| # | School | Team | A | TOT | |
|---|---|---|---|---|---|
| 1 |  |
Stanford University | Cardinal | 1 | 1 |
| 2 |  |
University of California at Santa Barbara | Gauchos | 2 | 2 |
| 3 |  |
California Maritime Academy | Keelhaulers | 3 | 3 |
| 4 |  |
University of Southern California | Trojans | 4 | 4 |
| 5 |  |
University of Hawaii | Rainbows | 5 | 5 |
| 6 |  |
University of California at Berkeley | Bears | 6 | 6 |
| School | Team | R1 | TOT | |
|---|---|---|---|---|
|
Stanford University | Cardinal | 1 | 1 |
|
University of California at Santa Barbara | Gauchos | 2 | 2 |
|
California Maritime Academy | Keelhaulers | 3 | 3 |
|
University of Southern California | Trojans | 4 | 4 |
|
University of Hawaii | Rainbows | 5 | 5 |
|
University of California at Berkeley | Bears | 6 | 6 |
| School | Team | Sailors | |
|---|---|---|---|
|
California Maritime Academy | Keelhaulers | SkipperJohannes McElvain '18 CrewEmi Stephanoff '21 CrewNolan Van Dine '20 |
|
Stanford University | Cardinal | SkipperRomain Screve '21 CrewElena VandenBerg '18 CrewJack Parkin '21 |
|
University of California at Berkeley | Bears | ReserveCole Fargo '18 ReserveKatey Hayes '20 ReserveAlexandre Mutricy '17 |
|
University of California at Santa Barbara | Gauchos | SkipperChristopher Weis '18 CrewDylan Finestone '19 CrewClaire Hunt '19 |
|
University of Hawaii | Rainbows | SkipperMichael Pacholski '19 CrewCuylar Zimmerman '19 CrewCaitlin Schadt '18 |
|
University of Southern California | Trojans | SkipperPaul Chyz '18 CrewSarah Alexander '19 CrewPeter Wong '18 |
Performance rating asks, for each skipper: holding every other boat fixed, which rating makes my actual race-by-race finishes least surprising? Boats are valued with the keelboat rating rather than the fleet rating: 0.41 × the skipper's fleet rating from the week before the event (unrated skippers start at 0.47); 0.28 × the mean fleet rating of the rated crew aboard; the skipper's own keelboat offset; the school's program effect. Non-finishes (OCS, DNF and similar) are left out of the fitting, as in the rating model.
Percentile places a rating among the 207 ICSA sailors with a keelboat rating who raced the discipline in the last three seasons. Surprise is how many bits more surprising the finishes were at the pre-event rating than at the performance rating. P(this good or better) simulates the event 3,000 times at the pre-event ratings, with the same regatta-form model as the fleet analysis pages, and reports how often the skipper would score this many points or fewer in the races they sailed.
The keelboat offsets are the current ones (the discipline files keep no weekly history), so a skipper's own offset has already seen this event; offsets are shrunk hard, so the effect is a few hundredths at most. Only skippers are rated.
| Fin | Skipper | School | Races | Rating before |
Perf. rating |
Δ | Percentile before → perf. |
Surprise (bits) |
P(this good or better) |
Avg finish actual / exp. |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Romain Screve '21 | Stanford University | 1/1 | 2.59 | 7.00+ | +4.41 | 99th → 99th | 1.9 | 23% | 1.0 / 3.0 |
| 2 | Christopher Weis '18 | University of California at Santa Barbara | 1/1 | 2.15 | 3.62 | +1.47 | 99th → 99th | 0.7 | 32% | 2.0 / 3.5 |
| 3 | Johannes McElvain '18 | California Maritime Academy | 1/1 | 2.07 | 2.77 | +0.69 | 99th → 99th | 0.2 | 48% | 3.0 / 3.6 |
| 4 | Paul Chyz '18 | University of Southern California | 1/1 | 2.16 | 2.12 | −0.04 | 99th → 99th | 0.0 | 68% | 4.0 / 3.5 |
| 5 | Michael Pacholski '19 | University of Hawaii | 1/1 | 1.80 | 1.63 | −0.17 | 96th → 92nd | 0.0 | 76% | 5.0 / 4.0 |
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% 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.
