| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
University of Michigan | Wolverines | 4–0 |
| 2 |  |
Northwestern University | Wildcats | 1–3 |
| 3 |  |
University of Wisconsin | Badgers | 1–3 |
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 17,727 team race matches sailed before 2022-04-02, using the line-ups that actually raced. It called 5 of 6 matches (83%) with a log-loss of 0.374 (a coin flip scores 0.693), so the event went well for the forecast. 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 | University of Michigan Wolverines | 1 | 5.92 school −0.05 | Christiana Scheibner / Caroline Grin Jenna Probst / Asha Baddi 2.47 Emily Pytell / Lily Allen | 82% | 4–0 (100%) | +0.7 |
| 2 | Northwestern University Wildcats | 3 | 4.16 school −0.20, home +0.33 | Lillian Nemeth / Eva Bambakidis 1.97 Marina Hutzler / Rachel Shapiro 1.49 Anna Kovacs / Aviva Kaplan | 26% | 1–3 (25%) | −0.0 |
| 3 | University of Wisconsin Badgers | 2 | 4.67 school −0.32 | Hailey Feinzig / Kate Thickens 2.00 Katherine Higgins / Mary Daniels Marissa Tegeder / Elisabeth Johanson 1.38 | 42% | 1–3 (25%) | −0.7 |
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 | 5.92 | 8.92 | 4–0 | 3.3 | ≥+3.00 | 1.1 | 45% |
| 2 | Northwestern University | 4.16 | 4.10 | 1–3 | 1.0 | −0.06 | 0.0 | 72% |
| 3 | University of Wisconsin | 4.67 | 3.76 | 1–3 | 1.7 | −0.91 | 0.4 | 91% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Marina Hutzler | Northwestern University | 1.49 | 1.44 | 1–3 | 1.0 | −0.06 | 0.0 | 72% |
| Anna Kovacs | Northwestern University | 0.47 | 0.41 | 1–3 | 1.0 | −0.06 | 0.0 | 72% |
| Katherine Higgins | University of Wisconsin | 1.51 | 0.60 | 1–3 | 1.7 | −0.91 | 0.4 | 91% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | Wolverines | Wildcats | Badgers | |
|---|---|---|---|---|---|
| University of Michigan Wolverines | 2–0 | ✕ | W | W |
| Northwestern University Wildcats | 1–1 | L | ✕ | W |
| University of Wisconsin Badgers | 0–2 | L | L | ✕ |
| Team | Rec. | Wolverines | Wildcats | Badgers | |
|---|---|---|---|---|---|
| University of Michigan Wolverines | 2–0 | ✕ | W | W |
| Northwestern University Wildcats | 0–2 | L | ✕ | L |
| University of Wisconsin Badgers | 1–1 | L | W | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Wolverines | 1-2-5 | def. | |
Wildcats | 3-4-6 |
| 2 | |
Wolverines | 1-2-3 | def. | |
Badgers | 4-5-6 |
| 3 | |
Wildcats | 1-2-3 | def. | |
Badgers | 4-5-6 |
| 4 | |
Wolverines | 1-2-4 | def. | |
Wildcats | 3-5-6 |
| 5 | |
Wolverines | 1-2-3 | def. | |
Badgers | 4-5-6 |
| 6 | |
Badgers | 1-4-5 | def. | |
Wildcats | 2-3-6 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
University of Michigan | Wolverines | Christiana Scheibner '23 Emily Pytell '25 Jenna Probst '22 |
Caroline Grin '25 Lily Allen '23 Asha Baddi '24 |
|
Northwestern University | Wildcats | Anna Kovacs '25 Lillian Nemeth '22 Marina Hutzler '23 |
Aviva Kaplan '24 Eva Bambakidis '22 Rachel Shapiro '22 |
|
University of Wisconsin | Badgers | Hailey Feinzig '22 Katherine Higgins '22 Marissa Tegeder '25 |
Kate Thickens '23 Mary Daniels '24 Elisabeth Johanson '22 |
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 ±0.14 means a rating gap counts about 12% 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.
