Tech Dinghy Invitational Regatta

SchoolSailor R1 R2 R3 R4 R5 R6 R7 R8 R9 TOT
Roger Williams University Roger Williams University William Bailey '24 2 2 2 3 1 1 2 1 2 16
Boston University Boston University John Cabell '26 7 1 3 2 3 2 1 2 6 27
Massachusetts Institute of Technology Massachusetts Institute of Technology Alexander Horne '26 3 3 6 4 2 6 5 3 8 40
Roger Williams University Roger Williams University Ethan Burt '26 5 5 4 5 4 12 3 5 3 46
Roger Williams University Roger Williams University Claire Siegel-Wilson '24 1 6 5 6 6 4 11 4 5 48
Amherst College Amherst College Nat Edmonds '24 6 4 1 1 5 9 6 7 10 49
Boston University Boston University Renato Korzinek '25 4 7 9 8 10 11 7 6 9 71
Boston University Boston University Richard Kalich '26 11 8 10 7 8 8 9 9 1 71
Amherst College Amherst College James Knowlton '25 9 12 11 10 12 3 4 11 4 76
Boston University Boston University Josh Sultanik '26 8 10 7 12 9 7 8 12 11 84
Roger Williams University Roger Williams University Ethan Snyder '25 10 11 13 11 7 10 12 8 7 89
Massachusetts Institute of Technology Massachusetts Institute of Technology Brooke Barry '26 13 9 8 9 11 5 10 10 14 89
Massachusetts Institute of Technology Massachusetts Institute of Technology Yitian Zhu '25 12 13 12 13 13 13 13 13 12 114
University of New Hampshire University of New Hampshire Matthew Adams '24 15 15 14 14 14 14 14 14 13 127
University of New Hampshire University of New Hampshire Devon Valenta '26 14 16 15 DNS DNS DNS 15 15 15 147
University of New Hampshire University of New Hampshire Cameron Mclean '26 16 14 16 DNS DNS DNS 16 16 16 151
Amherst College Amherst College Colin Sueyoshi '26 17 17 DNS 15 DNF DNS DNS DNS DNS 163
Worcester Polytechnic Institute Worcester Polytechnic Institute Worcester Polytech DNS DNS DNS DNS DNS DNS DNS DNS DNS 171
Boat Speed Analyzer

Division A

Team1A2A3A4A5A6A7A8A9A
Worcester Polytechnic InstituteWorcester Polytechnic Institute Worcester Polytech181818181818181818

Performance rating asks, for each skipper: holding every other boat fixed, which rating makes my actual race-by-race finishes least surprising? Boats are valued with the singlehanded rating rather than the fleet rating: 0.79 × the skipper's fleet rating from the week before the event (unrated skippers start at 1.11); the skipper's own singlehanded offset. Strength differences are scaled by 0.80 before they become probabilities, the value that calibrates the model on seasons it had not seen. Non-finishes (OCS, DNF and similar) are left out of the fitting, as in the rating model.

Percentile places a rating among the 309 ICSA sailors with a singlehanded rating who raced the discipline in the last three seasons. Surprise is how many bits more surprising the finishes were at the pre-event rating than at the performance rating. P(this good or better) simulates the event 3,000 times at the pre-event ratings, with the same regatta-form model as the fleet analysis pages, and reports how often the skipper would score this many points or fewer in the races they sailed.

The singlehanded offsets are the current ones (the discipline files keep no weekly history), so a skipper's own offset has already seen this event; offsets are shrunk hard, so the effect is a few hundredths at most. Only skippers are rated.

  • u no fleet rating — the model's level for unrated skippers is the baseline.
  • — in the performance column: last in every race sailed, so no finite least-surprisal rating exists.

Division A  18 boats · 9 races

FinSkipperSchool Races Rating
before
Perf.
rating
Δ Percentile
before → perf.
Surprise
(bits)
P(this good
or better)
Avg finish
actual / exp.
1William Bailey '24Roger Williams University Hawks 29/92.194.84+2.6557th → 99th11.7<1%1.8 / 6.5
2John Cabell '26Boston University Terriers 19/91.183.50+2.3327th → 90th11.2<1%3.0 / 8.9
3Alexander Horne '26Massachusetts Institute of Technology Engineers 19/92.482.62+0.1362nd → 69th0.132%4.4 / 5.7
4Ethan Burt '26Roger Williams University Hawks 49/91.032.26+1.2324th → 59th3.86%5.1 / 9.1
5Claire Siegel-Wilson '24Roger Williams University Hawks 19/92.252.17−0.0859th → 57th0.040%5.3 / 6.2
6Nat Edmonds '24Amherst College Mammoths 19/91.332.18+0.8531st → 57th2.013%5.4 / 8.4
7Renato Korzinek '25Boston University Terriers 49/91.221.43+0.2229th → 34th0.240%7.9 / 8.7
7Richard Kalich '26Boston University Terriers 39/91.191.41+0.2228th → 34th0.239%7.9 / 8.8
9James Knowlton '25Amherst College Mammoths 39/90.941.02+0.0821st → 23rd0.038%8.4 / 9.4
10Josh Sultanik '26Boston University Terriers 29/90.721.00+0.2817th → 22nd0.342%9.3 / 10.0
11Brooke Barry '26Massachusetts Institute of Technology Engineers 29/91.230.68−0.5529th → 17th1.167%9.9 / 8.8
11Ethan Snyder '25Roger Williams University Hawks 39/91.08u0.80−0.2825th → 18th0.364%9.9 / 9.0
13Yitian Zhu '25Massachusetts Institute of Technology Engineers 39/90.080.01−0.077th → 6th0.066%12.7 / 11.5
14Matthew Adams '24University of New Hampshire Wildcats 39/90.44u-1.08−1.5213th → 1st8.392%14.1 / 10.6
15Devon Valenta '26University of New Hampshire Wildcats 19/9-0.59-0.97−0.382nd → 1st0.394%16.3 / 13.1
16Cameron Mclean '26University of New Hampshire Wildcats 29/9-0.44-2.64−2.203rd → 1st6.698%16.8 / 12.7
17Colin Sueyoshi '26Amherst College Mammoths 29/9-1.29——1st → —3.9>99%18.1 / 14.4

Conditions — 09/24/2022 (8am–6pm)

☀️Clear sky
Humidity 51%  ·  Pressure 1010.6 hPa

Temperature

61°F
↑ 66°F   ↓ 49°F
Feels like 54°F

Wind

● from   ▲ toward
11.4 kn
avg  ·  max 13.6 kn
Gusts to 24.5 kn
From WNW (298°)

Sky & Precipitation

Cloud cover0% Sunshine11.0 hrs Humidity51% Pressure1010.6 hPa

What this breeze does at MIT — from fleet racing

Breeze: NW about 13 kn (weather reanalysis in the scorer’s units)
In fleet races at MIT in this breeze: a typical day (−0.02)
Nothing in this breeze favours either side in fleet racing here: results follow the ratings about as usual.

Orderliness is how tightly a fleet race’s finishing order follows the pre-regatta ratings, relative to a typical event (0): positive is the favourites’ day, negative the underdogs’. A value of ±0.02 means a rating gap counts about 2% less than on a typical day. This event is not a fleet race the rating model scores, so there is no actual value for it: this is what this venue’s fleet racing history says about the breeze on the day, read off the chart at the star. How this is estimated · compare venues.

Orderliness beyond the fleet at MIT by wind direction (angle) and speed (rings), fitted surface; dots are labelled regatta-days, ★ this regatta (112 labelled fleet-race days at MIT).

From the notes

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

All notes →