| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 | University of Michigan | Blue | 10–2 | |
| 2 | University of Notre Dame | Fighting Irish | 9–3 | |
| 3 | University of Saint Thomas | Tommies | 4–8 | |
| 4 | University of Michigan | Maize | 1–11 |
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 26,645 team race matches sailed before 2025-03-29, using the line-ups that actually raced. It called 21 of 24 matches (88%) with a log-loss of 0.403 (a coin flip scores 0.693), so the event went well for the forecast. Orderliness +0.38 ± 0.40: a typical event, more orderly than 83% 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 | University of Michigan Blue | 1 | 5.11 home +0.36 | Billy Vogel / Alexander Reynolds 1.66 Jack Hammett / Abigail Paul 1.47 Jack Homa / Sabrina Maas 1.47 | 72% | 10–2 (83%) | +1.3 |
| 2 | University of Notre Dame Fighting Irish | 2 | 4.89 school −0.19 | Timothy Hesse / Kate Norman 1.98 Trevor Bolger / John Sullivan John Szymanski / Jack O'Connor 1.64 | 66% | 9–3 (75%) | +1.0 |
| 3 | University of Saint Thomas Tommies | 3 | 4.33 school −0.10 | Greg Bittle / Lily Lupardus 1.82 Rakesh Dhiman / Naomi Peters 0.85 Theodore Graham / Leila Montoya | 51% | 4–8 (33%) | −2.1 |
| 4 | University of Michigan Maize | 4 | 2.58 home +0.36 | Jillian Giordano / Ben Visco John Mccalmont / Ryan Mcallister 0.70 Katherine Simcoe / Gabe Lynch 0.91 | 10% | 1–11 (8%) | −0.2 |
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 Blue | 5.11 | 5.83 | 10–2 | 8.7 | +0.72 | 0.7 | 29% |
| 2 | University of Notre Dame Fighting Irish | 4.89 | 5.38 | 9–3 | 8.0 | +0.49 | 0.4 | 37% |
| 4 | University of Michigan Maize | 2.58 | 2.33 | 1–11 | 1.3 | −0.25 | 0.0 | 74% |
| 3 | University of Saint Thomas Tommies | 4.33 | 3.34 | 4–8 | 6.1 | −0.99 | 1.5 | 96% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Trevor Bolger | University of Notre Dame | 1.32 | 1.81 | 9–3 | 8.0 | +0.49 | 0.4 | 37% |
| John Szymanski | University of Notre Dame | 1.64 | 2.12 | 9–3 | 8.0 | +0.49 | 0.4 | 37% |
| John Mccalmont | University of Michigan | 0.70 | 0.45 | 1–11 | 1.3 | −0.25 | 0.0 | 74% |
| Katherine Simcoe | University of Michigan | 0.91 | 0.66 | 1–11 | 1.3 | −0.25 | 0.0 | 74% |
| Theodore Graham | University of Saint Thomas | 1.48 | 0.49 | 4–8 | 6.1 | −0.99 | 1.5 | 96% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | Blue | Irish | Tommies | Maize | |
|---|---|---|---|---|---|---|
| University of Michigan Blue | 2–4 | ✕ | L | L | ✕ | |
| University of Notre Dame Fighting Irish | 3–0 | W | ✕ | W | W | |
| University of Saint Thomas Tommies | 1–2 | W | L | ✕ | W | |
| University of Michigan Maize | 2–4 | ✕ | L | L | ✕ |
| Team | Rec. | Blue | Irish | Tommies | Maize | |
|---|---|---|---|---|---|---|
| University of Michigan Blue | 3–3 | ✕ | L | W | ✕ | |
| University of Notre Dame Fighting Irish | 2–1 | W | ✕ | W | W | |
| University of Saint Thomas Tommies | 1–2 | L | L | ✕ | L | |
| University of Michigan Maize | 3–3 | ✕ | L | W | ✕ |
| Team | Rec. | Blue | Irish | Tommies | Maize | |
|---|---|---|---|---|---|---|
| University of Michigan Blue | 3–3 | ✕ | W | L | ✕ | |
| University of Notre Dame Fighting Irish | 2–1 | L | ✕ | W | L | |
| University of Saint Thomas Tommies | 1–2 | W | L | ✕ | W | |
| University of Michigan Maize | 3–3 | ✕ | W | L | ✕ |
| Team | Rec. | Blue | Irish | Tommies | Maize | |
|---|---|---|---|---|---|---|
| University of Michigan Blue | 3–3 | ✕ | L | L | ✕ | |
| University of Notre Dame Fighting Irish | 2–1 | W | ✕ | W | W | |
| University of Saint Thomas Tommies | 1–2 | W | L | ✕ | W | |
| University of Michigan Maize | 3–3 | ✕ | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | Fighting Irish | 1-2-4 | def. | Maize | 3-5-6 | ||
| 2 | Blue | 1-2-4 | def. | Tommies | 3-5-6 | ||
| 3 | Tommies | 1-2-4 | def. | Maize | 3-5-6 | ||
| 4 | Fighting Irish | 1-2-6 | def. | Blue | 3-4-5 | ||
| 5 | Fighting Irish | 1-2-6 | def. | Tommies | 3-4-5 | ||
| 6 | Blue | 1-2-3 | def. | Maize | 4-5-6 | ||
| 7 | Blue | 1-2-3 | def. | Maize | 4-5-6 | ||
| 8 | Fighting Irish | 1-2-5 | def. | Tommies | 3-4-6 | ||
| 9 | Blue | 2-3-5 | def. | Fighting Irish | 1-4-6 | ||
| 10 | Tommies | 1-3-4 | def. | Maize | 2-5-6 DSQ | ||
| 11 | Fighting Irish | 1-2-5 | def. | Maize | 3-4-6 | ||
| 12 | Blue | 1-2-5 | def. | Tommies | 3-4-6 | ||
| 13 | Blue | 1-2-3 | def. | Tommies | 4-5-6 | ||
| 14 | Fighting Irish | 1-2-4 | def. | Maize | 3-5-6 | ||
| 15 | Tommies | 2-3-4 | def. | Maize | 1-5-6 | ||
| 16 | Blue | 2-3-4 | def. | Fighting Irish | 1-5-6 | ||
| 17 | Blue | 1-2-3 | def. | Maize | 4-5-6 | ||
| 18 | Fighting Irish | 1-2-6 | def. | Tommies | 3-4-5 | ||
| 19 | Fighting Irish | 1-2-5 | def. | Tommies | 3-4-6 DSQ | ||
| 20 | Blue | 1-3-5 | def. | Maize | 2-4-6 | ||
| 21 | Blue | 1-3-6 | def. | Fighting Irish | 2-4-5 | ||
| 22 | Maize | 1-2-3 | def. | Tommies | 4-5-6 | ||
| 23 | Tommies | 1-3-6 | def. | Blue | 2-4-5 | ||
| 24 | Fighting Irish | 1-2-4 | def. | Maize | 3-5-6 | ||
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
| University of Michigan | Blue | Billy Vogel '25 Jack Hammett '26 Jack Homa '27 |
Alexander Reynolds '28 Abigail Paul '25 Sabrina Maas '27 |
|
| University of Notre Dame | Fighting Irish | John Szymanski '25 Timothy Hesse '27 Trevor Bolger '26 |
Jack O'Connor '26 Kate Norman '26 John Sullivan '26 |
|
| University of Saint Thomas | Tommies | Greg Bittle '25 Rakesh Dhiman '26 Theodore Graham '26 |
Lily Lupardus '27 Naomi Peters '26 Leila Montoya '28 |
|
| University of Michigan | Maize | Jillian Giordano '27 John Mccalmont '27 Katherine Simcoe '26 |
Ben Visco '28 Ryan Mcallister '26 Gabe Lynch '26 |
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 ±0.08 means a rating gap counts about 7% 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.