| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
Stanford University | Cardinal | 9–0 |
| 2 |  |
University of California at Santa Barbara | Gauchos | 5–4 |
| 3 |  |
University of Hawaii | Rainbows | 4–5 |
| 4 |  |
California State University Long Beach | 49ers | 0–9 |
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 3,748 team race matches sailed before 2014-05-03, using the line-ups that actually raced. It called 16 of 18 matches (89%) with a log-loss of 0.175 (a coin flip scores 0.693), so the event went well for the forecast. Orderliness +0.16 ± 0.41: a typical event, more orderly than 62% 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 | 12.48 school +0.27, home +0.33 | Mateo Vargas / Mckenzie Wilson 3.83 6–0 Kevin Laube / John Cannistraro 4.03 6–0 Oliver Toole / Helena Scutt 3.82 6–0 Mateo Vargas / Tally Buckstaff 4.05 3–0 Kevin Laube / Yuri Namikawa 4.29 3–0 Oliver Toole / Carolyn Ortel 3.89 3–0 | 98% | 9–0 (100%) | +0.2 |
| 2 | University of California at Santa Barbara Gauchos | 3 | 8.72 school −0.08 | Stephen Long / Ginger Luckey 3.40 Ian Stokes / Natalie Davidson 3.22 Grant Rickon / Kristina Miller 2.11 | 43% | 5–4 (56%) | +1.1 |
| 3 | University of Hawaii Rainbows | 2 | 9.51 school +0.16 | Madeline Kennedy / Katrina Berry 2.94 Adam Pokras / Ana-Melissa Kea 3.54 Chuck Eaton / Giacomo Paoletti 2.79 4–2 Chuck Eaton / Kellie Yamada 2.90 0–3 | 56% | 4–5 (44%) | −1.0 |
| 4 | California State University Long Beach 49ers | 4 | 5.97 school −0.57 | John Hill / Sunny Scarbrough 2.29 Mark Ryan / Nicholas Santos 2.49 Tyler Webb / Alex Newton | 3% | 0–9 (0%) | −0.3 |
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 | 12.48 | 15.48 | 9–0 | 8.8 | ≥+3.00 | 0.3 | 82% |
| 2 | University of California at Santa Barbara | 8.72 | 9.88 | 5–4 | 3.9 | +1.17 | 1.0 | 24% |
| 3 | University of Hawaii | 9.51 | 8.39 | 4–5 | 5.1 | −1.11 | 0.9 | 95% |
| 4 | California State University Long Beach | 5.97 | 2.97 | 0–9 | 0.3 | ≤−3.00 | 0.4 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Mateo Vargas | Stanford University | 3.83 | 6.83 | 9–0 | 8.8 | ≥+3.00 | 0.3 | 82% |
| Kevin Laube | Stanford University | 4.03 | 7.03 | 9–0 | 8.8 | ≥+3.00 | 0.3 | 82% |
| Oliver Toole | Stanford University | 3.82 | 6.82 | 9–0 | 8.8 | ≥+3.00 | 0.3 | 82% |
| Stephen Long | University of California at Santa Barbara | 3.40 | 4.56 | 5–4 | 3.9 | +1.17 | 1.0 | 24% |
| Ian Stokes | University of California at Santa Barbara | 3.22 | 4.39 | 5–4 | 3.9 | +1.17 | 1.0 | 24% |
| Grant Rickon | University of California at Santa Barbara | 2.11 | 3.28 | 5–4 | 3.9 | +1.17 | 1.0 | 24% |
| Chuck Eaton | University of Hawaii | 2.79 | 1.67 | 4–5 | 5.1 | −1.11 | 0.9 | 95% |
| Madeline Kennedy | University of Hawaii | 2.94 | 1.82 | 4–5 | 5.1 | −1.11 | 0.9 | 95% |
| Adam Pokras | University of Hawaii | 3.54 | 2.42 | 4–5 | 5.1 | −1.11 | 0.9 | 95% |
| Mark Ryan | California State University Long Beach | 2.49 | -0.51 | 0–9 | 0.3 | ≤−3.00 | 0.4 | >99% |
| Tyler Webb | California State University Long Beach | 1.36 | -1.64 | 0–9 | 0.3 | ≤−3.00 | 0.4 | >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 | 49ers | |
|---|---|---|---|---|---|---|
| Stanford University Cardinal | 3–0 | ✕ | W | W | W |
| University of California at Santa Barbara Gauchos | 2–1 | L | ✕ | W | W |
| University of Hawaii Rainbows | 1–2 | L | L | ✕ | W |
| California State University Long Beach 49ers | 0–3 | L | L | L | ✕ |
| Team | Rec. | Cardinal | Gauchos | Rainbows | 49ers | |
|---|---|---|---|---|---|---|
| Stanford University Cardinal | 3–0 | ✕ | W | W | W |
| University of California at Santa Barbara Gauchos | 2–1 | L | ✕ | W | W |
| University of Hawaii Rainbows | 1–2 | L | L | ✕ | W |
| California State University Long Beach 49ers | 0–3 | L | L | L | ✕ |
| Team | Rec. | Cardinal | Gauchos | Rainbows | 49ers | |
|---|---|---|---|---|---|---|
| Stanford University Cardinal | 3–0 | ✕ | W | W | W |
| University of California at Santa Barbara Gauchos | 1–2 | L | ✕ | L | W |
| University of Hawaii Rainbows | 2–1 | L | W | ✕ | W |
| California State University Long Beach 49ers | 0–3 | L | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Cardinal | 1-2-3 | def. | |
Rainbows | 4-5-6 |
| 2 | |
Gauchos | 1-2-4 | def. | |
49ers | 3-5-6 |
| 3 | |
Gauchos | 1-4-5 | def. | |
Rainbows | 2-3-6 |
| 4 | |
Cardinal | 1-2-3 | def. | |
49ers | 4-5-6 |
| 5 | |
Cardinal | 1-2-3 | def. | |
Gauchos | 4-5-6 |
| 6 | |
Rainbows | 1-2-5 | def. | |
49ers | 3-4-6 |
| 7 | |
Cardinal | 1-2-5 | def. | |
Rainbows | 3-4-6 |
| 8 | |
Gauchos | 1-2-5 | def. | |
49ers | 3-4-6 |
| 9 | |
Gauchos | 2-3-4 | def. | |
Rainbows | 1-5-6 |
| 10 | |
Cardinal | 1-2-4 | def. | |
49ers | 3-5-6 |
| 11 | |
Cardinal | 1-2-3 | def. | |
Gauchos | 4-5-6 |
| 12 | |
Rainbows | 1-2-3 | def. | |
49ers | 4-5-6 |
| 13 | |
Cardinal | 1-3-6 | def. | |
Rainbows | 2-4-5 |
| 14 | |
Gauchos | 2-3-4 | def. | |
49ers | 1-5-6 |
| 15 | |
Rainbows | 2-3-5 | def. | |
Gauchos | 1-4-6 |
| 16 | |
Cardinal | 1-2-3 | def. | |
49ers | 4-5-6 |
| 17 | |
Cardinal | 1-2-3 | def. | |
Gauchos | 4-5-6 |
| 18 | |
Rainbows | 2-3-4 | def. | |
49ers | 1-5-6 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
Stanford University | Cardinal | Kevin Laube '14 Mateo Vargas '14 Oliver Toole '14 |
Yuri Namikawa '14 John Cannistraro '17 Tally Buckstaff '14 Mckenzie Wilson '14 Carolyn Ortel '14 Helena Scutt '14 |
|
University of California at Santa Barbara | Gauchos | Grant Rickon '16 Ian Stokes '16 Stephen Long '17 |
Kristina Miller '17 Natalie Davidson '16 Ginger Luckey '16 |
|
University of Hawaii | Rainbows | Adam Pokras '14 Chuck Eaton '17 Madeline Kennedy '14 |
Ana-Melissa Kea '13 Kellie Yamada '16 Giacomo Paoletti '15 Katrina Berry '15 |
|
California State University Long Beach | 49ers | John Hill '13 Mark Ryan '15 Tyler Webb '15 |
Sunny Scarbrough '17 Nicholas Santos '15 Alex Newton '16 |
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 ±0.01 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.
