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The Grant — Analysis

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* This regatta is not official yet. Ratings marked * are from the last weekly update and have not absorbed this event; the performance columns use the results posted so far, and everything refreshes as scoring is finalised.

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 (2022-W15). 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 2,292 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.

Division A  14 boats · 14 races

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 Connor Mraz '23 Princeton University 14/14 298* 329 +31 84th → 89th 0.6 18% 3.4 / 4.5 302*
2 Leopold Brandl '24 Columbia University 14/14 264* 301 +37 79th → 85th 0.9 19% 3.8 / 5.3 266*
2 Stewart Gurnell '23 Washington College 14/14 312* 298 -15 86th → 84th 0.1 40% 3.8 / 4.2 315*
4 Valerio Palamara '24 Hampton University 14/14 255* 324 +69 77th → 88th 2.8 26% 4.5 / 5.5 258*
5 Daniel Hodges '22 Christopher Newport University 14/14 242* 292 +51 74th → 83rd 1.4 48% 5.6 / 5.7 242*
6 Constantyn Van Der Voort '23 William and Mary 14/14 158* 212 +54 56th → 68th 2.1 31% 6.7 / 7.6 159*
7 Marco Centanaro Garcia '23 University of Virginia 14/14 166* 145 -21 58th → 54th 0.3 55% 7.7 / 7.5 166*
8 Owen Ward '22 Villanova University 14/14 227* 168 -59 72nd → 58th 2.7 89% 7.8 / 6.1 220*
9 Christopher Magno '22 Virginia Tech 8/14 172* 95 -77 59th → 42nd 2.6 79% 8.6 / 7.3 170*
9 Aidan Young '24 Virginia Tech 6/14 168* 173 +5 58th → 60th 0.0 43% 7.0 / 7.5 168*
10 Stanley Galloway '22 University of Pittsburgh 14/14 117* 127 +10 47th → 50th 0.1 35% 8.1 / 8.7 117*
11 Kristin Hess '22 SUNY Stony Brook 14/14 88* 98 +10 41st → 43rd 0.1 44% 9.1 / 9.3 90*
12 Alec Wyers '23 Syracuse University 14/14 71* 63 -8 37th → 35th 0.0 72% 10.5 / 9.6 70*
13 Elliot Tindall '24 Rochester Institute of Technology 14/14 -15* -81 -66 19th → 8th 1.6 97% 13.3 / 11.2 0*
14 Stephen Blair '24 American University 14/14 -127n* -265 -138 3rd → 1st 2.1 78% 13.6 / 12.5 -127*

Division B  14 boats · 14 races

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 Luke Hayes '22 Christopher Newport University 14/14 206* 248 +42 67th → 76th 1.2 15% 3.6 / 5.0 204*
2 Alex Heim '24 Washington College 14/14 177* 203 +26 61st → 67th 0.4 15% 4.2 / 5.7 183*
3 Asher Green '24 Princeton University 14/14 215* 220 +5 69th → 70th 0.0 40% 4.4 / 4.9 216*
4 Reid Shanabrook '22 Virginia Tech 14/14 222* 187 -35 70th → 63rd 0.9 50% 4.6 / 4.7 221*
5 Stefano Palamara '25 Hampton University 14/14 173* 223 +50 60th → 70th 1.4 30% 4.8 / 5.9 177*
6 Ryan Magill '22 SUNY Stony Brook 14/14 79* 176 +97 39th → 60th 5.6 21% 6.5 / 8.0 80*
7 Nathan Whisner '23 William and Mary 14/14 194* 191 -3 65th → 64th 0.0 84% 6.6 / 5.4 199*
8 Jack Murray '22 Villanova University 14/14 83* 66 -17 40th → 36th 0.2 50% 8.0 / 8.0 83*
9 Shay Gualdoni '25 Syracuse University 14/14 111* 62 -49 45th → 35th 1.9 79% 8.4 / 7.3 105*
10 Connor Lothrop '24 University of Virginia 14/14 59* 29 -30 34th → 28th 0.5 55% 8.7 / 8.5 59*
11 Karl Wagerson '24 American University 8/14 101* 97 -4 43rd → 43rd 0.0 55% 7.9 / 7.7 101*
11 Lydia Lopiccolo '25 American University 6/14 -99n* -238 -139 6th → 1st 1.9 79% 13.0 / 11.1 -99*
12 Benjamin Knisely '24 Rochester Institute of Technology 14/14 -97* -12 +85 6th → 20th 3.6 29% 10.9 / 11.6 -20*
13 Ava Rotondo '24 Columbia University 10/14 -4* -57 -53 21st → 12th 1.3 78% 11.2 / 9.8 -14*
13 Jasmine Chen '25 Columbia University 4/14 -29n* -88 -59 17th → 7th 0.6 70% 11.8 / 10.1 -29*
14 Jason "Strongbones" Case '25 University of Pittsburgh 14/14 -74* -126 -51 9th → 3rd 0.5 >99% 14.1 / 11.1 -77*

From the notes

Short write-ups of what the ratings and results show, with the numbers and how they were computed.

All notes →