The Mike Horn Trophy

HostHarvard
Date11/09/2013
TypeNOT ALLOWED In-Conference
BoatFJ
ScoringTeam

#	School	Team	Rec.
1	Massachusetts Institute of Technology	Engineers	13–2
2	Boston College	Eagles	11–4
2	Tufts University	Jumbos	11–4
4	Roger Williams University	Hawks	7–8
5	Boston University	Terriers	2–13
6	Harvard University	Crimson	1–14

Pre-event forecast

Fitted on the 1,769 team race matches sailed before 2013-11-09, using the line-ups that actually raced. It called 31 of 45 matches (69%) with a log-loss of 0.485 (a coin flip scores 0.693), so the event went about as expected for the forecast. Orderliness −0.07 ± 0.29: a typical event, more orderly than 36% 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.

Place	School	Pred.	Strength	Boats (skipper / crew, boat value)	Pred. win %	Record	vs forecast
1	Massachusetts Institute of Technology Engineers	4	7.33 school −0.46	Joseph Paggi / Jorlyn Le Garrec 2.47 Alexander Stewart / Paige Omura Laura Dunphy / Lisa Sukharev-Chuyan 2.06	53%	13–2 (87%)	+5.0
2	Boston College Eagles	1	8.31 school +0.12	Allyson Donahue / Allison Ferraris Jacob Martz / Katherine Downey Henry Dumke / Emily Vasiliou 2.56	75%	11–4 (73%)	−0.3
2	Tufts University Jumbos	3	7.90 school +0.19	Duncan Swain / Julie Pringle 3.12 Rolfe Glover / Leah Fletcher 2.46 Kate Levinson / Emily Lynn 1.90	67%	11–4 (73%)	+1.0
4	Roger Williams University Hawks	2	7.94 school +0.30	A. Tucker Atterbury / Tatiana Chiu 2.68 Rachel Perry / Morgan Wheeler 2.07 Jonathan Pope / Beatrice Bellacosa 2.61	67%	7–8 (47%)	−3.1
5	Boston University Terriers	6	4.53 school −0.13	Conor Fowler / Hannah Callahan Anna Booras / Simon Bertocci Justin Kusiel / Joseph Benoit	6%	2–13 (13%)	+1.2
6	Harvard University Crimson	5	6.37 school −0.13, home +0.37	Sophie Bermudez / Ashleigh Inglis 2.48 Bennett Capozzi / Cara Kennedy Cuomo 1.95 William Bloxham / Adam Brodheim 1.30	33%	1–14 (7%)	−3.9

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.

Place	School	Strength	Perf.	Record	Exp. wins	Δ	Surprise (bits)	P(this good or better)
1	Massachusetts Institute of Technology	7.33	9.42	13–2	7.9	+2.09	7.0	<1%
5	Boston University	4.53	5.53	2–13	0.8	+1.01	1.0	20%
2	Tufts University	7.90	8.29	11–4	10.0	+0.39	0.3	38%
2	Boston College	8.31	8.20	11–4	11.3	−0.11	0.0	70%
4	Roger Williams University	7.94	6.83	7–8	10.1	−1.11	2.4	98%
6	Harvard University	6.37	3.55	1–14	4.9	−2.82	6.7	>99%

By skipper

Skipper	School	Value	Perf.	Record	Exp. wins	Δ	Surprise (bits)	P(this good or better)
Joseph Paggi	Massachusetts Institute of Technology	2.47	4.56	13–2	7.9	+2.09	7.0	<1%
Alexander Stewart	Massachusetts Institute of Technology	2.67	4.76	13–2	7.9	+2.09	7.0	<1%
Laura Dunphy	Massachusetts Institute of Technology	2.06	4.15	13–2	7.9	+2.09	7.0	<1%
Conor Fowler	Boston University	2.20	3.21	2–13	0.8	+1.01	1.0	20%
Anna Booras	Boston University	1.04	2.05	2–13	0.8	+1.01	1.0	20%
Justin Kusiel	Boston University	0.90	1.90	2–13	0.8	+1.01	1.0	20%
Duncan Swain	Tufts University	3.12	3.51	11–4	10.0	+0.39	0.3	38%
Rolfe Glover	Tufts University	2.46	2.84	11–4	10.0	+0.39	0.3	38%
Kate Levinson	Tufts University	1.90	2.29	11–4	10.0	+0.39	0.3	38%
Allyson Donahue	Boston College	2.46	2.34	11–4	11.3	−0.11	0.0	70%
Jacob Martz	Boston College	2.70	2.59	11–4	11.3	−0.11	0.0	70%
Henry Dumke	Boston College	2.56	2.45	11–4	11.3	−0.11	0.0	70%
A. Tucker Atterbury	Roger Williams University	2.68	1.57	7–8	10.1	−1.11	2.4	98%
Rachel Perry	Roger Williams University	2.07	0.96	7–8	10.1	−1.11	2.4	98%
Jonathan Pope	Roger Williams University	2.61	1.50	7–8	10.1	−1.11	2.4	98%
Sophie Bermudez	Harvard University	2.48	-0.33	1–14	4.9	−2.82	6.7	>99%
Bennett Capozzi	Harvard University	1.95	-0.87	1–14	4.9	−2.82	6.7	>99%
William Bloxham	Harvard University	1.30	-1.51	1–14	4.9	−2.82	6.7	>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	#	Winner	Loser	P(winner)
1	1	Massachusetts Institute of Technology	Harvard University	68%
1	2	Roger Williams University	Boston University	97%
1	3	Boston College	Tufts University	54%
1	4	Harvard University	Boston University	87%
1	5	Massachusetts Institute of Technology	Tufts University	31%	upset
1	6	Boston College	Roger Williams University	56%
1	7	Tufts University	Boston University	97%
1	8	Roger Williams University	Harvard University	83%
1	9	Massachusetts Institute of Technology	Boston College	27%	upset
1	10	Tufts University	Roger Williams University	50%	upset
1	11	Massachusetts Institute of Technology	Boston University	94%
1	12	Boston College	Harvard University	87%
1	13	Massachusetts Institute of Technology	Roger Williams University	35%	upset
1	14	Tufts University	Harvard University	82%
1	15	Boston College	Boston University	98%
2	16	Massachusetts Institute of Technology	Harvard University	72%
2	17	Roger Williams University	Boston University	97%
2	18	Boston College	Tufts University	60%
2	19	Boston University	Harvard University	14%	upset
2	20	Tufts University	Massachusetts Institute of Technology	63%
2	21	Roger Williams University	Boston College	40%	upset
2	22	Tufts University	Boston University	97%
2	23	Roger Williams University	Harvard University	83%
2	24	Boston College	Massachusetts Institute of Technology	73%
2	25	Tufts University	Roger Williams University	49%	upset
2	26	Massachusetts Institute of Technology	Boston University	94%
2	27	Boston College	Harvard University	88%
2	28	Massachusetts Institute of Technology	Roger Williams University	37%	upset
2	29	Tufts University	Harvard University	82%
2	30	Boston College	Boston University	98%
3	31	Massachusetts Institute of Technology	Harvard University	73%
3	32	Roger Williams University	Boston University	97%
3	33	Tufts University	Boston College	38%	upset
3	34	Boston University	Harvard University	14%	upset
3	35	Massachusetts Institute of Technology	Tufts University	39%	upset
3	36	Boston College	Roger Williams University	61%
3	37	Tufts University	Boston University	96%
3	38	Roger Williams University	Harvard University	82%
3	39	Massachusetts Institute of Technology	Boston College	28%	upset
3	40	Tufts University	Roger Williams University	48%	upset
3	41	Massachusetts Institute of Technology	Boston University	94%
3	42	Boston College	Harvard University	88%
3	43	Massachusetts Institute of Technology	Roger Williams University	38%	upset
3	44	Tufts University	Harvard University	81%
3	45	Boston College	Boston University	98%

Round 1

Team	Rec.	Engineers	Eagles	Jumbos	Hawks	Terriers	Crimson
Massachusetts Institute of Technology Engineers	5–0	✕	W	W	W	W	W
Boston College Eagles	4–1	L	✕	W	W	W	W
Tufts University Jumbos	3–2	L	L	✕	W	W	W
Roger Williams University Hawks	2–3	L	L	L	✕	W	W
Boston University Terriers	0–5	L	L	L	L	✕	L
Harvard University Crimson	1–4	L	L	L	L	W	✕

Round 2

Team	Rec.	Engineers	Eagles	Jumbos	Hawks	Terriers	Crimson
Massachusetts Institute of Technology Engineers	3–2	✕	L	L	W	W	W
Boston College Eagles	4–1	W	✕	W	L	W	W
Tufts University Jumbos	4–1	W	L	✕	W	W	W
Roger Williams University Hawks	3–2	L	W	L	✕	W	W
Boston University Terriers	1–4	L	L	L	L	✕	W
Harvard University Crimson	0–5	L	L	L	L	L	✕

Round 3

Team	Rec.	Engineers	Eagles	Jumbos	Hawks	Terriers	Crimson
Massachusetts Institute of Technology Engineers	5–0	✕	W	W	W	W	W
Boston College Eagles	3–2	L	✕	L	W	W	W
Tufts University Jumbos	4–1	L	W	✕	W	W	W
Roger Williams University Hawks	2–3	L	L	L	✕	W	W
Boston University Terriers	1–4	L	L	L	L	✕	W
Harvard University Crimson	0–5	L	L	L	L	L	✕

Filter by team:

#					Pos.
1	Engineers	1-2-5	def.	Crimson	3-4-6
2	Hawks	1-2-4	def.	Terriers	3-5-6
3	Eagles	1-2-5	def.	Jumbos	3-4-6
4	Crimson	1-2-4	def.	Terriers	3-5-6
5	Engineers	2-3-4	def.	Jumbos	1-5-6
6	Eagles	1-2-3	def.	Hawks	4-5-6
7	Jumbos	1-2-4	def.	Terriers	3-5-6
8	Hawks	1-2-4	def.	Crimson	3-5-6
9	Engineers	1-3-5	def.	Eagles	2-4-6
10	Jumbos	1-2-5	def.	Hawks	3-4-6
11	Engineers	1-2-4	def.	Terriers	3-5-6
12	Eagles	1-2-4	def.	Crimson	3-5-6
13	Engineers	1-2-5	def.	Hawks	3-4-6
14	Jumbos	1-2-4	def.	Crimson	3-5-6
15	Eagles	1-2-4	def.	Terriers	3-5-6
16	Engineers	2-3-4	def.	Crimson	1-5-6
17	Hawks	1-3-4	def.	Terriers	2-5-6
18	Eagles	1-2-6	def.	Jumbos	3-4-5
19	Terriers	1-2-4	def.	Crimson	3-5-6
20	Jumbos	1-3-5	def.	Engineers	2-4-6
21	Hawks	1-2-6	def.	Eagles	3-4-5
22	Jumbos	1-3-4	def.	Terriers	2-5-6
23	Hawks	1-2-4	def.	Crimson	3-5-6
24	Eagles	1-2-5	def.	Engineers	3-4-6
25	Jumbos	1-2-6	def.	Hawks	3-4-5
26	Engineers	1-2-4	def.	Terriers	3-5-6
27	Eagles	1-4-6	def.	Crimson	2-3-5 RAF
28	Engineers	1-2-6	def.	Hawks	3-4-5
29	Jumbos	1-2-6	def.	Crimson	3-4-5
30	Eagles	1-2-3	def.	Terriers	4-5-6
31	Engineers	1-2-5	def.	Crimson	3-4-6
32	Hawks	1-2-3	def.	Terriers	4-5-6
33	Jumbos	1-2-6	def.	Eagles	3-4-5
34	Terriers	1-2-6	def.	Crimson	3-4-5
35	Engineers	1-3-5	def.	Jumbos	2-4-6
36	Eagles	1-2-5	def.	Hawks	3-4-6
37	Jumbos	1-2-5	def.	Terriers	3-4-6
38	Hawks	1-2-5	def.	Crimson	3-4-6
39	Engineers	1-2-5	def.	Eagles	3-4-6
40	Jumbos	1-2-3	def.	Hawks	4-5-6
41	Engineers	1-2-3	def.	Terriers	4-5-6
42	Eagles	1-2-5	def.	Crimson	3-4-6
43	Engineers	1-2-4	def.	Hawks	3-5-6
44	Jumbos	2-3-4	def.	Crimson	1-5-6
45	Eagles	1-2-6	def.	Terriers	3-4-5

School	Team	Skippers	Crew
Massachusetts Institute of Technology	Engineers	Alexander Stewart '17 Joseph Paggi '16 Laura Dunphy '15	Paige Omura '17 Jorlyn Le Garrec '17 Lisa Sukharev-Chuyan '16
Boston College	Eagles	Allyson Donahue '17 Henry Dumke '17 Jacob Martz '17	Allison Ferraris '17 Emily Vasiliou '16 Katherine Downey '16
Tufts University	Jumbos	Duncan Swain '15 Kate Levinson '15 Rolfe Glover '16	Julie Pringle '14 Emily Lynn '15 Leah Fletcher '17
Roger Williams University	Hawks	A. Tucker Atterbury '16 Jonathan Pope '17 Rachel Perry '15	Tatiana Chiu '17 Beatrice Bellacosa '17 Morgan Wheeler '15
Boston University	Terriers	Anna Booras '16 Conor Fowler '14 Justin Kusiel '14	Simon Bertocci '16 Hannah Callahan '16 Joseph Benoit '16
Harvard University	Crimson	Bennett Capozzi '17 Sophie Bermudez '16 William Bloxham '17	Cara Kennedy Cuomo '17 Ashleigh Inglis '15 Adam Brodheim '16

Conditions — 11/09/2013 (8am–6pm)

☁️Overcast

Humidity 47% · Pressure 1021.2 hPa

Temperature

41°F

↑ 45°F ↓ 33°F

Feels like 32°F

Wind

● from ▲ toward

8.8 kn

avg · max 10.8 kn

Gusts to 21.4 kn

From WNW (285°)

Sky & Precipitation

Cloud cover57% Sunshine9.2 hrs Humidity47% Pressure1021.2 hPa

What this breeze does at Harvard — from fleet racing

Breeze: W about 11 kn (weather reanalysis in the scorer’s units)

In fleet races at Harvard in this breeze: a typical day (+0.00)

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 ±0.00 means a rating gap counts about 1% more 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 Harvard by wind direction (angle) and speed (rings), fitted surface; dots are labelled regatta-days, ★ this regatta (59 labelled fleet-race days at Harvard).

From the notes

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

How team race forecasts workThree boats’ ratings, a school term and a home edge; 78.8% of held-out matches called.
Play 1, Play 2, Play 4: how team races are wonHow often each play happens, how that shifts with breeze and level, and whether a star wins differently.
Which team races go to form?Orderliness is a real property of a team race regatta and nothing known in advance predicts it.

All notes →

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