Rumson-Fair Haven '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: 99.43 percentile among ICSA crews with 30+ races. Uncertainty ±0.070 rating points from 510 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 2024 | University of Pennsylvania | 6.73 | 99.75 | 90.00 | 1 of 5 | 21 | Team page | |
| Fall 2023 | University of Pennsylvania | 6.49 | 99.69 | 95.83 | 1 of 12 | 36 | Team page | |
| Spring 2023 | University of Pennsylvania | 6.80 | 99.74 | 94.44 | 1 of 9 | 44 | Team page | |
| Fall 2022 | University of Pennsylvania | 6.77 | 99.78 | 95.45 | 1 of 11 | 73 | 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 |
|---|---|---|---|
| 1.50 (fleet 2.41) | unranked (fewer than 30 matches as skipper, or not active) | +0.00 ± 0.18 about what the fleet rating predicts |
as skipper 31–17 (65%) |
| Season | Regattas | Skipper W–L | Crew W–L |
|---|---|---|---|
| Spring 2019 | 1 | 8–0 | — |
| Fall 2018 | 1 | 5–2 | — |
| Spring 2018 | 3 | 13–12 | — |
| Fall 2017 | 2 | 5–3 | — |
Rating before each regatta driven (solid; dots green when the boat won 60% or more of its matches there, red under 40%) and what the fleet rating alone would give (dashed). The gap is the team race offset as it stood at the time. Hover a dot for the regatta; click to open it.
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 5 graded regattas (44 matches) the record is 29–15 against 27.7 wins the pre-event forecasts expected (+1.3). 1 regatta clearly above the rating (P ≤ 20%), 2 clearly below (P ≥ 80%); 8 bits of surprise in all.
| Date | Regatta | Role | Value | Perf. | Record | Exp. wins |
Δ | Surprise (bits) |
P(this good or better) |
|---|---|---|---|---|---|---|---|---|---|
| 2019-04-08 | NJISA 2019 Spring Team Race Qualifier | skipper | 1.68 | 4.68 | 8–0 | 4.9 | ≥+3.00 | 6.2 | 1% |
| 2018-09-30 | NJISA Fall Team Champs | skipper | 1.50 | 1.03 | 5–2 | 5.3 | −0.48 | 0.1 | 82% |
| 2018-04-28 | MASSA Team Championship Baker Qualifier | skipper | 1.34 | 0.37 | 8–9 | 10.1 | −0.96 | 1.1 | 94% |
| 2018-04-21 | NJISA Spring Team Champs | skipper | 1.51 | 2.43 | 5–1 | 4.3 | +0.91 | 0.4 | 44% |
| 2017-11-04 | MASSA 2017 Fall Team Race Championship | skipper | 1.41 | 1.28 | 3–3 | 3.1 | −0.14 | 0.0 | 73% |
| League | Name | School | Year |
|---|---|---|---|
| ICSA | Shaanti Choi-Bose |  University of Pennsylvania |
'24 |
Short write-ups of what the ratings and results show, with the numbers and how they were computed.
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