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Harvard Invitational — 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 (2016-W39). 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 2,600 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  12 boats · 6 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 Julien Guiot '18 Tufts University 6/6 319* 452 +133 91st → 99th 3.1 4% 1.7 / 3.7 320*
2 Rachel Foster '20 Brown University 6/6 239* 261 +21 77th → 82nd 0.1 30% 4.0 / 5.2 240*
3 Kyle Riggs '21 Northeastern University 6/6 216* 259 +42 73rd → 81st 0.6 38% 4.8 / 5.6 217*
4 Joseph Chamberlin '19 Massachusetts Institute of Technology Engineers 1 6/6 168* 239 +71 61st → 77th 1.5 25% 5.2 / 6.6 172*
5 Matthew Schryver '20 University of Rhode Island 6/6 201n* 200 -1 70th → 69th 0.0 46% 5.5 / 6.0 201*
5 Samuel Wright '18 Boston University 6/6 114* 214 +100 48th → 72nd 2.7 13% 5.5 / 7.7 114*
7 Fiona Mckellar '20 Massachusetts Institute of Technology Engineers 2 6/6 172* 227 +55 62nd → 75th 0.9 38% 5.8 / 6.5 174*
7 Jessica Williams '20 Harvard University 6/6 246* 198 -48 79th → 69th 0.9 74% 5.8 / 5.0 245*
9 Ethan Merrill '20 Worcester Polytechnic Institute 6/6 169* 144 -25 61st → 56th 0.2 87% 8.3 / 6.6 164*
9 Malich Altman '19 McGill University 6/6 85* 152 +67 42nd → 57th 1.4 51% 8.3 / 8.2 80*
11 Morgan Lindback '17 University of New England 6/6 -69* 81 +150 10th → 41st 5.8 60% 11.0 / 10.5 -68*

Division B  12 boats · 6 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 Zachary Bresnick '21 Northeastern University 6/6 205* 329 +124 70th → 92nd 3.4 8% 2.5 / 4.9 206*
2 John Reyes '19 Massachusetts Institute of Technology Engineers 1 6/6 180* 261 +81 64th → 82nd 1.7 15% 3.5 / 5.5 180*
3 Nicholas Giacobbe '19 Tufts University 6/6 193* 209 +17 67th → 72nd 0.1 31% 4.3 / 5.2 193*
4 Sofia Marie Mascia '20 Harvard University 6/6 210* 208 -2 72nd → 71st 0.0 56% 5.0 / 4.9 211*
5 Andrew Stoycos '18 Boston University 6/6 190* 207 +17 67th → 71st 0.1 52% 5.2 / 5.3 190*
6 Henry Sharpe '17 Brown University 6/6 206* 153 -54 71st → 58th 1.1 75% 6.0 / 4.9 207*
7 Collin Benzoni '20 University of Rhode Island 6/6 138* 164 +27 54th → 60th 0.2 50% 6.2 / 6.3 138*
8 William Gleckner '17 McGill University 6/6 45* 147 +101 33rd → 56th 2.9 34% 7.3 / 8.1 47*
9 Alexander Peraire-Bueno '20 Massachusetts Institute of Technology Engineers 2 6/6 17* 130 +113 27th → 52nd 3.5 37% 8.0 / 8.6 28*
10 Samuel Bissonnette '18 University of New England 6/6 -88* 86 +175 7th → 42nd 7.4 20% 9.2 / 10.3 -87*
11 Adam Camilli '19 Worcester Polytechnic Institute 6/6 39* 50 +11 31st → 34th 0.0 75% 9.5 / 8.3 32*

From the notes

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

All notes →