Luce 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-W43). 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 2,027 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	James Lilyquist '28	Virginia Tech	6/6	249	313	+64	81st → 89th	1.1	16%	3.5 / 5.3	250
2	Logan Mraz '29	Princeton University	6/6	306	281	-26	89th → 85th	0.2	47%	3.7 / 4.1	306
3	Andrew Chisholm '29	Queen's University	6/6	266	292	+25	84th → 87th	0.2	37%	4.0 / 5.1	269
4	Adam Starck '29	SUNY Maritime College	6/6	285	268	-17	86th → 84th	0.1	53%	4.3 / 4.5	285
4	Joseph Murphy '28	Penn State University	6/6	204	260	+56	73rd → 82nd	0.9	15%	4.3 / 6.5	205
6	Alexander Salzberg '29	Washington College	6/6	165	261	+96	66th → 82nd	2.0	41%	6.8 / 7.5	166
7	Andrew Martin '27	Rutgers University	6/6	65	160	+95	48th → 66th	2.5	12%	7.7 / 10.3	—
8	Tristan Feves '27	Stevens Institute of Technology	6/6	22	137	+114	39th → 62nd	3.6	13%	8.8 / 11.4	—
9	Nehuel Armenanzas '30	Rochester Institute of Technology	6/6	168	100	-68	67th → 55th	1.9	89%	10.0 / 7.4	166
9	Sophie Grigg '28	University of Maryland	6/6	110	104	-6	56th → 55th	0.0	67%	10.0 / 9.0	110
11	Julia Gordon '27	Villanova University	6/6	23	79	+57	39th → 50th	0.9	32%	10.3 / 11.4	24
12	Eric Johnson '27	William and Mary	6/6	109	-10	-119	56th → 33rd	4.9	84%	11.3 / 9.1	—
13	Reid Chapman '27	Hamilton College	6/6	-55	47	+102	22nd → 44th	2.9	25%	11.7 / 13.2	—
15	Yichen Cai '30	SUNY Stony Brook	5/6	76	-1	-77	49th → 35th	1.0	80%	12.4 / 10.1	75
15	Sergei Ivanov '27	SUNY Stony Brook	1/6	-129	—	—	6th → —	1.6	>99%	17.0 / 14.2	-130
16	Gus Hankinson '27	U. S. Military Academy	5/6	-93	-237	-144	13th → 1st	2.1	82%	15.8 / 14.1	-93
16	Nathan Gaddis '29	U. S. Military Academy	1/6	-131	-67	+63	6th → 19th	0.2	50%	15.0 / 14.4	-124
17	Jackson Manley '27	Hampton University	6/6	-88	-90	-2	14th → 14th	0.0	98%	16.5 / 13.9	-88

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	Nathaniel Hartwell '28	SUNY Maritime College	6/6	212	358	+146	75th → 94th	4.2	3%	2.0 / 4.9	213
2	Claire Sullivan '29	Penn State University	6/6	196	233	+37	72nd → 79th	0.4	29%	4.0 / 5.4	196
3	Joby Overton '31	Rochester Institute of Technology	6/6	173	199	+25	68th → 73rd	0.2	34%	4.8 / 6.0	174
4	Imogene Nuss '28	Washington College	6/6	85	160	+75	51st → 66th	1.7	22%	6.5 / 8.5	85
5	Jude Martel '29	Queen's University	6/6	101	127	+26	55th → 60th	0.2	37%	6.8 / 8.0	102
6	Zachary Bender '28	Virginia Tech	6/6	199	122	-77	73rd → 59th	2.5	81%	7.0 / 5.3	196
7	Cameron Giordano '30	Stevens Institute of Technology	6/6	-4	150	+154	34th → 64th	6.1	9%	7.2 / 11.1	—
8	Sebastien Nordenson '27	Princeton University	6/6	91	104	+13	52nd → 55th	0.1	38%	7.3 / 8.3	91
9	John O'Leary '30	Villanova University	6/6	66	26	-40	48th → 40th	0.5	45%	8.5 / 9.0	66
11	Khai Hern Low '28	SUNY Stony Brook	3/6	-47	122	+170	24th → 59th	3.5	13%	8.0 / 12.1	-47
11	Sarah Rupa '30	SUNY Stony Brook	3/6	4	27	+23	36th → 40th	0.1	57%	11.7 / 10.9	4
12	Eli Leopold '28	University of Maryland	6/6	101	47	-55	55th → 44th	1.2	84%	10.3 / 8.0	101
13	Cooper Bennett '27	Rutgers University	6/6	23	9	-14	39th → 37th	0.1	55%	10.7 / 10.3	—
14	Thomas Brearley '27	U. S. Military Academy	6/6	-54	-24	+30	22nd → 30th	0.3	67%	13.7 / 12.4	-54
15	Isaac Faulkner '31	Hampton University	6/6	-49	-32	+17	23rd → 28th	0.1	83%	14.7 / 12.2	-49
16	Teddy Jiganti '29	Hamilton College	6/6	34	-108	-142	42nd → 10th	7.8	98%	15.0 / 9.9	—
17	Tyler Boissevain '28	William and Mary	6/6	-90	-187	-97	14th → 1st	1.7	91%	16.0 / 13.3	—