| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
University of Michigan | Wolverines | 7–1 |
| 2 |  |
University of Wisconsin | Badgers | 5–3 |
| 3 |  |
University of Wisconsin Milwaukee | Panthers | 4–4 |
| 4 |  |
University of Minnesota | Gophers | 3–5 |
| 5 |  |
Northwestern University | Wildcats | 2–3 |
| 6 |  |
Marquette University | Golden Eagles | 0–5 |
Each side is the sum of its three boats (the skipper's fleet rating times 0.72 plus a team race offset learned from their past matches, plus the crew's offset and a small experience term) plus the school's team race strength and a home-water edge for the host. The win probability is a logistic function of the difference. The forecast was made before the event: the model was refitted on every match sailed before the first day, with fleet ratings as of the week before.
Fitted on the 11,499 team race matches sailed before 2018-04-21, using the line-ups that actually raced. It called 15 of 21 matches (71%) with a log-loss of 0.496 (a coin flip scores 0.693), so the event went about as expected for the forecast. Orderliness +0.19 ± 0.46: a typical event, more orderly than 65% 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.
For each side, the performance strength is the value that would have made its record least surprising: the strength at which the model's expected wins over exactly these opponents equal the actual wins, with every opponent fixed at its pre-event strength. For a skipper, the same shift is applied only to their boat in the matches they sailed, with team-mates and opponents fixed; team-mates who sailed every match together therefore get the same number. Surprise is in bits: -log2 of the probability of the record at the pre-event strength minus the same at the performance strength. P(this good or better) treats matches as independent coin flips at the model's pre-event probabilities, so it ignores form within the day; read it as a rough guide.
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 | University of Michigan | 6.94 | 8.16 | 7–1 | 5.6 | +1.22 | 1.2 | 23% |
| 4 | University of Minnesota | 5.62 | 5.84 | 3–5 | 2.7 | +0.22 | 0.1 | 54% |
| 3 | University of Wisconsin Milwaukee | 6.16 | 6.30 | 4–4 | 3.8 | +0.14 | 0.0 | 57% |
| 2 | University of Wisconsin | 6.99 | 6.59 | 5–3 | 5.6 | −0.40 | 0.2 | 83% |
| 5 | Northwestern University | 6.42 | 5.40 | 2–3 | 3.0 | −1.02 | 0.7 | 94% |
| 6 | Marquette University | 3.93 | 0.93 | 0–5 | 0.4 | ≤−3.00 | 0.6 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| August Sturm | University of Michigan | 2.64 | 3.86 | 7–1 | 5.6 | +1.22 | 1.2 | 23% |
| Mason Wolters | University of Michigan | 2.41 | 3.64 | 7–1 | 5.6 | +1.22 | 1.2 | 23% |
| Michael Valverde | University of Minnesota | 1.28 | 2.24 | 2–3 | 1.0 | +0.96 | 0.7 | 27% |
| Alex Keck | University of Wisconsin Milwaukee | 1.63 | 1.78 | 4–4 | 3.8 | +0.14 | 0.0 | 57% |
| Charles Bocklet | University of Wisconsin | 2.41 | 2.01 | 5–3 | 5.6 | −0.40 | 0.2 | 83% |
| Charlie Kutschenreuter | University of Wisconsin | 2.32 | 1.92 | 5–3 | 5.6 | −0.40 | 0.2 | 83% |
| Noah Janssen | University of Wisconsin | 1.84 | 1.44 | 5–3 | 5.6 | −0.40 | 0.2 | 83% |
| Cailin Oakes | University of Minnesota | 1.44 | 0.28 | 1–2 | 1.7 | −1.17 | 0.5 | 95% |
| Bobby Sessions | Marquette University | 1.20 | -1.80 | 0–5 | 0.4 | ≤−3.00 | 0.6 | >99% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | Wolverines | Badgers | Panthers | Gophers | Wildcats | Eagles | |
|---|---|---|---|---|---|---|---|---|
| University of Michigan Wolverines | 7–1 | ✕ | W | W | W | L | W |
| University of Wisconsin Badgers | 5–3 | L | ✕ | W | L | W | W |
| University of Wisconsin Milwaukee Panthers | 4–4 | L | L | ✕ | W | W | W |
| University of Minnesota Gophers | 3–5 | L | W | L | ✕ | W | W |
| Northwestern University Wildcats | 2–3 | W | L | L | L | ✕ | W |
| Marquette University Golden Eagles | 0–5 | L | L | L | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Gophers | 1-4-5 | def. | |
Wildcats | 2-3-6 |
| 2 | |
Panthers | 1-2-6 | def. | |
Golden Eagles | 3-4-5 |
| 3 | |
Wolverines | 1-2-5 | def. | |
Badgers | 3-4-6 |
| 4 | |
Wildcats | 1-2-3 | def. | |
Golden Eagles | 4-5-6 |
| 5 | |
Badgers | 1-2-4 | def. | |
Gophers | 3-5-6 |
| 6 | |
Wolverines | 1-2-3 | def. | |
Panthers | 4-5-6 |
| 7 | |
Badgers | 1-2-5 | def. | |
Golden Eagles | 3-4-6 |
| 8 | |
Panthers | 1-2-5 | def. | |
Wildcats | 3-4-6 |
| 9 | |
Wolverines | 1-2-5 | def. | |
Gophers | 3-4-6 |
| 10 | |
Badgers | 1-2-5 | def. | |
Panthers | 3-4-6 |
| 11 | |
Gophers | 1-3-4 | def. | |
Golden Eagles | 2-5-6 |
| 12 | |
Wildcats | 1-2-5 | def. | |
Wolverines | 3-4-6 |
| 13 | |
Panthers | 1-2-3 | def. | |
Gophers | 4-5-6 |
| 14 | |
Badgers | 1-4-5 | def. | |
Wildcats | 2-3-6 |
| 15 | |
Wolverines | 1-2-6 | def. | |
Golden Eagles | 3-4-5 |
| 16 | |
Badgers | 1-3-4 | def. | |
Panthers | 2-5-6 |
| 17 | |
Wolverines | 1-3-4 | def. | |
Gophers | 2-5-6 |
| 18 | |
Gophers | 1-4-5 | def. | |
Badgers | 2-3-6 |
| 19 | |
Wolverines | 1-2-6 | def. | |
Panthers | 3-4-5 |
| 20 | |
Panthers | 1-2-3 | def. | |
Gophers | 4-5-6 |
| 21 | |
Wolverines | 1-2-6 | def. | |
Badgers | 3-4-5 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
University of Michigan | Wolverines | August Sturm '20 Colton Gerber '18 Mason Wolters '18 |
Sofia Gambini '18 Sidney Thompson '18 Michael Gapuz '18 |
|
University of Wisconsin | Badgers | Charles Bocklet '18 Charlie Kutschenreuter '19 Noah Janssen '19 |
Maggie Houtz '19 Olivia Staruck '20 Brianna Probst '20 |
|
University of Wisconsin Milwaukee | Panthers | Alex Keck '21 Jack Behrend '20 Parker Trepton '18 |
Simon Wolfrum '21 Susie Voltz '19 Brett Hutchinson '19 |
|
University of Minnesota | Gophers | Cailin Oakes '18 Carl Eaton '21 Michael Stone '19 Michael Valverde '18 |
Bailey Adkins '19 Cailin Oakes '18 Cassandra Trask '20 Kristin Berg '20 |
|
Northwestern University | Wildcats | Mark Davies '20 Noah Rosenthal '18 Ryder Easterlin '20 |
Audrey Debruine '19 Olivia Martins '20 Susan Riley '17 Zachary Herron '18 |
|
Marquette University | Golden Eagles | Bobby Sessions '20 Mason Chrabaszcz '18 Ryan Clulo '18 |
Karaline Black '20 Lindsey Clark '20 Anne Clark '19 |
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 ±0.09 means a rating gap counts about 8% 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.
Short write-ups of what the ratings and results show, with the numbers and how they were computed.
