| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
University of Michigan | Wolverines | 7–0 |
| 2 |  |
Northwestern University | Wildcats | 0–7 |
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 30,300 team race matches sailed before 2026-04-03, using the line-ups that actually raced. It called 7 of 7 matches (100%) with a log-loss of 0.077 (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 | 4.80 school +0.09, home +0.35 | Katherine Simcoe / Tilden Coate 1.28 Jillian Giordano / Megan Gottfried 1.19 Lila Torresen / Sabrina Maas 1.85 | 93% | 7–0 (100%) | +0.5 |
| 2 | Northwestern University Wildcats | 2 | 2.28 | Grace Bray / Maddie Fitzgerald 1.56 Samantha Forgosh / Charlie Meyers 0.54 Marguerite Eno / Kate Virsik 0.02 | 7% | 0–7 (0%) | −0.5 |
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 | 4.80 | 7.80 | 7–0 | 6.5 | ≥+3.00 | 0.7 | 58% |
| 2 | Northwestern University | 2.28 | -0.72 | 0–7 | 0.5 | ≤−3.00 | 0.7 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Katherine Simcoe | University of Michigan | 1.28 | 4.28 | 7–0 | 6.5 | ≥+3.00 | 0.7 | 58% |
| Jillian Giordano | University of Michigan | 1.19 | 4.19 | 7–0 | 6.5 | ≥+3.00 | 0.7 | 58% |
| Lila Torresen | University of Michigan | 1.85 | 4.85 | 7–0 | 6.5 | ≥+3.00 | 0.7 | 58% |
| Grace Bray | Northwestern University | 1.56 | -1.44 | 0–7 | 0.5 | ≤−3.00 | 0.7 | >99% |
| Samantha Forgosh | Northwestern University | 0.54 | -2.46 | 0–7 | 0.5 | ≤−3.00 | 0.7 | >99% |
| Marguerite Eno | Northwestern University | 0.02 | -2.98 | 0–7 | 0.5 | ≤−3.00 | 0.7 | >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 | Wildcats | |
|---|---|---|---|---|
| University of Michigan Wolverines | 1–0 | ✕ | W |
| Northwestern University Wildcats | 0–1 | L | ✕ |
| Team | Rec. | Wolverines | Wildcats | |
|---|---|---|---|---|
| University of Michigan Wolverines | 1–0 | ✕ | W |
| Northwestern University Wildcats | 0–1 | L | ✕ |
| Team | Rec. | Wolverines | Wildcats | |
|---|---|---|---|---|
| University of Michigan Wolverines | 1–0 | ✕ | W |
| Northwestern University Wildcats | 0–1 | L | ✕ |
| Team | Rec. | Wolverines | Wildcats | |
|---|---|---|---|---|
| University of Michigan Wolverines | 1–0 | ✕ | W |
| Northwestern University Wildcats | 0–1 | L | ✕ |
| Team | Rec. | Wolverines | Wildcats | |
|---|---|---|---|---|
| University of Michigan Wolverines | 1–0 | ✕ | W |
| Northwestern University Wildcats | 0–1 | L | ✕ |
| Team | Rec. | Wolverines | Wildcats | |
|---|---|---|---|---|
| University of Michigan Wolverines | 1–0 | ✕ | W |
| Northwestern University Wildcats | 0–1 | L | ✕ |
| Team | Rec. | Wolverines | Wildcats | |
|---|---|---|---|---|
| University of Michigan Wolverines | 1–0 | ✕ | W |
| Northwestern University Wildcats | 0–1 | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Wolverines | 1-2-3 | def. | |
Wildcats | 4-5-6 |
| 2 | |
Wolverines | 2-3-4 | def. | |
Wildcats | 1-5-6 |
| 3 | |
Wolverines | 2-3-4 | def. | |
Wildcats | 1-5-6 |
| 4 | |
Wolverines | 1-3-4 | def. | |
Wildcats | 2-5-6 |
| 5 | |
Wolverines | 2-3-4 | def. | |
Wildcats | 1-5-6 |
| 6 | |
Wolverines | 2-3-4 | def. | |
Wildcats | 1-5-6 |
| 7 | |
Wolverines | 1-2-5 | def. | |
Wildcats | 3-4-6 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
University of Michigan | Wolverines | Jillian Giordano '27 Katherine Simcoe '26 Lila Torresen '29 |
Megan Gottfried '27 Tilden Coate '29 Sabrina Maas '27 |
|
Northwestern University | Wildcats | Grace Bray '27 Marguerite Eno '27 Samantha Forgosh '26 |
Maddie Fitzgerald '26 Kate Virsik '26 Charlie Meyers '29 |
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.
