Axel Sly | Stanford University | 3.65 | 6.65 | 5–0 | 5.0 | ≥+3.00 | 0.0 | 96% |
| Antoine Screve | Stanford University | 4.00 | 7.00 | 5–0 | 5.0 | ≥+3.00 | 0.0 | 96% |
| Sterling Henken | University of California at Santa Barbara | 2.92 | 5.69 | 4–1 | 2.8 | +2.77 | 2.0 | 19% |
| No Show | University of California at Santa Barbara | 1.59 | 4.36 | 4–1 | 2.8 | +2.77 | 2.0 | 19% |
| Andrew Solomon | University of California at Berkeley | 2.51 | 4.14 | 2–3 | 1.1 | +1.63 | 1.2 | 21% |
| Charles Eaton Iv | University of Hawaii | 2.96 | 2.94 | 3–2 | 3.0 | −0.02 | 0.0 | 75% |
| Giacomo Paoletti | University of Hawaii | 2.61 | 2.59 | 3–2 | 3.0 | −0.02 | 0.0 | 75% |
| John Coakley | University of Southern California | 2.67 | -0.25 | 1–4 | 2.8 | −2.92 | 3.7 | >99% |
| Peter Wong | University of Southern California | 2.33 | -0.59 | 1–4 | 2.8 | −2.92 | 3.7 | >99% |
| Ben Brough | Cal Poly University S.L.O. | 1.96 | -1.04 | 0–5 | 0.3 | ≤−3.00 | 0.5 | >99% |
| Maria-Elena Falahee-Walker | Cal Poly University S.L.O. | 0.70 | -2.30 | 0–5 | 0.3 | ≤−3.00 | 0.5 | >99% |
| Joseph Lausten | Cal Poly University S.L.O. | 1.53 | -1.47 | 0–5 | 0.3 | ≤−3.00 | 0.5 | >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 | Gauchos | Rainbows | Bears | Trojans | Mustang | |
|---|---|---|---|---|---|---|---|---|
| Stanford University Cardinal | 5–0 | ✕ | W | W | W | W | W |
| University of California at Santa Barbara Gauchos | 4–1 | L | ✕ | W | W | W | W |
| University of Hawaii Rainbows | 3–2 | L | L | ✕ | W | W | W |
| University of California at Berkeley Bears | 2–3 | L | L | L | ✕ | W | W |
| University of Southern California Trojans | 1–4 | L | L | L | L | ✕ | W |
| Cal Poly University S.L.O. Mustang | 0–5 | L | L | L | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Rainbows | 2-3-4 | def. | |
Trojans | 1-5-6 |
| 2 | |
Gauchos | 1-2-5 | def. | |
Bears | 3-4-6 |
| 3 | |
Cardinal | 1-2-3 | def. | |
Mustang | 4-5-6 |
| 4 | |
Gauchos | 1-3-6 | def. | |
Rainbows | 2-4-5 |
| 5 | |
Trojans | 1-3-4 | def. | |
Mustang | 2-5-6 |
| 6 | |
Cardinal | 1-2-4 | def. | |
Bears | 3-5-6 |
| 7 | |
Gauchos | 1-2-6 | def. | |
Mustang | 3-4-5 |
| 8 | |
Rainbows | 1-2-4 | def. | |
Bears | 3-5-6 |
| 9 | |
Cardinal | 1-2-3 | def. | |
Trojans | 4-5-6 |
| 10 | |
Bears | 1-2-5 | def. | |
Mustang | 3-4-6 |
| 11 | |
Gauchos | 1-2-6 | def. | |
Trojans | 3-4-5 |
| 12 | |
Cardinal | 1-2-5 | def. | |
Rainbows | 3-4-6 |
| 13 | |
Bears | 1-2-4 | def. | |
Trojans | 3-5-6 |
| 14 | |
Rainbows | 1-2-3 | def. | |
Mustang | 4-5-6 |
| 15 | |
Cardinal | 1-2-3 | def. | |
Gauchos | 4-5-6 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
Stanford University | Cardinal | Antoine Screve '16 Axel Sly '16 Will La Dow '18 |
Samantha Steele '16 Sarah Placek '18 Nicolette Obel '17 |
|
University of California at Santa Barbara | Gauchos | Christopher Weis '18 No Show Sterling Henken '18 |
Claire Hunt '19 Kristina Miller '17 Neil Stapleton '18 Ginger Luckey '16 |
|
University of Hawaii | Rainbows | Charles Eaton Iv '17 Giacomo Paoletti '15 Michael Pacholski '19 Soren Wilde '19 |
Kellie Yamada '16 Patrick Manuel '16 Cole Brauer '17 Annika Garrett '19 |
|
University of California at Berkeley | Bears | Andrew Solomon '18 Kyra Oakes '18 Nicolai Sponholtz '19 |
Chun Ting Justin Lai '16 Noah Stevenson '19 Olivia Macdonald '19 |
|
University of Southern California | Trojans | John Coakley '16 Matthew Hecht '16 Peter Wong '18 |
Melissa Schem '18 Mackenzie Freeman '18 Paul Chyz '18 |
|
Cal Poly University S.L.O. | Mustang | Ben Brough '19 Joseph Lausten '18 Maria-Elena Falahee-Walker '19 |
Helen Payne '19 Belle Lemoine '19 Dallas Butler '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 ±0.00 means a rating gap counts about 1% 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.
