(8) #229 Baylor (10-10)

1019.47 (280)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
207 Texas-B Win 9-8 7.77 327 3.85% Counts Feb 10th Big D in Little D 2024
197 Texas State Win 10-9 9.99 415 4.07% Counts Feb 10th Big D in Little D 2024
380 Texas-Arlington** Win 13-2 0 321 0% Ignored (Why) Feb 10th Big D in Little D 2024
266 Texas Tech Win 11-10 -0.25 334 4.07% Counts Feb 10th Big D in Little D 2024
128 Houston Loss 9-10 13.18 281 5.13% Counts Mar 9th Centex Tier 2 2024
211 San Diego State Loss 5-7 -11.36 309 4.07% Counts Mar 9th Centex Tier 2 2024
190 Texas-Dallas Win 9-8 13.45 400 4.85% Counts Mar 9th Centex Tier 2 2024
207 Texas-B Loss 10-13 -13.99 327 5.13% Counts Mar 10th Centex Tier 2 2024
281 Trinity Loss 10-15 -34.91 323 5.13% Counts Mar 10th Centex Tier 2 2024
73 Ave Maria Loss 4-13 -0.29 255 5.75% Counts (Why) Mar 23rd Huckfest 2024
266 Texas Tech Win 10-7 14.9 334 5.44% Counts Mar 23rd Huckfest 2024
258 North Texas Win 8-6 10.3 263 4.94% Counts Mar 23rd Huckfest 2024
380 Texas-Arlington Win 13-6 -7.26 321 5.75% Counts (Why) Mar 23rd Huckfest 2024
295 Texas A&M-B Win 11-4 17.67 385 5.28% Counts (Why) Mar 24th Huckfest 2024
109 Tarleton State Loss 5-14 -9.27 181 5.75% Counts (Why) Mar 24th Huckfest 2024
190 Texas-Dallas Win 8-6 22.83 400 4.94% Counts Mar 24th Huckfest 2024
190 Texas-Dallas Loss 8-11 -16.66 400 6.84% Counts Apr 13th North Texas D I Mens Conferences 2024
266 Texas Tech Loss 7-8 -16.56 334 6.08% Counts Apr 13th North Texas D I Mens Conferences 2024
109 Tarleton State Loss 9-11 14.61 181 6.84% Counts Apr 13th North Texas D I Mens Conferences 2024
258 North Texas Loss 7-8 -14.72 263 6.08% 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.