| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
University of Wisconsin | Badgers | 11–1 |
| 2 |  |
University of Michigan | Wolverines | 7–5 |
| 3 |  |
Indiana University | Hoosiers | 7–5 |
| 4 |  |
University of Notre Dame | Fighting Irish | 5–7 |
| 5 |  |
Northwestern University | Wildcats | 0–12 |
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 21,440 team race matches sailed before 2023-04-22, using the line-ups that actually raced. It called 26 of 30 matches (87%) with a log-loss of 0.388 (a coin flip scores 0.693), so the event went well for the forecast. Orderliness +0.62 ± 0.40: an orderly event, more orderly than 97% 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 Wisconsin | 3.85 | 5.50 | 11–1 | 8.4 | +1.65 | 2.6 | 7% |
| 2 | University of Michigan | 3.21 | 3.43 | 7–5 | 6.5 | +0.22 | 0.1 | 49% |
| 3 | Indiana University | 3.62 | 3.53 | 7–5 | 7.2 | −0.08 | 0.0 | 67% |
| 4 | University of Notre Dame | 3.54 | 2.70 | 5–7 | 7.0 | −0.84 | 1.2 | 95% |
| 5 | Northwestern University | 0.92 | -2.08 | 0–12 | 0.9 | ≤−3.00 | 1.3 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Phineas Tait | University of Wisconsin | 0.78 | 2.43 | 11–1 | 8.4 | +1.65 | 2.6 | 7% |
| Adrian Stone | University of Michigan | 1.23 | 1.88 | 4–2 | 3.1 | +0.65 | 0.4 | 36% |
| Katherine Simcoe | University of Michigan | 0.58 | 0.80 | 7–5 | 6.5 | +0.22 | 0.1 | 49% |
| Nithya Balachander | Indiana University | 1.13 | 1.05 | 7–5 | 7.2 | −0.08 | 0.0 | 67% |
| Daniel De La Vega Iii | Indiana University | 0.67 | 0.58 | 7–5 | 7.2 | −0.08 | 0.0 | 67% |
| Joseph Thuente | University of Notre Dame | 1.55 | 0.70 | 5–7 | 7.0 | −0.84 | 1.2 | 95% |
| John Szymanski | University of Notre Dame | 1.39 | 0.55 | 5–7 | 7.0 | −0.84 | 1.2 | 95% |
| Gabriel Abbott | Northwestern University | 0.30 | -2.70 | 0–12 | 0.9 | ≤−3.00 | 1.3 | >99% |
| Cole Abbott | Northwestern University | 0.45 | -2.55 | 0–12 | 0.9 | ≤−3.00 | 1.3 | >99% |
| Prabhav Jain | Northwestern University | -0.39 | -3.39 | 0–12 | 0.9 | ≤−3.00 | 1.3 | >99% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | Badgers | Wolverines | Hoosiers | Irish | Wildcats | |
|---|---|---|---|---|---|---|---|
| University of Wisconsin Badgers | 4–0 | ✕ | W | W | W | W |
| University of Michigan Wolverines | 3–1 | L | ✕ | W | W | W |
| Indiana University Hoosiers | 2–2 | L | L | ✕ | W | W |
| University of Notre Dame Fighting Irish | 1–3 | L | L | L | ✕ | W |
| Northwestern University Wildcats | 0–4 | L | L | L | L | ✕ |
| Team | Rec. | Badgers | Wolverines | Hoosiers | Irish | Wildcats | |
|---|---|---|---|---|---|---|---|
| University of Wisconsin Badgers | 3–1 | ✕ | W | W | L | W |
| University of Michigan Wolverines | 3–1 | L | ✕ | W | W | W |
| Indiana University Hoosiers | 2–2 | L | L | ✕ | W | W |
| University of Notre Dame Fighting Irish | 2–2 | W | L | L | ✕ | W |
| Northwestern University Wildcats | 0–4 | L | L | L | L | ✕ |
| Team | Rec. | Badgers | Wolverines | Hoosiers | Irish | Wildcats | |
|---|---|---|---|---|---|---|---|
| University of Wisconsin Badgers | 4–0 | ✕ | W | W | W | W |
| University of Michigan Wolverines | 1–3 | L | ✕ | L | L | W |
| Indiana University Hoosiers | 3–1 | L | W | ✕ | W | W |
| University of Notre Dame Fighting Irish | 2–2 | L | W | L | ✕ | W |
| Northwestern University Wildcats | 0–4 | L | L | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Wolverines | 1-2-4 | def. | |
Wildcats | 3-5-6 |
| 2 | |
Hoosiers | 1-2-6 | def. | |
Fighting Irish | 3-4-5 |
| 3 | |
Badgers | 1-3-6 | def. | |
Wolverines | 2-4-5 |
| 4 | |
Hoosiers | 1-2-5 | def. | |
Wildcats | 3-4-6 |
| 5 | |
Badgers | 1-2-4 | def. | |
Fighting Irish | 3-5-6 DNF |
| 6 | |
Wolverines | 1-2-3 | def. | |
Hoosiers | 4-5-6 |
| 7 | |
Fighting Irish | 1-2-3 | def. | |
Wildcats | 4-5-6 |
| 8 | |
Badgers | 1-2-4 | def. | |
Hoosiers | 3-5-6 |
| 9 | |
Wolverines | 2-3-5 | def. | |
Fighting Irish | 1-4-6 |
| 10 | |
Badgers | 1-2-4 | def. | |
Wildcats | 3-5-6 |
| 11 | |
Hoosiers | 1-3-5 | def. | |
Fighting Irish | 2-4-6 |
| 12 | |
Badgers | 1-2-3 | def. | |
Wildcats | 4-5-6 |
| 13 | |
Wolverines | 1-4-5 | def. | |
Fighting Irish | 2-3-6 |
| 14 | |
Hoosiers | 1-2-4 | def. | |
Wildcats | 3-5-6 |
| 15 | |
Badgers | 1-2-5 | def. | |
Wolverines | 3-4-6 |
| 16 | |
Fighting Irish | 1-2-3 | def. | |
Wildcats | 4-5-6 |
| 17 | |
Badgers | 1-2-6 | def. | |
Hoosiers | 3-4-5 |
| 18 | |
Wolverines | 1-2-3 | def. | |
Wildcats | 4-5-6 |
| 19 | |
Fighting Irish | 1-3-4 | def. | |
Badgers | 2-5-6 |
| 20 | |
Wolverines | 1-3-5 | def. | |
Hoosiers | 2-4-6 |
| 21 | |
Badgers | 1-2-3 | def. | |
Wolverines | 4-5-6 |
| 22 | |
Hoosiers | 1-4-5 | def. | |
Fighting Irish | 2-3-6 |
| 23 | |
Wolverines | 1-2-3 | def. | |
Wildcats | 4-5-6 |
| 24 | |
Badgers | 1-2-3 | def. | |
Hoosiers | 4-5-6 |
| 25 | |
Fighting Irish | 1-2-6 | def. | |
Wildcats | 3-4-5 |
| 26 | |
Hoosiers | 1-2-4 | def. | |
Wolverines | 3-5-6 |
| 27 | |
Badgers | 1-2-3 | def. | |
Fighting Irish | 4-5-6 |
| 28 | |
Hoosiers | 1-2-3 | def. | |
Wildcats | 4-5-6 |
| 29 | |
Fighting Irish | 1-2-5 | def. | |
Wolverines | 3-4-6 |
| 30 | |
Badgers | 1-2-3 | def. | |
Wildcats | 4-5-6 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
University of Wisconsin | Badgers | Calvin Lutton '26 Phineas Tait '25 Seamus Hendrickson '25 |
Maeve Katics '26 Maddy Netzer '26 Kerstin Hyer '25 |
|
University of Michigan | Wolverines | Adrian Stone '25 Jack Hammett '26 Katherine Simcoe '26 Luciana Solorzano '24 |
Alden Gort '26 Bryson Denuyl '24 Abigail Baker '25 Anna Howell '26 |
|
Indiana University | Hoosiers | Daniel De La Vega Iii '25 John Hultquist '23 Nithya Balachander '25 |
William Beckman '25 Alex Tigges '26 Fiona Naughton '26 Delaynie Grove '24 |
|
University of Notre Dame | Fighting Irish | Jack O'Connor '26 John Szymanski '25 Joseph Thuente '24 |
Theresa Villa '23 Kate Norman '26 Carmela D’Antuono '24 |
|
Northwestern University | Wildcats | Cole Abbott '26 Gabriel Abbott '23 Prabhav Jain '23 |
Sebastian Myhe '25 Millie Rose Taub '26 Brandon Chang '23 Tara Chen '25 |
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.
