Harry H. Anderson Jr. 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-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.
• — 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	Blake Behrens '27	Brown University	9/9	485	546	+61	99th → 99th	1.4	10%	2.6 / 4.2	494
2	Dorothy Mendelblatt '28	Yale University	9/9	409	420	+11	99th → 99th	0.1	32%	5.3 / 6.3	411
3	Harrison Strom '28	Harvard University	9/9	377	393	+17	97th → 98th	0.1	27%	6.0 / 7.3	379
4	Alex Lech '28	Boston College	9/9	385	385	-0	98th → 97th	0.0	41%	6.3 / 7.0	385
5	Robert Ulmer '27	Massachusetts Institute of Technology	9/9	361	354	-6	96th → 95th	0.0	37%	7.0 / 7.7	361
6	Cooper Smith '27	University of Vermont	9/9	276	365	+89	88th → 96th	3.5	10%	7.9 / 10.5	292
6	Keith Grupenhoff '29	University of Rhode Island	9/9	352	327	-25	95th → 93rd	0.3	49%	7.9 / 8.1	351
8	Benjamin Reeser '28	U. S. Coast Guard Academy	9/9	318	325	+7	93rd → 93rd	0.0	34%	8.1 / 9.0	320
9	Connor Mchugh '28	Roger Williams University	9/9	313	323	+10	92nd → 93rd	0.0	34%	8.3 / 9.3	315
10	Olivia Drulard '27	Dartmouth College	9/9	334	311	-23	94th → 92nd	0.3	65%	9.2 / 8.5	333
11	Michelangelo Vecchio '27	Bowdoin College	9/9	336	299	-38	94th → 91st	0.7	68%	9.4 / 8.5	335
13	Wyatt Bischoff '27	Tufts University	9/9	296	308	+12	90th → 92nd	0.1	63%	10.4 / 9.8	297
14	Max Teo '28	Boston University	9/9	315	250	-64	92nd → 85th	2.3	89%	11.8 / 9.3	312
15	Charles Wilkinson '28	Northeastern University	6/9	245	262	+17	84th → 86th	0.1	57%	11.7 / 11.3	247
15	Jeremy Bullock '29	Northeastern University	3/9	250	255	+6	84th → 85th	0.0	79%	13.7 / 11.2	250
16	James Zanenga '29	Babson College	9/9	177	205	+28	74th → 78th	0.3	70%	14.3 / 12.9	182
17	Eben Dooling '29	McGill University	9/9	118	33	-85	62nd → 46th	1.3	89%	16.3 / 14.2	116

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	Bo Angus '29	Roger Williams University	8/8	373	442	+69	97th → 99th	1.8	11%	4.0 / 6.1	386
2	Tanner Krygsveld '29	Boston College	8/8	388	406	+18	98th → 99th	0.1	33%	4.8 / 5.7	390
3	Jacob Posner '29	Harvard University	8/8	374	397	+23	97th → 98th	0.2	27%	4.9 / 6.1	383
4	Audrey Foley '28	Yale University	8/8	331	349	+18	94th → 95th	0.1	33%	6.4 / 7.3	335
6	Richard Pokorny '29	U. S. Coast Guard Academy	8/8	293	315	+22	90th → 92nd	0.2	21%	6.9 / 8.6	297
7	Tyler Lamm '28	Brown University	8/8	398	319	-79	98th → 93rd	3.5	89%	7.5 / 5.4	382
7	William Kulas '28	Massachusetts Institute of Technology	8/8	322	343	+21	93rd → 95th	0.2	50%	7.5 / 7.6	326
9	Turner Ryon '29	Dartmouth College	8/8	283	294	+11	89th → 90th	0.1	51%	8.8 / 8.8	285
10	Ryan Keenan '29	Bowdoin College	8/8	281	284	+3	89th → 89th	0.0	67%	9.6 / 8.8	282
11	Peter Stewart '28	Boston University	8/8	225	217	-8	81st → 80th	0.0	43%	10.1 / 10.5	225
12	Christopher Chwalk '28	University of Rhode Island	4/8	274	190	-83	88th → 76th	1.4	72%	10.5 / 9.0	265
12	Owen Grainger '28	University of Rhode Island	4/8	302	264	-38	91st → 86th	0.4	76%	10.0 / 8.3	301
13	Gus Macaulay '27	Tufts University	8/8	278	251	-27	88th → 85th	0.4	78%	10.4 / 8.9	277
14	Gavin Hudson '28	Northeastern University	6/8	273	233	-40	88th → 82nd	0.6	86%	11.3 / 9.1	272
14	Carter Anderson '27	Northeastern University	2/8	207	295	+88	78th → 90th	0.8	35%	9.5 / 10.9	211
15	Baruch Saffer '29	McGill University	8/8	107n	135	+28	61st → 66th	0.2	42%	12.8 / 12.9	107
16	Charlie Walton '29	University of Vermont	4/8	83	99	+16	56th → 59th	0.0	77%	15.2 / 13.8	84
16	William Denker '27	University of Vermont	4/8	140	121	-19	67th → 63rd	0.1	80%	14.8 / 12.8	139
17	Ethan Ibgui '29	Babson College	8/8	-109n	—	—	19th → —	1.6	>99%	17.1 / 15.9	-109