Rebecca Blank Memorial Regatta — 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.

• 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	Max Naseef '28	University of Chicago	6/9	126	304	+178	59th → 88th	6.5	1%	2.3 / 6.0	151
1	Tomas Lopez-Valcarcel '29	University of Chicago	3/9	-35	87	+122	27th → 52nd	2.0	30%	7.7 / 8.8	—
2	Andrew Liu '27	Case Western Reserve University	9/9	176	164	-11	69th → 66th	0.1	35%	4.2 / 4.9	—
2	Flinn Opel '29	Washington University	9/9	94	179	+85	53rd → 69th	2.9	7%	4.2 / 6.5	—
4	Payden Pittman '28	University of Notre Dame	9/9	189	223	+33	71st → 77th	0.4	52%	4.4 / 4.5	187
6	Hayden Johansen '27	University of Minnesota	9/9	110	133	+23	56th → 61st	0.2	26%	5.2 / 6.2	110
7	Emma Hershey '27	Ohio State University	9/9	67	158	+91	48th → 65th	3.1	26%	6.1 / 7.1	67
12	Victoria Kutkovska '27	Lake Forest College	6/9	-81n	-74	+7	15th → 17th	0.0	80%	11.2 / 9.4	-81
12	Noelle Erslovas '28	Lake Forest College	3/9	-59n	—	—	21st → —	1.5	>99%	12.3 / 9.1	-59

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	Alyssa Neumann '30	University of Notre Dame	9/9	116n	151	+35	58th → 64th	0.5	28%	2.9 / 4.4	116
3	Charlie Hewitt '30	Ohio State University	9/9	139	109	-30	62nd → 56th	0.4	44%	3.4 / 3.9	—
4	Amelia Pare '28	Washington University	9/9	13	68	+55	37th → 48th	1.3	20%	4.7 / 6.2	—
5	Joshua Van Norman '29	University of Minnesota	9/9	31	50	+19	41st → 45th	0.2	39%	5.2 / 5.8	41
6	Arahant Veerareddy '30	Case Western Reserve University	9/9	-84	43	+127	15th → 43rd	6.2	6%	5.3 / 8.0	—
9	Sam Dunham '30	University of Chicago	5/9	-42	-4	+38	25th → 34th	0.4	55%	7.4 / 7.2	-42
9	Aaron Mortara '29	University of Chicago	4/9	-26n	-44	-18	29th → 25th	0.1	87%	9.8 / 6.9	-26
10	Alex Berrie '29	Lake Forest College	7/9	-111n	-67	+44	10th → 19th	0.7	61%	9.1 / 8.3	-111
10	Amelia Long '30	Lake Forest College	2/9	-108n	-77	+31	10th → 16th	0.1	70%	10.0 / 8.2	-108