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Frosh-Soph Regatta — 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-W40). 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 2,605 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  21 boats · 8 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 Upamanyu Dutta '20 University of Southern California Trojans 1 8/8 242n* 358 +116 78th → 95th 4.0 14% 2.4 / 6.2 242*
2 Nicolai Sponholtz '19 University of California at Berkeley Bears 1 8/8 209* 290 +80 71st → 86th 2.3 10% 3.5 / 6.6 210*
3 Alex Demmler '19 California State University Long Beach 49ers 1 8/8 170* 273 +103 61st → 83rd 3.8 12% 4.2 / 7.8 157*
4 Vincent Todosiev '19 University of California at Berkeley Bears 4 8/8 133* 220 +87 52nd → 73rd 2.8 10% 4.9 / 8.9 150*
5 Ben Brough '19 Cal Poly University S.L.O. Mustang 1 8/8 205* 222 +17 70th → 73rd 0.1 58% 6.9 / 6.6 227*
6 Jeremy Burke '20 University of California at Santa Cruz 8/8 38n* 178 +141 31st → 63rd 7.0 19% 7.8 / 11.8 38*
7 Christina Gasparich '19 University of Southern California Trojans 2 8/8 181* 191 +10 64th → 67th 0.0 70% 8.5 / 7.4 199*
8 David Hopkins '19 University of California at Berkeley Bears 3 8/8 119n* 170 +51 49th → 61st 1.0 52% 9.5 / 9.5 119*
9 Michael Gabassi '18 University of California at Berkeley Bears 2 8/8 90n* 99 +9 42nd → 44th 0.0 49% 10.0 / 10.3 90*
10 Nicholas Lenz Lenz '19 University of California at Los Angeles Bruins 2 8/8 128* 24 -105 52nd → 28th 4.7 68% 10.2 / 9.0 127*
10 Nick Aline '20 Arizona State University Sun Devils 1 8/8 58* 44 -13 35th → 32nd 0.1 35% 10.2 / 11.4 56*
12 Abraham Dearden '20 University of California at Los Angeles Bruins 1 8/8 132n* 76 -56 52nd → 39th 1.6 66% 10.6 / 9.0 132*
13 Liam Hood '20 Cal Poly University S.L.O. Mustang 2 8/8 240* 115 -125 77th → 48th 9.0 98% 10.9 / 5.7 218*
14 Natalie Hopper '19 University of California at San Diego Tritons 2 8/8 36* 51 +15 30th → 34th 0.1 51% 12.1 / 12.0 48*
15 Jake Zarraonandia '19 University of California at Irvine Anteaters 2 8/8 -52* 31 +83 13th → 29th 2.4 49% 14.8 / 14.7 -34*
16 Annie Thompson '19 California State University Long Beach 49ers 2 7/8 -89* -35 +54 6th → 16th 1.0 44% 15.3 / 15.5 -89*
16 John Lisenby '17 California State University Long Beach 49ers 2 1/8 -35n* 1 +36 16th → 23rd 0.1 52% 15.0 / 13.9 -35*
17 Warren Ko '19 University of California at San Diego Tritons 3 6/8 -64n* -128 -64 11th → 3rd 1.3 71% 17.2 / 14.6 -64*
17 Casey Bowers-Loeffler '19 University of California at San Diego Tritons 3 2/8 -2* 0 +1 23rd → 23rd 0.0 61% 14.5 / 13.2 7*
18 Emily Guy '18 University of California at Irvine Anteaters 1 6/8 -72n* -84 -12 9th → 7th 0.0 65% 16.7 / 14.7 -72*
18 Stacey Agustin '18 University of California at Irvine Anteaters 1 2/8 -53n* -95 -42 13th → 6th 0.2 67% 17.0 / 14.4 -53*
19 Sierra Madden '19 University of California at Davis 8/8 -180* -199 -20 1st → 1st 0.1 65% 18.8 / 17.7 -180*
20 Cameron Khorsandi '17 University of California at San Diego Tritons 1 8/8 -84n* -135 -52 7th → 2nd 1.2 85% 19.0 / 15.2 -84*
21 Daniel Rust '19 Arizona State University Sun Devils 2 8/8 -160n* -224 -65 1st → 1st 1.2 80% 19.6 / 17.0 -160*

Division B  21 boats · 8 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 Elizabeth Hansen '19 University of California at Berkeley Bears 1 8/8 202* 229 +26 69th → 75th 0.2 17% 3.0 / 5.3 207*
2 Mason Comerford '20 University of Southern California Trojans 1 8/8 219n* 168 -51 73rd → 61st 1.0 37% 3.6 / 5.4 219*
3 Whitney Ziesing '19 University of California at Berkeley Bears 3 8/8 97n* 175 +78 44th → 63rd 2.3 20% 4.6 / 8.3 97*
4 Andrew Klotz '20 University of California at Santa Cruz 8/8 111n* 140 +29 47th → 54th 0.4 32% 5.6 / 7.9 111*
5 Chad Farrell '20 California State University Long Beach 49ers 1 8/8 26* 150 +123 28th → 57th 5.5 10% 6.0 / 10.2 23*
6 Lauren Lopez '20 University of Southern California Trojans 2 8/8 112n* 107 -5 48th → 46th 0.0 44% 6.8 / 7.9 112*
7 Katey Hayes '20 University of California at Berkeley Bears 4 8/8 53n* 88 +35 35th → 42nd 0.5 35% 7.5 / 9.5 53*
8 Maddy Kuhn '20 University of California at Los Angeles Bruins 1 8/8 61n* 56 -5 36th → 35th 0.0 48% 8.9 / 9.5 61*
9 Shea Walker '20 Cal Poly University S.L.O. Mustang 2 8/8 77* 102 +26 39th → 45th 0.3 60% 9.2 / 8.7 74*
10 Erika Barth '16 University of California at San Diego Tritons 2 8/8 108n* 48 -60 46th → 33rd 1.6 68% 9.8 / 8.1 108*
11 Hallie Muller '20 University of California at Los Angeles Bruins 2 8/8 12n* 30 +18 25th → 29th 0.1 45% 10.0 / 10.7 12*
12 Kartikye Mittal '20 University of California at Berkeley Bears 2 4/8 32n* 63 +31 29th → 37th 0.2 48% 9.5 / 10.2 32*
12 Matthieu Laurent '17 University of California at Berkeley Bears 2 4/8 -6n* 16 +21 22nd → 26th 0.1 58% 12.0 / 11.2 -6*
13 Sierra Apillanes '19 Arizona State University Sun Devils 1 6/8 -71* -6 +65 9th → 21st 1.3 36% 12.0 / 13.3 -55*
13 Olivia Ayd '20 Arizona State University Sun Devils 1 2/8 -93n* 54 +147 6th → 35th 1.9 20% 9.0 / 13.7 -93*
14 Dallas Butler '17 Cal Poly University S.L.O. Mustang 1 8/8 -65* 2 +67 10th → 23rd 1.6 48% 13.0 / 13.1 -38*
15 Stacey Agustin '18 University of California at Irvine Anteaters 2 8/8 -53n* -76 -23 13th → 9th 0.3 67% 14.6 / 12.5 -53*
16 Chase Verstuyft '17 University of California at San Diego Tritons 3 4/8 -96* -108 -13 6th → 4th 0.0 72% 16.2 / 14.0 -93*
16 Jacqueline Jabuka '20 University of California at San Diego Tritons 3 4/8 -63n* -92 -29 11th → 6th 0.2 71% 15.8 / 13.0 -63*
17 Bradley Patterson '19 Arizona State University Sun Devils 2 8/8 -116n* -143 -27 3rd → 2nd 0.4 76% 17.4 / 14.4 -116*
18 Adityavardhan Vijay Shah '20 University of California at Davis 8/8 -208n* -230 -23 1st → 1st 0.2 60% 18.0 / 16.6 -208*
19 Jason Nideffer '20 University of California at San Diego Tritons 1 8/8 -129n* -193 -64 3rd → 1st 1.9 80% 18.2 / 14.7 -129*
20 Ivan Penaloza '18 University of California at Irvine Anteaters 1 8/8 -146n* -202 -56 2nd → 1st 1.2 84% 18.8 / 15.1 -146*
20 Sebastian Guerra '20 California State University Long Beach 49ers 2 8/8 -162n* -249 -87 1st → 1st 2.3 82% 18.8 / 15.4 -162*

From the notes

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

All notes →