| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
University of Texas | Longhorns | 5–0 |
| 2 |  |
University of North Texas | Mean Green | 2–3 |
| 3 |  |
Texas A&M University at Corpus Christi | Islanders | 3–2 |
| 4 |  |
Texas A&M University | Aggies | 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 16,158 team race matches sailed before 2022-03-05, using the line-ups that actually raced. It called 3 of 3 matches (100%) with a log-loss of 0.187 (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 Texas Longhorns | 1 | 4.95 home +0.35 | Ethan Froelich / Lillian Egan 2.16 Karina Bertelsmann / Sophia Herrada 1.69 Thomas Norman / Etienne Black | 83% | 3–0 (100%) | +0.5 |
| 2 | University of North Texas Mean Green | 2 | 3.37 school +0.21 | Taylor Snyder / Timothy Verriere Jamie Weston / Rohit Rajan Dutch Byerly / Gavin Garcia | 17% | 0–3 (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 Texas | 4.95 | 7.95 | 3–0 | 2.5 | ≥+3.00 | 0.8 | 57% |
| 2 | University of North Texas | 3.37 | 0.37 | 0–3 | 0.5 | ≤−3.00 | 0.8 | >99% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Karina Bertelsmann | University of Texas | 1.69 | 4.69 | 3–0 | 2.5 | ≥+3.00 | 0.8 | 57% |
| Thomas Norman | University of Texas | 0.75 | 3.75 | 3–0 | 2.5 | ≥+3.00 | 0.8 | 57% |
| Taylor Snyder | University of North Texas | 0.49 | -2.51 | 0–3 | 0.5 | ≤−3.00 | 0.8 | >99% |
| Jamie Weston | University of North Texas | 0.16 | -2.84 | 0–3 | 0.5 | ≤−3.00 | 0.8 | >99% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Rd | # | Winner | Loser | P(winner) | |
|---|---|---|---|---|---|
| 1 | 3 | University of Texas | University of North Texas | 84% | |
| 1 | 9 | University of Texas | University of North Texas | 83% | |
| 2 | 10 | University of Texas | University of North Texas | 82% |
| Team | Rec. | Longhorns | Green | Islanders | Aggies | |
|---|---|---|---|---|---|---|
| University of Texas Longhorns | 4–0 | ✕ | W | W | W |
| University of North Texas Mean Green | 2–2 | L | ✕ | W | W |
| Texas A&M University at Corpus Christi Islanders | 2–2 | L | L | ✕ | W |
| Texas A&M University Aggies | 0–4 | L | L | L | ✕ |
| Team | Rec. | Longhorns | Green | Islanders | Aggies | |
|---|---|---|---|---|---|---|
| University of Texas Longhorns | 1–0 | ✕ | W | – | – |
| University of North Texas Mean Green | 0–1 | L | ✕ | – | – |
| Texas A&M University at Corpus Christi Islanders | 1–0 | – | – | ✕ | W |
| Texas A&M University Aggies | 0–1 | – | – | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Longhorns | 1-2-4 | def. | |
Islanders | 3-5-6 |
| 2 | |
Longhorns | 1-2-3 | def. | |
Aggies | 4-5-6 |
| 3 | |
Longhorns | 1-2-4 | def. | |
Mean Green | 3-5-6 DNF |
| 4 | |
Mean Green | 2-3-4 | def. | |
Islanders | 1-5-6 |
| 5 | |
Mean Green | 1-2-3 | def. | |
Aggies | 4-5-6 |
| 6 | |
Islanders | 1-2-4 | def. | |
Aggies | 3-5-6 |
| 7 | |
Islanders | 1-2-3 | def. | |
Aggies | 4-5-6 |
| 9 | |
Longhorns | 1-2-4 | def. | |
Mean Green | 3-5-6 |
| 8 | |
Islanders | 1-2-3 | def. | |
Aggies | 4-5-6 |
| 10 | |
Longhorns | 1-2-4 | def. | |
Mean Green | 3-5-6 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
University of Texas | Longhorns | Ethan Froelich '24 Karina Bertelsmann '22 Reilly Linn '23 Thomas Norman '23 |
Sophia Herrada '24 Lillian Egan '23 Warren Wegener '23 Etienne Black '23 |
|
University of North Texas | Mean Green | Dutch Byerly '24 Jamie Weston '22 Taylor Snyder '24 |
Gavin Garcia '24 Rohit Rajan '26 Timothy Verriere '24 |
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 ±0.07 means a rating gap counts about 6% 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.
