| # | School | Team | Rec. | |
|---|---|---|---|---|
| 1 |  |
University of North Texas | Mean Green | 7–1 |
| 2 |  |
University of Texas | Longhorns | 5–3 |
| 3 |  |
Texas A&M University | Aggies | 3–5 |
| 4 |  |
Texas A&M University at Galveston | Aggies | 3–5 |
| 5 |  |
Texas A&M University at Corpus Christi | Islanders | 0–8 |
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 18,964 team race matches sailed before 2023-02-25, using the line-ups that actually raced. It called 10 of 12 matches (83%) with a log-loss of 0.284 (a coin flip scores 0.693), so the event went well for the forecast. Orderliness +0.39 ± 0.51: a typical event, more orderly than 84% 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 North Texas Mean Green | 2 | 4.15 school +0.42 | Dutch Byerly / Alana Babilonia 2.46 Timothy Verriere / Dennis Kosthujin 0.21 Taylor Snyder / Rahul Shaw 0.98 | 78% | 5–1 (83%) | +0.3 |
| 2 | University of Texas Longhorns | 1 | 4.18 home +0.38 | Ethan Froelich / Sophia Herrada 2.31 Etienne Black / Rohit Rajan 0.43 Reilly Linn / Leigha Forrest | 79% | 5–1 (83%) | +0.2 |
| 3 | Texas A&M University Aggies | 3 | 1.63 school −0.07 | Kate Hennig / Emily Davison 1.02 Cole Broberg / Jonathan Zhou Elliot Mcmahon / Andrew Pool | 23% | 1–5 (17%) | −0.4 |
| 4 | Texas A&M University at Galveston Aggies | 4 | 1.45 school −0.21 | Jacob Granberry / Brett Pearson 0.73 Isaac Barkley / Joshua Richie Jason Elliott / Emma Stodghill | 19% | 1–5 (17%) | −0.1 |
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 North Texas | 4.15 | 4.58 | 5–1 | 4.7 | +0.43 | 0.1 | 61% |
| 2 | University of Texas | 4.18 | 4.52 | 5–1 | 4.8 | +0.33 | 0.1 | 64% |
| 4 | Texas A&M University at Galveston | 1.45 | 1.24 | 1–5 | 1.2 | −0.22 | 0.0 | 77% |
| 3 | Texas A&M University | 1.63 | 1.08 | 1–5 | 1.4 | −0.55 | 0.2 | 85% |
| Skipper | School | Value | Perf. | Record | Exp. wins | Δ | Surprise (bits) | P(this good or better) |
|---|---|---|---|---|---|---|---|---|
| Timothy Verriere | University of North Texas | 0.21 | 0.64 | 5–1 | 4.7 | +0.43 | 0.1 | 61% |
| Taylor Snyder | University of North Texas | 0.98 | 1.41 | 5–1 | 4.7 | +0.43 | 0.1 | 61% |
| Etienne Black | University of Texas | 0.43 | 0.76 | 5–1 | 4.8 | +0.33 | 0.1 | 64% |
| Reilly Linn | University of Texas | 0.95 | 1.29 | 5–1 | 4.8 | +0.33 | 0.1 | 64% |
| Jacob Granberry | Texas A&M University at Galveston | 0.73 | 0.52 | 1–5 | 1.2 | −0.22 | 0.0 | 77% |
| Isaac Barkley | Texas A&M University at Galveston | 0.50 | 0.28 | 1–5 | 1.2 | −0.22 | 0.0 | 77% |
| Jason Elliott | Texas A&M University at Galveston | 0.12 | -0.10 | 1–5 | 1.2 | −0.22 | 0.0 | 77% |
| Cole Broberg | Texas A&M University | 0.31 | -0.24 | 1–5 | 1.4 | −0.55 | 0.2 | 85% |
| Elliot Mcmahon | Texas A&M University | 0.09 | -0.46 | 1–5 | 1.4 | −0.55 | 0.2 | 85% |
Probability the model gave the side that went on to win. Red bars are upsets (the winner was given under 50%).
| Team | Rec. | Green | Longhorns | Aggies | Aggies | Islanders | |
|---|---|---|---|---|---|---|---|
| University of North Texas Mean Green | 3–1 | ✕ | L | W | W | W |
| University of Texas Longhorns | 4–0 | W | ✕ | W | W | W |
| Texas A&M University Aggies | 2–2 | L | L | ✕ | W | W |
| Texas A&M University at Galveston Aggies | 1–3 | L | L | L | ✕ | W |
| Texas A&M University at Corpus Christi Islanders | 0–4 | L | L | L | L | ✕ |
| Team | Rec. | Green | Longhorns | Aggies | Aggies | Islanders | |
|---|---|---|---|---|---|---|---|
| University of North Texas Mean Green | 4–0 | ✕ | W | W | W | W |
| University of Texas Longhorns | 3–1 | L | ✕ | W | W | W |
| Texas A&M University Aggies | 1–3 | L | L | ✕ | L | W |
| Texas A&M University at Galveston Aggies | 2–2 | L | L | W | ✕ | W |
| Texas A&M University at Corpus Christi Islanders | 0–4 | L | L | L | L | ✕ |
| # | Winner | Loser | Pos. | ||||
|---|---|---|---|---|---|---|---|
| 1 | |
Longhorns | 1-2-3 | def. | |
Islanders | 4-5-6 |
| 2 | |
Mean Green | 1-2-6 | def. | |
Aggies | 3-4-5 |
| 3 | |
Longhorns | 1-2-3 | def. | |
Aggies | 4-5-6 |
| 4 | |
Mean Green | 1-2-3 | def. | |
Islanders | 4-5-6 |
| 5 | |
Aggies | 1-2-5 | def. | |
Aggies | 3-4-6 |
| 6 | |
Longhorns | 1-3-4 | def. | |
Mean Green | 2-5-6 |
| 7 | |
Aggies | 1-2-3 | def. | |
Islanders | 4-5-6 |
| 8 | |
Mean Green | 1-2-4 | def. | |
Aggies | 3-5-6 |
| 9 | |
Longhorns | 1-2-3 | def. | |
Aggies | 4-5-6 |
| 10 | |
Aggies | 1-2-3 | def. | |
Islanders | 4-5-6 |
| 11 | |
Mean Green | 1-2-3 | def. | |
Aggies | 4-5-6 |
| 12 | |
Longhorns | 1-2-3 | def. | |
Aggies | 4-5-6 |
| 13 | |
Mean Green | 1-2-3 | def. | |
Islanders | 4-5-6 |
| 14 | |
Longhorns | 1-2-3 | def. | |
Aggies | 4-5-6 |
| 15 | |
Aggies | 1-2-3 | def. | |
Islanders | 4-5-6 |
| 16 | |
Mean Green | 1-2-5 DNF | def. | |
Longhorns | 3-4-6 DNF |
| 17 | |
Aggies | 1-3-6 | def. | |
Aggies | 2-4-5 |
| 18 | |
Longhorns | 1-2-3 | def. | |
Islanders | 4-5-6 |
| 19 | |
Mean Green | 1-2-3 | def. | |
Aggies | 4-5-6 |
| 20 | |
Aggies | 1-2-3 | def. | |
Islanders | 4-5-6 |
| School | Team | Skippers | Crew | |
|---|---|---|---|---|
|
University of North Texas | Mean Green | Dutch Byerly '24 Taylor Snyder '24 Timothy Verriere '24 |
Alana Babilonia '25 Rahul Shaw '26 Dennis Kosthujin '26 |
|
University of Texas | Longhorns | Ethan Froelich '24 Etienne Black '23 Reilly Linn '23 |
Sophia Herrada '24 Rohit Rajan '26 Leigha Forrest '24 |
|
Texas A&M University | Aggies | Cole Broberg '25 Elliot Mcmahon '23 Kate Hennig '26 |
Jonathan Zhou '23 Andrew Pool '23 Emily Davison '24 |
|
Texas A&M University at Galveston | Aggies | Isaac Barkley '26 Jacob Granberry '26 Jason Elliott '25 |
Joshua Richie '26 Brett Pearson '26 Emma Stodghill '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.
