Nevins Trophy — Analysis

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-W42). 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 2,065 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.

• — in the performance column means the skipper finished last in every race they sailed, so no finite least-surprisal rating exists.

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	Thomas Whidden '28	Stanford University	12/12	463	469	+6	99th → 99th	0.0	33%	4.3 / 5.3	465
2	Mathias Reimer '27	Yale University	12/12	358	384	+26	93rd → 96th	0.4	10%	6.0 / 8.7	358
3	Chris Small '30	University of Pennsylvania	12/12	394	396	+2	97th → 97th	0.0	40%	6.8 / 7.4	394
4	Owen Fretwell '30	St. Mary's College of Maryland	12/12	375	382	+8	95th → 96th	0.0	39%	7.4 / 8.1	375
5	Jj Klempen '27	Hobart and William Smith Colleges	12/12	366	357	-9	94th → 93rd	0.1	48%	8.2 / 8.3	366
6	Jacob Zils '27	Fordham University	12/12	353	320	-33	93rd → 90th	0.6	47%	8.5 / 8.8	352
7	Steven Hardee '26	University of Miami	12/12	413	394	-19	97th → 97th	0.2	87%	8.8 / 6.8	412
8	Sean Trudell '28	U. S. Naval Academy	12/12	354	320	-34	93rd → 90th	0.8	57%	9.1 / 8.8	353
9	Crue Ziskind '29	Tufts University	12/12	362	301	-61	94th → 88th	2.8	76%	9.8 / 8.5	361
10	Kevin Gosselin '27	North Carolina State University	12/12	323	347	+24	90th → 92nd	0.3	54%	10.1 / 9.9	324
10	Liam Marcell '30	U. S. Coast Guard Academy	6/12	321	348	+27	90th → 93rd	0.3	35%	8.8 / 10.0	322
10	Tomas Riccio '28	U. S. Coast Guard Academy	6/12	291	316	+25	86th → 89th	0.2	56%	11.3 / 11.0	291
12	Pj Rodrigues '28	Eckerd College	12/12	352	354	+2	93rd → 93rd	0.0	84%	10.8 / 8.8	353
13	Pierce Brindley '28	Old Dominion University	12/12	295	286	-9	87th → 85th	0.1	55%	11.0 / 10.8	295
14	Gavin Galway '30	Webb Institute	12/12	220	273	+53	76th → 84th	1.5	24%	11.4 / 13.1	221
15	Reed Mcallister '27	U. S. Merchant Marine Academy	12/12	297	307	+9	87th → 89th	0.1	65%	11.5 / 10.8	297
16	Rhett Krawitt '30	George Washington University	12/12	256	279	+23	81st → 84th	0.3	41%	11.7 / 12.1	256
17	Peter Stewart '28	Boston University	10/12	281	285	+4	85th → 85th	0.0	68%	12.2 / 11.2	281
17	Aaron Toy-Lim '30	Boston University	2/12	259	260	+2	82nd → 82nd	0.0	74%	15.0 / 12.0	259
18	Nathaniel Hartwell '28	SUNY Maritime College	12/12	223	244	+21	77th → 80th	0.2	58%	13.5 / 13.1	224

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	Dylan Sih '28	Stanford University	12/12	428	432	+4	98th → 98th	0.0	29%	3.9 / 4.9	430
2	Devon Owen '27	Tufts University	12/12	344	422	+78	92nd → 98th	3.2	23%	5.9 / 7.4	345
2	Leo Robillard '29	U. S. Naval Academy	12/12	356	374	+18	93rd → 95th	0.2	30%	5.9 / 7.0	358
4	James Kopack '28	Hobart and William Smith Colleges	12/12	331	338	+7	91st → 92nd	0.0	40%	7.2 / 7.8	332
5	Drew D’Orsi '28	University of Pennsylvania	12/12	323	367	+44	90th → 94th	1.2	40%	7.5 / 8.1	323
6	Joseph Marynowski '28	St. Mary's College of Maryland	6/12	365	373	+8	94th → 95th	0.0	33%	5.3 / 6.7	365
6	Landon Cormie '27	St. Mary's College of Maryland	6/12	380	221	-158	96th → 76th	9.9	95%	10.2 / 6.3	378
7	David Webb '28	University of Miami	12/12	297	337	+41	87th → 92nd	1.1	32%	7.8 / 8.9	297
8	Erickson Rankin '27	Fordham University	12/12	274	242	-32	84th → 80th	0.6	37%	8.9 / 9.7	274
9	Beckett Shinn '29	Boston University	12/12	307	283	-24	89th → 85th	0.4	62%	9.2 / 8.6	306
10	Peter Judge '29	U. S. Coast Guard Academy	12/12	288	320	+31	86th → 90th	0.6	54%	9.4 / 9.3	292
11	Benjamin Usher '27	North Carolina State University	12/12	301	289	-12	88th → 86th	0.1	79%	10.2 / 8.8	301
12	Gianna Dewey '27	Old Dominion University	12/12	246	259	+13	80th → 82nd	0.1	45%	10.3 / 10.6	247
12	Jordan Vieira '28	Eckerd College	12/12	305	238	-67	88th → 79th	3.3	80%	10.3 / 8.7	305
14	Langdon Wallace '27	U. S. Merchant Marine Academy	12/12	214	254	+40	75th → 81st	0.9	27%	10.5 / 11.7	214
15	Eli Shainker '30	Yale University	12/12	233	193	-40	78th → 72nd	1.1	80%	13.2 / 11.0	233
16	Luke Barker '28	SUNY Maritime College	12/12	166	185	+19	66th → 70th	0.2	59%	13.6 / 13.1	166
17	Marc Leyk '28	Webb Institute	10/12	133	210	+77	61st → 74th	2.7	63%	14.7 / 13.9	129
17	Jackson Fisher '30	Webb Institute	2/12	89	—	—	52nd → —	2.2	73%	17.0 / 15.0	89
18	Austin Budd '30	George Washington University	12/12	102	126	+24	55th → 59th	0.3	77%	16.1 / 14.6	102