Bears Invite — Analysis

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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 1,767 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 10/10 289 352 +63 89th95th 2.0 16% 5.0 / 7.6 299
2 Leonardo Burnham '29 University of Rhode Island Rams 1 10/10 284 348 +64 89th95th 2.0 15% 5.1 / 7.9 299
3 Reece Schwartz '29 Roger Williams University Hawks 1 9/10 332 378 +45 94th97th 0.9 26% 4.1 / 6.5 341
4 Thomas O'Grady '30 Yale University Bulldogs 1 10/10 257 291 +33 86th90th 0.6 26% 6.7 / 8.7 265
5 Dylan Seawards '30 Roger Williams University Hawks 2 10/10 304 288 -16 91st89th 0.2 54% 7.3 / 7.2 303
6 Crue Ziskind '29 Tufts University Jumbos 1 10/10 303 262 -40 91st86th 1.1 70% 8.3 / 7.2 299
7 Andrew Lamm '30 Brown University Bears 2 10/10 242 246 +4 83rd84th 0.0 41% 8.4 / 9.2 247
8 Liam Marcell '30 U. S. Coast Guard Academy Bears 3 10/10 244 246 +1 84th84th 0.0 47% 8.9 / 9.1 246
9 Peter Judge '29 U. S. Coast Guard Academy Bears 1 10/10 231 265 +34 82nd87th 0.7 45% 9.2 / 9.6 239
10 Kiana Beachy '28 Tufts University Jumbos 3 10/10 233 230 -3 82nd82nd 0.0 58% 10.0 / 9.5 233
11 Devon Owen '27 Tufts University Jumbos 2 10/10 264 202 -62 86th77th 2.5 72% 10.1 / 8.5 256
12 James Hacket '30 University of Rhode Island Rams 3 10/10 195 228 +33 77th81st 0.6 47% 10.6 / 10.8 201
13 Stewart Mccaleb '30 Roger Williams University Hawks 3 10/10 196 205 +8 77th78th 0.0 57% 11.2 / 10.7 199
14 Katlia Sherman '30 Brown University Bears 3 10/10 166 171 +5 71st72nd 0.0 48% 11.7 / 11.6 169
15 Eli Shainker '30 Yale University Bulldogs 2 10/10 154n 174 +20 69th73rd 0.2 47% 11.9 / 11.9 154
16 Joey Richardson '28 U. S. Coast Guard Academy Bears 2 10/10 230 158 -72 82nd71st 3.1 83% 12.2 / 9.7 223
17 Pierson Falk '28 University of Rhode Island Rams 2 10/10 191 167 -24 76th72nd 0.4 85% 13.8 / 11.0 189
18 Patrick Wahlig '27 Yale University Bulldogs 3 10/10 113 103 -11 62nd60th 0.0 71% 15.1 / 13.2 112