(5) #266 Texas Tech (7-13)

888.68 (334)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
229 Baylor Loss 10-11 0.27 280 4.47% Counts Feb 10th Big D in Little D 2024
197 Texas State Loss 12-13 5.44 415 4.47% Counts Feb 10th Big D in Little D 2024
380 Texas-Arlington Win 13-5 0.55 321 4.47% Counts (Why) Feb 10th Big D in Little D 2024
207 Texas-B Win 12-10 20.51 327 4.47% Counts Feb 10th Big D in Little D 2024
46 Florida** Loss 5-13 0 215 0% Ignored (Why) Feb 24th Mardi Gras XXXVI college
83 Indiana** Loss 3-13 0 311 0% Ignored (Why) Feb 24th Mardi Gras XXXVI college
197 Texas State Loss 5-13 -18.96 415 5.02% Counts (Why) Feb 24th Mardi Gras XXXVI college
116 LSU Loss 3-13 -3.04 346 5.02% Counts (Why) Feb 24th Mardi Gras XXXVI college
281 Trinity Loss 7-13 -32.73 323 5.02% Counts Feb 25th Mardi Gras XXXVI college
73 Ave Maria** Loss 2-13 0 255 0% Ignored (Why) Mar 23rd Huckfest 2024
229 Baylor Loss 7-10 -16.47 280 5.98% Counts Mar 23rd Huckfest 2024
258 North Texas Win 12-6 39.87 263 6.16% Counts (Why) Mar 23rd Huckfest 2024
380 Texas-Arlington Win 13-5 0.8 321 6.33% Counts (Why) Mar 23rd Huckfest 2024
295 Texas A&M-B Win 14-5 30.23 385 6.33% Counts (Why) Mar 24th Huckfest 2024
190 Texas-Dallas Loss 4-13 -22.31 400 6.33% Counts (Why) Mar 24th Huckfest 2024
229 Baylor Win 8-7 18.32 280 6.68% Counts Apr 13th North Texas D I Mens Conferences 2024
258 North Texas Win 8-7 10.99 263 6.68% Counts Apr 13th North Texas D I Mens Conferences 2024
109 Tarleton State Loss 7-15 -1.71 181 7.52% Counts (Why) Apr 13th North Texas D I Mens Conferences 2024
258 North Texas Loss 9-10 -7.85 263 7.52% Counts Apr 14th North Texas D I Mens Conferences 2024
190 Texas-Dallas Loss 8-15 -24.01 400 7.52% Counts Apr 14th North Texas D I Mens Conferences 2024
**Blowout Eligible. Learn more about how this works here.

FAQ

The results on this page ("USAU") are the results of an implementation of the USA Ultimate Top 20 algorithm, which is used to allocate post season bids to both colleg and club ultimate teams. The data was obtained by scraping USAU's score reporting website. Learn more about the algorithm here. TL;DR, here is the rating function. Every game a team plays gets a rating equal to the opponents rating +/- the score value. With all these data points, we iterate team ratings until convergence. There is also a rule for discounting blowout games (see next FAQ)
For reference, here is handy table with frequent game scrores and the resulting game value:
"...if a team is rated more than 600 points higher than its opponent, and wins with a score that is more than twice the losing score plus one, the game is ignored for ratings purposes. However, this is only done if the winning team has at least N other results that are not being ignored, where N=5."

Translation: if a team plays a game where even earning the max point win would hurt them, they can have the game ignored provided they win by enough and have suffficient unignored results.