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 1,907 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.
• — 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	474	554	+80	99th → 99th	2.3	7%	2.6 / 4.7	473
2	Dorothy Mendelblatt '28	Yale University	9/9	413	430	+17	99th → 99th	0.1	29%	5.2 / 6.4	416
3	Harrison Strom '28	Harvard University	9/9	391	394	+3	98th → 98th	0.0	32%	6.0 / 7.1	391
4	Alex Lech '28	Boston College	9/9	383	395	+12	97th → 98th	0.1	34%	6.2 / 7.2	383
5	Robert Ulmer '27	Massachusetts Institute of Technology	9/9	370	363	-8	96th → 96th	0.0	40%	7.0 / 7.7	374
6	Keith Grupenhoff '29	University of Rhode Island	9/9	334	337	+3	94th → 94th	0.0	33%	7.9 / 8.8	331
7	Benjamin Reeser '28	U. S. Coast Guard Academy	9/9	336	335	-0	94th → 94th	0.0	39%	8.0 / 8.7	347
8	Connor Mchugh '28	Roger Williams University	9/9	310	333	+23	92nd → 94th	0.2	27%	8.2 / 9.6	314
9	Olivia Drulard '27	Dartmouth College	9/9	356	319	-37	96th → 93rd	0.7	71%	9.1 / 8.1	358
10	Cooper Smith '27	University of Vermont	9/9	289	362	+73	89th → 96th	2.2	32%	9.3 / 10.3	306
10	Michelangelo Vecchio '27	Bowdoin College	9/9	343	308	-36	95th → 92nd	0.6	68%	9.3 / 8.5	345
12	Henry Scholz '27	Connecticut College	7/9	369	353	-17	96th → 95th	0.1	67%	8.4 / 7.7	381
12	Rory Murray '29	Connecticut College	2/9	278	256	-22	88th → 85th	0.1	78%	13.5 / 10.5	285
13	Wyatt Bischoff '27	Tufts University	9/9	304	316	+12	91st → 92nd	0.1	62%	10.3 / 9.8	310
14	Max Teo '28	Boston University	9/9	319	256	-63	93rd → 85th	2.2	88%	11.7 / 9.3	316
15	Charles Wilkinson '28	Northeastern University	6/9	242	271	+30	83rd → 87th	0.3	47%	11.5 / 11.6	250
15	Jeremy Bullock '29	Northeastern University	3/9	247	266	+19	84th → 87th	0.1	76%	13.7 / 11.6	245
16	James Zanenga '29	Babson College	9/9	195	213	+18	76th → 79th	0.1	70%	14.2 / 12.8	199
17	Eben Dooling '29	McGill University	9/9	128	40	-88	64th → 48th	1.4	87%	16.2 / 14.2	125

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	396	452	+55	98th → 99th	1.2	16%	4.0 / 5.8	407
2	Tanner Krygsveld '29	Boston College	8/8	384	419	+35	97th → 99th	0.5	24%	4.8 / 6.1	386
3	Jacob Posner '29	Harvard University	8/8	378	407	+29	97th → 99th	0.4	26%	4.9 / 6.3	386
4	Audrey Foley '28	Yale University	8/8	353	359	+6	95th → 96th	0.0	39%	6.4 / 7.0	354
4	William Hurd '27	Connecticut College	8/8	331	368	+37	94th → 96th	0.6	27%	6.4 / 7.7	336
6	Richard Pokorny '29	U. S. Coast Guard Academy	8/8	307	327	+20	92nd → 93rd	0.2	23%	6.9 / 8.5	323
7	Tyler Lamm '28	Brown University	8/8	398	329	-69	98th → 93rd	2.6	86%	7.5 / 5.7	378
7	William Kulas '28	Massachusetts Institute of Technology	8/8	324	353	+29	93rd → 95th	0.4	45%	7.5 / 7.9	324
9	Turner Ryon '29	Dartmouth College	8/8	299	304	+5	91st → 91st	0.0	53%	8.8 / 8.6	299
10	Ryan Keenan '29	Bowdoin College	8/8	303	294	-9	91st → 90th	0.0	71%	9.6 / 8.5	303
11	Peter Stewart '28	Boston University	8/8	226	226	+0	81st → 81st	0.0	36%	10.1 / 10.8	237
12	Christopher Chwalk '28	University of Rhode Island	4/8	279	198	-81	88th → 76th	1.3	70%	10.5 / 9.2	267
12	Owen Grainger '28	University of Rhode Island	4/8	307	272	-35	92nd → 87th	0.3	74%	10.0 / 8.4	307
13	Gus Macaulay '27	Tufts University	8/8	280	261	-18	88th → 86th	0.2	73%	10.4 / 9.2	278
14	Gavin Hudson '28	Northeastern University	6/8	290	240	-50	89th → 83rd	0.9	85%	11.3 / 9.0	291
14	Carter Anderson '27	Northeastern University	2/8	225	310	+85	81st → 92nd	0.7	36%	9.5 / 10.8	226
15	Baruch Saffer '29	McGill University	8/8	110	139	+28	62nd → 66th	0.2	32%	12.8 / 13.5	120
16	Charlie Walton '29	University of Vermont	4/8	75	92	+17	55th → 58th	0.0	67%	15.2 / 14.2	76
16	William Denker '27	University of Vermont	4/8	138	123	-15	66th → 64th	0.0	75%	14.8 / 13.2	153
17	Ethan Ibgui '29	Babson College	8/8	-33n	—	—	34th → —	0.6	>99%	17.9 / 15.4	-33