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Cal Team Race

Pre-event forecast

Fitted on the 15,913 team race matches sailed before 2022-02-12, using the line-ups that actually raced. It called 21 of 23 matches (91%) with a log-loss of 0.263 (a coin flip scores 0.693), so the event went well for the forecast. Orderliness +0.60 ± 0.40: an orderly event, more orderly than 96% of college team race events (temperature of the forecast log-odds that best fits the results, relative to the league average; nothing about the field, venue or wind predicts it, see the Weather tab). vs forecast is a team's wins minus the wins the model expected from the matches it sailed: positive means it beat its rating.

PlaceSchool Pred. Strength Boats (skipper / crew, boat value) Pred. win % Recordvs forecast
1California Maritime Academy
Keelhaulers		17.72
school +0.17		Brock Paquin / Clay Myers 2.81
Maxwell Roth / Justin Zmina
Jacob Fisker-Andersen / Cyrus Khaleeli 2.66		96%7–0 (100%)+0.3
2Cal Poly University S.L.O.
Mustangs		35.24
school +0.13		Kai Ponting / Maura Darby 2.01
Max Case / Peyton Nienaber 1.34
Sean Ross / Corey Quillen		56%5–2 (71%)+1.1
3University of California at San Diego
Tritons 1		25.04
school +0.07		Nicholas Dorn / Sebastien Franck 2.20
Noah Barton / Drake Coy 1.56
Svenja Leonard / Elizaveta Pertseva		59%4–3 (57%)−0.1
4University of California at Berkeley
Bears		44.47
home +0.36		Will Cornell / Beck Boyea 3–3
Enzo Cremers / Ethan Lisle 3–3
Nate Ingebritson / Audrey Turcotte 3–3
Ethan Lisle / Enzo Cremers 1–0
Nate Ingebritson / Jessica Stewart 1–0
Michael Mcculloch / Audrey Turcotte 1–0		48%4–3 (57%)+0.6
5University of California at Berkeley
Mama Bears		53.99
home +0.36		Kingsley Ehrich / Lindsea Hayes 1.51
Fiona Wu / Marlena Tavernier-Fine 2–3
Katherine Olsen / Iesha Carter 1.49 2–3
Mackenzie Berwick / Marlena Tavernier-Fine 0.96 0–1
Katherine Olsen / Nora Giannoni 1.56 0–1		38%2–4 (33%)−0.3
6University of California at Los Angeles
Bruins		63.94
school −0.28		Gideon Burnes Heath / Mary Jane Howland 1.92
Ian Marshall / Teresa Dang 1.26
Zack Taylor / Marianna Shand		37%1–5 (17%)−1.2
7University of California at San Diego
Tritons 2		71.67
school +0.07		Tobie Bloom / Audra Spokas-Jaros
Alexander Bastuba / Bella Valente
Mckenna Roonan / Jessica Dally 0–3
Sean Lipps / Jonathan Hickey 0–2
Jonathan Hickey / Sean Lipps 0–1		7%0–6 (0%)−0.4

Surprisal: who beat their rating

Pre-event strength against the strength that would have made each record least surprising, everything else fixed. Δ is in the model's logit units, the same scale as the boat values above (a side is three boats plus the school term); green is a clear over-performance, red a clear under-performance. Skippers need three or more matches.

PlaceSchoolStrength Perf.RecordExp.
wins		ΔSurprise
(bits)		P(this good
or better)
1California Maritime Academy Keelhaulers7.7210.727–06.7≥+3.000.473%
2Cal Poly University S.L.O. Mustangs5.246.525–23.9+1.281.028%
4University of California at Berkeley Bears4.474.974–33.4+0.490.245%
3University of California at San Diego Tritons 15.044.964–34.1−0.080.071%
5University of California at Berkeley Mama Bears3.993.682–42.3−0.310.179%
6University of California at Los Angeles Bruins3.942.371–52.2−1.571.398%
7University of California at San Diego Tritons 21.67-1.330–60.4≤−3.000.6>99%

By skipper

SkipperSchoolValue Perf.RecordExp.
wins		ΔSurprise
(bits)		P(this good
or better)
Maxwell RothCalifornia Maritime Academy1.934.937–06.7≥+3.000.473%
Jacob Fisker-AndersenCalifornia Maritime Academy2.665.667–06.7≥+3.000.473%
Enzo CremersUniversity of California at Berkeley0.741.163–32.5+0.410.150%
Svenja LeonardUniversity of California at San Diego1.091.014–34.1−0.080.071%
Katherine OlsenUniversity of California at Berkeley1.491.172–42.3−0.310.179%
Gideon Burnes HeathUniversity of California at Los Angeles1.920.351–52.2−1.571.398%
Ian MarshallUniversity of California at Los Angeles1.26-0.311–52.2−1.571.398%
Tobie BloomUniversity of California at San Diego0.53-2.470–60.4≤−3.000.6>99%
Alexander BastubaUniversity of California at San Diego0.76-2.240–60.4≤−3.000.6>99%

Match by match

Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).

Rd#WinnerLoser P(winner)
11California Maritime Academy KeelhaulersCal Poly University S.L.O. Mustangs92%
12University of California at Berkeley Mama BearsUniversity of California at Los Angeles Bruins50%upset
13University of California at Berkeley BearsUniversity of California at San Diego Tritons 137%upset
14California Maritime Academy KeelhaulersUniversity of California at San Diego Tritons 2100%
15Cal Poly University S.L.O. MustangsUniversity of California at Berkeley Mama Bears79%
16University of California at Berkeley BearsUniversity of California at Los Angeles Bruins66%
17University of California at San Diego Tritons 1University of California at San Diego Tritons 297%
18California Maritime Academy KeelhaulersUniversity of California at Berkeley Mama Bears98%
19Cal Poly University S.L.O. MustangsUniversity of California at Berkeley Bears65%
110University of California at San Diego Tritons 1University of California at Los Angeles Bruins75%
111University of California at Berkeley Mama BearsUniversity of California at San Diego Tritons 289%
112California Maritime Academy KeelhaulersUniversity of California at Berkeley Bears96%
113Cal Poly University S.L.O. MustangsUniversity of California at San Diego Tritons 155%
114University of California at Los Angeles BruinsUniversity of California at San Diego Tritons 288%
115University of California at Berkeley BearsUniversity of California at Berkeley Mama Bears66%
116California Maritime Academy KeelhaulersUniversity of California at San Diego Tritons 194%
117Cal Poly University S.L.O. MustangsUniversity of California at Los Angeles Bruins78%
118University of California at Berkeley BearsUniversity of California at San Diego Tritons 285%
119University of California at San Diego Tritons 1University of California at Berkeley Mama Bears67%
120California Maritime Academy KeelhaulersUniversity of California at Los Angeles Bruins98%
121Cal Poly University S.L.O. MustangsUniversity of California at San Diego Tritons 298%
123California Maritime Academy KeelhaulersCal Poly University S.L.O. Mustangs92%
322University of California at San Diego Tritons 1University of California at Berkeley Bears58%

Round 1

TeamRec.KeelhaulersMustangs1BearsBearsBruins2
California Maritime Academy Keelhaulers7–0✕WWWWWW
Cal Poly University S.L.O. Mustangs5–2L✕WWWWW
University of California at San Diego Tritons 13–9LL✕WWL✕
University of California at Berkeley Bears6–6LLL✕✕WL
University of California at Berkeley Mama Bears6–6LLL✕✕WL
University of California at Los Angeles Bruins1–5LLWLL✕W
University of California at San Diego Tritons 23–9LL✕WWL✕

Round 3

TeamRec.1BearsBears2
University of California at San Diego Tritons 11–0✕WW✕
University of California at Berkeley Bears0–1L✕✕L
University of California at Berkeley Mama Bears0–1L✕✕L
University of California at San Diego Tritons 21–0✕WW✕
# Winner Loser Pos.
1 California Maritime Academy Keelhaulers 1-2-4 def. Cal Poly University S.L.O. Mustangs 3-5-6
2 University of California at Berkeley Mama Bears 1-2-4 def. University of California at Los Angeles Bruins 3-5-6
3 University of California at Berkeley Bears 1-2-6 def. University of California at San Diego Tritons 1 3-4-5
4 California Maritime Academy Keelhaulers 1-2-3 def. University of California at San Diego Tritons 2 4-5-6
5 Cal Poly University S.L.O. Mustangs 1-2-6 def. University of California at Berkeley Mama Bears 3-4-5
6 University of California at Berkeley Bears 1-2-6 def. University of California at Los Angeles Bruins 3-4-5
7 University of California at San Diego Tritons 1 1-2-3 def. University of California at San Diego Tritons 2 4-5-6
8 California Maritime Academy Keelhaulers 1-2-3 def. University of California at Berkeley Mama Bears 4-5-6
9 Cal Poly University S.L.O. Mustangs 1-2-6 def. University of California at Berkeley Bears 3-4-5
10 University of California at San Diego Tritons 1 1-2-6 def. University of California at Los Angeles Bruins 3-4-5
11 University of California at Berkeley Mama Bears 1-2-4 def. University of California at San Diego Tritons 2 3-5-6
12 California Maritime Academy Keelhaulers 1-2-3 def. University of California at Berkeley Bears 4-5-6
13 Cal Poly University S.L.O. Mustangs 1-2-3 def. University of California at San Diego Tritons 1 4-5-6
14 University of California at Los Angeles Bruins 1-2-4 def. University of California at San Diego Tritons 2 3-5-6
15 University of California at Berkeley Bears 1-4-5 def. University of California at Berkeley Mama Bears 2-3-6
16 California Maritime Academy Keelhaulers 1-2-3 def. University of California at San Diego Tritons 1 4-5-6
17 Cal Poly University S.L.O. Mustangs 1-2-4 def. University of California at Los Angeles Bruins 3-5-6
18 University of California at Berkeley Bears 1-2-5 def. University of California at San Diego Tritons 2 3-4-6
19 University of California at San Diego Tritons 1 1-2-4 def. University of California at Berkeley Mama Bears 3-5-6
20 California Maritime Academy Keelhaulers 1-2-3 def. University of California at Los Angeles Bruins 4-5-6
21 Cal Poly University S.L.O. Mustangs 1-2-5 def. University of California at San Diego Tritons 2 3-4-6
23 California Maritime Academy Keelhaulers 1-2-3 def. Cal Poly University S.L.O. Mustangs 4-5-6
22 University of California at San Diego Tritons 1 1-2-4 def. University of California at Berkeley Bears 3-5-6

Conditions — 02/12/2022 (8am–6pm)

☀️Clear sky
Humidity 60%  ·  Pressure 1020.2 hPa

Temperature

66°F
↑ 73°F   ↓ 54°F
Feels like 64°F

Wind

● from   ▲ toward
6.1 kn
avg  ·  max 8.4 kn
Gusts to 18.5 kn
From NE (48°)

Sky & Precipitation

Cloud cover52% Sunshine9.9 hrs Humidity60% Pressure1020.2 hPa

What this breeze does at Berkeley — from fleet racing

Breeze: NE about 9 kn (weather reanalysis in the scorer’s units)
In fleet races at Berkeley in this breeze: an underdogs’ day (−0.02)
Underdogs’ conditions: in fleet racing here the fleet bunches up and lower-rated boats beat the seeds more often than 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 Berkeley by wind direction (angle) and speed (rings), fitted surface; dots are labelled regatta-days, ★ this regatta (24 labelled fleet-race days at Berkeley).

From the notes

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

All notes →