Cornell University '19
How much better or worse boats do with this sailor crewing than their skipper's rating predicts, shown on the skipper scale so crews can be lined up across teams. Percentiles compare against rostered crews that season (league) or teammates (team).
How crew ratings work →Crew effect over their whole career, not scaled to a team: 92.97 percentile among ICSA crews with 30+ races. Uncertainty ±0.075 rating points from 291 races with rated skippers.
How crew ratings work →No season rating yet: needs 8+ crewed races in a season.
A team race rating is what the sailor brings to one of the three boats: 0.72 x their fleet rating plus a team-race offset learned from every match they sailed, shrunk toward zero so a few matches cannot move it far. Sides are compared by the sum of their three boats plus the school's team race strength.
| Rating | Rank | vs fleet rating | Record |
|---|---|---|---|
| 0.80 (fleet 1.11) | unranked (fewer than 30 matches as skipper, or not active) | +0.00 about what the fleet rating predicts |
as crew 72–71 (50%) |
| Season | Regattas | Skipper W–L | Crew W–L |
|---|---|---|---|
| Spring 2019 | 3 | — | 30–20 |
| Spring 2018 | 6 | — | 42–51 |
For each regatta, the boat value that would have made this sailor's record least surprising: the shift applied to their boat alone, in the matches they sailed, with team-mates and opponents fixed at the pre-event forecast. A boat's skipper and crew share the boat, so they get the same number when they sailed every match together. Surprise is in bits at the pre-event value minus at the performance value; P(this good or better) treats the matches as independent at the forecast probabilities. Regattas forecast from fewer than 1,000 training matches are left out.
Over 9 graded regattas (143 matches) the record is 72–71 against 71.0 wins the pre-event forecasts expected (+1.0). 1 regatta clearly above the rating (P ≤ 20%), 0 clearly below (P ≥ 80%); 3 bits of surprise in all.
| Date | Regatta | Role | Value | Perf. | Record | Exp. wins |
Δ | Surprise (bits) |
P(this good or better) |
|---|---|---|---|---|---|---|---|---|---|
| 2019-04-13 | Women's Team Race National Invitational | crew | 2.98 | 2.79 | 11–8 | 11.7 | −0.19 | 0.1 | 74% |
| 2019-04-06 | Prosser Trophy | crew | 2.94 | 2.90 | 4–10 | 4.1 | −0.04 | 0.0 | 63% |
| 2019-03-30 | Team Race Challenge | crew | 2.89 | 3.34 | 15–2 | 14.3 | +0.45 | 0.2 | 45% |
| 2018-05-26 | LaserPerformance Team Race National Championship | crew | 2.61 | 2.33 | 3–7 | 3.3 | −0.28 | 0.1 | 77% |
| 2018-04-07 | Capt. Prosser Trophy - MAISA TR Championship | crew | 2.94 | 3.10 | 9–12 | 8.3 | +0.16 | 0.1 | 46% |
| 2018-03-31 | Team Race Challenge | crew | 2.89 | 4.29 | 14–1 | 11.8 | +1.40 | 1.9 | 13% |
| 2018-03-24 | Aaron Szambecki Team Race | crew | 2.62 | 2.28 | 3–11 | 3.8 | −0.34 | 0.2 | 79% |
| 2018-03-17 | Graham Hall Team Race | crew | 2.32 | 2.03 | 5–11 | 5.7 | −0.29 | 0.1 | 78% |
| 2018-03-10 | John Jackson Memorial Team Race | crew | 2.89 | 2.86 | 8–9 | 8.1 | −0.04 | 0.0 | 64% |
| League | Name | School | Year |
|---|---|---|---|
| ISSA | Kimberly Wong |  Sage Hill School |
'15 |
Short write-ups of what the ratings and results show, with the numbers and how they were computed.
