Bears Invite — Analysis

← Results

Performance rating answers one question for each skipper: holding every other skipper's rating fixed, which rating makes my actual race-by-race finishes least surprising? It is the maximum-likelihood rating under the same Plackett–Luce model that produces the power rankings (how accurate it is), with each opponent fixed at their rating from the last rated week before this event (2026-W37). Substitute skippers are scored only on the races they sailed, and races the boat did not finish (OCS, DNF and similar) are left out.

Percentile places a rating among the 1,907 ICSA skippers who were rated within the year before this event, so it reads as “better than X% of the skippers racing in this league right now”. The arrow shows where the sailor stood going in and where this performance would put them.

Surprise is how many bits more surprising the sailor's finishes were at their pre-event rating than at their performance rating (0 = exactly as expected). P(this good or better) simulates the regatta 3,000 times with the sailor at their pre-event rating against the actual fleet, and reports how often they would score this many points or fewer in the races they skippered. Races in one regatta are not independent, so each simulation also gives every skipper a good or bad regatta, form that drifts from race to race, and the speed of each boat the rotation puts them in (why). Low means an unusually good regatta, high means an unusually poor one. It deliberately ignores how rivals actually sailed, so it agrees with the performance rating rather than with the division finish, which also depends on everyone else's day. Ratings are shown on the rankings scale (model rating × 100). Only skippers are rated; crews are not part of the model.

  • n no rating before this event — the model's estimate from this week is used as the baseline.

Division A  18 boats · 10 races

Fin Skipper School Races Rating
before
Perf.
rating
Δ Percentile
before → perf.
Surprise
(bits)
P(this good
or better)
Avg finish
actual / exp.
Rating
after
1 Nathan Pine '30 Brown University Bears 1 10/10 309 363 +53 92nd → 96th 1.4 20% 5.0 / 7.4 319
2 Leonardo Burnham '29 University of Rhode Island Rams 1 10/10 271 362 +91 87th → 96th 3.9 11% 5.1 / 8.6 289
3 Reece Schwartz '29 Roger Williams University Hawks 1 10/10 338 388 +50 94th → 98th 1.1 41% 5.6 / 6.7 343
4 Thomas O'Grady '30 Yale University Bulldogs 1 10/10 301 300 -1 91st → 91st 0.0 39% 6.7 / 7.7 310
5 Dylan Seawards '30 Roger Williams University Hawks 2 10/10 310 300 -10 92nd → 91st 0.1 52% 7.3 / 7.4 310
6 Crue Ziskind '29 Tufts University Jumbos 1 10/10 309 273 -37 92nd → 87th 0.9 66% 8.3 / 7.4 306
7 Andrew Lamm '30 Brown University Bears 2 10/10 254 258 +4 85th → 86th 0.0 40% 8.4 / 9.3 270
8 Liam Marcell '30 U. S. Coast Guard Academy Bears 3 10/10 251 256 +5 84th → 85th 0.0 46% 8.9 / 9.4 261
9 Peter Judge '29 U. S. Coast Guard Academy Bears 1 10/10 235 277 +42 82nd → 88th 1.0 42% 9.2 / 9.9 241
10 Kiana Beachy '28 Tufts University Jumbos 3 10/10 232 242 +10 82nd → 83rd 0.1 50% 10.0 / 10.0 231
11 Devon Owen '27 Tufts University Jumbos 2 10/10 284 216 -68 88th → 80th 3.1 74% 10.1 / 8.3 276
12 James Hacket '30 University of Rhode Island Rams 3 10/10 203 239 +36 77th → 83rd 0.7 46% 10.6 / 10.9 210
13 Stewart Mccaleb '30 Roger Williams University Hawks 3 10/10 206 215 +9 78th → 79th 0.0 54% 11.2 / 10.9 207
14 Katlia Sherman '30 Brown University Bears 3 10/10 174 183 +9 73rd → 75th 0.0 45% 11.7 / 11.9 182
15 Eli Shainker '30 Yale University Bulldogs 2 10/10 193n 185 -8 76th → 75th 0.0 54% 11.9 / 11.1 193
16 Joey Richardson '28 U. S. Coast Guard Academy Bears 2 10/10 237 169 -68 82nd → 72nd 2.7 79% 12.2 / 9.9 230
17 Pierson Falk '28 University of Rhode Island Rams 2 10/10 200 179 -21 77th → 74th 0.3 83% 13.8 / 11.0 198
18 Patrick Wahlig '27 Yale University Bulldogs 3 10/10 126 117 -8 64th → 63rd 0.0 71% 15.1 / 13.2 125