Corona del Mar High School '24
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: 6.13 percentile among ICSA crews with 30+ races. Uncertainty ±0.091 rating points from 141 races with rated skippers.
How crew ratings work →Includes linked high school and college seasons.
| Season | Team | Rating | Range | League percentile |
Team percentile |
On team | Races | Team page |
|---|---|---|---|---|---|---|---|---|
| Spring 2026 | Stanford University | 2.36 | 75.00 | 5.56 | 9 of 9 | 13 | Team page | |
| Fall 2025 | Stanford University | 2.54 | 77.05 | 12.50 | 11 of 12 | 37 | Team page | |
| Spring 2025 | Stanford University | 2.27 | 68.24 | 6.25 | 8 of 8 | 16 | Team page | |
| Fall 2024 | Stanford University | 2.25 | 69.91 | 4.55 | 11 of 11 | 67 | Team page |
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 |
|---|---|---|---|
| 2.19 (fleet 3.57) | unranked (fewer than 30 matches as skipper, or not active) | −0.02 ± 0.18 about what the fleet rating predicts |
as skipper 13–35 (27%) |
| Season | Regattas | Skipper W–L | Crew W–L |
|---|---|---|---|
| Spring 2024 | 1 | 3–9 | — |
| Spring 2023 | 1 | 3–15 | — |
| Spring 2022 | 1 | 7–11 | — |
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 3 graded regattas (48 matches) the record is 13–35 against 17.3 wins the pre-event forecasts expected (−4.3). 0 regattas clearly above the rating (P ≤ 20%), 2 clearly below (P ≥ 80%); 4 bits of surprise in all.
| Date | Regatta | Role | Value | Perf. | Record | Exp. wins |
Δ | Surprise (bits) |
P(this good or better) |
|---|---|---|---|---|---|---|---|---|---|
| 2024-04-13 | PCISA Team Race PCCs 2024 | skipper | 2.42 | 0.64 | 3–9 | 5.9 | −1.78 | 2.7 | 99% |
| 2023-04-22 | PCISA Team Race Championship - Baker Elims | skipper | 2.44 | 1.33 | 3–15 | 5.4 | −1.10 | 1.4 | 95% |
| 2022-04-23 | 2022 Team Race PCCs: Baker Elims | skipper | 1.81 | 2.17 | 7–11 | 6.0 | +0.36 | 0.2 | 38% |
| League | Name | School | Year |
|---|---|---|---|
| ICSA | Piper Blackband |  Stanford University |
'28 |
Short write-ups of what the ratings and results show, with the numbers and how they were computed.
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