KP Dinghy Open — 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 2,062 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.
• ^u not rated by the model — the division median 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	Michael Burns '29	Fordham University Rams 1 A	1/1	211	700+	+489	75th → 99th	2.4	15%	1.0 / 5.0	211
2	Nicole Ostapowicz '28	SUNY Maritime College Privateers A	1/1	122	358	+237	60th → 94th	1.7	14%	2.0 / 7.2	122
3	Alexander Sau Hang Ching '28	Columbia University Lions A	1/1	97	284	+187	54th → 86th	1.3	18%	3.0 / 8.1	97
4	Mack Robertson '30	Fordham University Rams 2 B	1/1	104	236	+132	56th → 80th	0.7	27%	4.0 / 7.8	105
5	Alden Hughes '29	U. S. Merchant Marine Academy Mariners B	1/1	10	207	+197	37th → 74th	1.5	17%	5.0 / 10.3	10
6	Jonathan Wittick '29	Fordham University Rams 2 A	1/1	132	170	+39	61st → 68th	0.1	49%	6.0 / 7.0	132
7	Charlie Parsons '30	Fordham University Rams 1 B	1/1	200	129	-70	73rd → 61st	0.4	74%	7.0 / 5.1	199
8	Nikita Troast '29	SUNY Maritime College Privateers B	1/1	116	118	+2	58th → 59th	0.0	60%	8.0 / 7.4	116
9	Nicholas Lorenzen '27	Princeton University Tigers B	1/1	102	91	-10	56th → 53rd	0.0	61%	9.0 / 7.9	101
10	Aidan Woods '29	Webb Institute Clippers A	1/1	90	63	-27	53rd → 47th	0.0	66%	10.0 / 8.2	90
11	Matthew Mccarvill '28	U. S. Merchant Marine Academy Mariners A	1/1	18	42	+24	39th → 44th	0.0	55%	11.0 / 10.2	18
12	Advik Eswaran '27	Princeton University Tigers A	1/1	113	-23	-135	58th → 30th	1.5	89%	12.0 / 7.4	112
13	Julia Marich '28	Monmouth University Hawks A	1/1	-109	-25	+84	11th → 30th	0.3	37%	13.0 / 13.2	-109
14	Andrew Sparks '27	Columbia University Lions B	1/1	18n	-139	-157	39th → 5th	1.6	78%	14.0 / 9.9	18
15	John Bailey '28	Monmouth University Hawks B	1/1	-126	—	—	7th → —	0.9	67%	15.0 / 13.4	-126
16	Anastasios Korfiatis '29	Webb Institute Clippers B	1/1	102u	—	—	56th → —	0.0	>99%	17.0 / 8.0	—