LaserPerformance Men's Singlehanded Nationals

HostUC Santa Barbara
Date11/09/2019
TypeNational Championship Finals
BoatLaser
ScoringSinglehanded

#	School	Sailor	TOT
1	St. Mary's College of Maryland	Leo Boucher '22	65
2	University of Miami	Richard Mccann '23	68
3	U. S. Coast Guard Academy	Gage Schoenherr '21	69
4	Hobart and William Smith Colleges	Charles Carraway '22	70
5	Brown University	Connor Nelson '24	85
6	U. S. Coast Guard Academy	Richie Gordon '21	99
7	California State University Long Beach	Paul Didham '22	99
8	Christopher Newport University	Vir Menon '20	116
9	U. S. Merchant Marine Academy	Carrson Pearce '21	118
10	U. S. Naval Academy	Gavin Mcjones '23	138
11	Stanford University	John Kirkpatrick '22	144
12	Boston University	Javier De Urdanibia Panos '21	151
13	College of Charleston	Malcolm Benn Smith '21	155
14	College of Charleston	Steven Leuck '20	159
15	Tulane University	Cameron Giblin '23	187
16	University of Wisconsin	Jonathan Bailey '22	208
17	University of Washington	Erik Skeel '20	224
18	University of Wisconsin	Patrick Schmidt '22	239

School	Sailor	R1	R2	R3	R4	R5	R6	R7	R8	R9	R10	R11	R12	R13	R14	TOT
St. Mary's College of Maryland	Leo Boucher '22	12	3	1	1	4	1	8	3	9	6	2	5	3	7	65
University of Miami	Richard Mccann '23	9	2	3	7	7	7	2	1	2	2	10	9	4	3	68
U. S. Coast Guard Academy	Gage Schoenherr '21	3	8	4	11	1	4	5	4	1	1	8	4	11	4	69
Hobart and William Smith Colleges	Charles Carraway '22	11	1	8	3	2	10	12	9	5	5	1	1	1	1	70
Brown University	Connor Nelson '24	14	6	2	6	5	6	6	5	3	12	7	6	5	2	85
U. S. Coast Guard Academy	Richie Gordon '21	5	7	9	2	10	14	1	2	6	3	4	17	9	10	99
California State University Long Beach	Paul Didham '22	8	11	6	5	3	11	3	6	7	11	5	8	7	8	99
Christopher Newport University	Vir Menon '20	4	5	7	4	6	2	7	7	4	13	13	13	16	15	116
U. S. Merchant Marine Academy	Carrson Pearce '21	2	4	13	10	11	12	13	13	14	14	3	2	2	5	118
U. S. Naval Academy	Gavin Mcjones '23	1	9	12	8	9	8	9	12	10	10	14	11	12	13	138
Stanford University	John Kirkpatrick '22	13	15	5	9	8	9	4	15	12	4	17	10	6	17	144
Boston University	Javier De Urdanibia Panos '21	10	10	14	15	14	3	14	10	11	7	12	7	15	9	151
College of Charleston	Malcolm Benn Smith '21	6	12	10	12	12	13	11	11	13	9	9	12	14	11	155
College of Charleston	Steven Leuck '20	17	13	11	13	13	5	10	8	8	8	16	15	10	12	159
Tulane University	Cameron Giblin '23	16	16	18	14	15	16	18	18	18	15	6	3	8	6	187
University of Wisconsin	Jonathan Bailey '22	7	17	15	17	18	15	17	14	16	18	11	14	13	16	208
University of Washington	Erik Skeel '20	18	14	16	16	16	18	15	16	15	17	15	16	18	14	224
University of Wisconsin	Patrick Schmidt '22	15	18	17	18	17	17	16	17	17	16	18	18	17	18	239

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.

Division A 18 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.
1	Leo Boucher '22	St. Mary's College of Maryland	14/14	4.61	4.73	+0.12	99th → 99th	0.1	29%	4.6 / 5.7
2	Richard Mccann '23	University of Miami	14/14	3.66	4.74	+1.08	93rd → 99th	4.9	5%	4.9 / 8.0
3	Gage Schoenherr '21	U. S. Coast Guard Academy Bears 1	14/14	3.60	4.66	+1.07	92nd → 99th	4.8	4%	4.9 / 8.2
4	Charles Carraway '22	Hobart and William Smith Colleges	14/14	3.46	4.61	+1.15	90th → 99th	5.5	3%	5.0 / 8.7
5	Connor Nelson '24	Brown University	14/14	3.47	4.31	+0.84	90th → 98th	3.2	12%	6.1 / 8.6
6	Paul Didham '22	California State University Long Beach	14/14	2.70	4.12	+1.42	72nd → 96th	8.4	9%	7.1 / 10.7
6	Richie Gordon '21	U. S. Coast Guard Academy Bears 2	14/14	3.30	3.70	+0.40	87th → 93rd	0.8	17%	7.1 / 9.1
8	Vir Menon '20	Christopher Newport University	14/14	2.95	3.46	+0.51	81st → 89th	1.3	21%	8.3 / 10.0
9	Carrson Pearce '21	U. S. Merchant Marine Academy	14/14	3.51	3.43	−0.07	90th → 89th	0.0	50%	8.4 / 8.5
10	Gavin Mcjones '23	U. S. Naval Academy	14/14	3.11	3.35	+0.24	84th → 89th	0.3	55%	9.9 / 9.6
11	John Kirkpatrick '22	Stanford University	14/14	3.00	2.89	−0.11	82nd → 78th	0.1	57%	10.3 / 10.0
12	Javier De Urdanibia Panos '21	Boston University	14/14	3.48	3.01	−0.47	90th → 83rd	1.4	80%	10.8 / 8.6
13	Malcolm Benn Smith '21	College of Charleston Cougars 2	14/14	3.15	3.13	−0.02	84th → 84th	0.0	78%	11.1 / 9.5
14	Steven Leuck '20	College of Charleston Cougars 1	14/14	3.36	2.83	−0.53	89th → 77th	1.7	87%	11.4 / 8.9
15	Cameron Giblin '23	Tulane University	14/14	3.10	1.51	−1.59	84th → 38th	13.5	97%	13.4 / 9.6
16	Jonathan Bailey '22	University of Wisconsin Badgers 1	14/14	2.35	1.72	−0.63	60th → 43rd	2.1	91%	14.9 / 11.6
17	Erik Skeel '20	University of Washington	14/14	1.92	1.56	−0.35	48th → 39th	0.6	97%	16.0 / 12.8
18	Patrick Schmidt '22	University of Wisconsin Badgers 2	14/14	1.89	0.97	−0.92	48th → 22nd	3.9	>99%	17.1 / 12.8

Conditions — 11/09/2019 (8am–6pm)

☀️Clear sky

Humidity 19% · Pressure 1016.4 hPa

Temperature

80°F

↑ 84°F ↓ 63°F

Feels like 75°F

Wind

● from ▲ toward

3.5 kn

avg · max 7.0 kn

Gusts to 19.2 kn

From SW (231°)

Sky & Precipitation

Cloud cover0% Sunshine10.2 hrs Humidity19% Pressure1016.4 hPa

What this breeze does at UC Santa Barbara — from fleet racing

Breeze: SW about 7 kn (weather reanalysis in the scorer’s units)

In fleet races at UC Santa Barbara in this breeze: a typical day (−0.01)

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 ±0.01 means a rating gap counts about 1% 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 UC Santa Barbara by wind direction (angle) and speed (rings), fitted surface; dots are labelled regatta-days, ★ this regatta (46 labelled fleet-race days at UC Santa Barbara).

From the notes

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

What a rating point is worthA one-point edge is a 73% chance to beat another boat in a race; two points is 88%.
Should the best sailor sail A?Add your two skippers’ ratings and compare with the field; above it the better skipper belongs in A.
Which venues get orderly in a breeze?Some venues get orderly when it blows, a few get messier; the fleet comes first.

All notes →

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