(26) #236 Loyola-Chicago (8-11)

990.9 (445)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
222 Ball State Win 11-7 29.23 238 5.33% Counts Mar 23rd Butler Spring Fling
363 Indiana-B Win 11-8 -10.84 428 5.47% Counts Mar 23rd Butler Spring Fling
285 Penn State-Behrend Win 10-9 -3.79 471 5.47% Counts Mar 23rd Butler Spring Fling
280 Western Michigan Win 13-1 25.34 326 5.47% Counts (Why) Mar 23rd Butler Spring Fling
232 Butler Loss 4-10 -29.15 342 4.78% Counts (Why) Mar 24th Butler Spring Fling
220 Hillsdale Loss 8-13 -25.28 421 5.47% Counts Mar 24th Butler Spring Fling
173 Xavier Loss 2-13 -20.69 260 5.47% Counts (Why) Mar 24th Butler Spring Fling
39 Illinois** Loss 1-15 0 306 0% Ignored (Why) Apr 13th Illinois D I Mens Conferences 2024
324 Illinois-Chicago Win 14-8 11.7 6.51% Counts (Why) Apr 13th Illinois D I Mens Conferences 2024
106 Northwestern Loss 5-15 -7.52 147 6.51% Counts (Why) Apr 13th Illinois D I Mens Conferences 2024
145 Southern Illinois-Edwardsville Win 13-12 32.42 271 6.51% Counts Apr 13th Illinois D I Mens Conferences 2024
39 Illinois** Loss 5-15 0 306 0% Ignored (Why) Apr 14th Illinois D I Mens Conferences 2024
324 Illinois-Chicago Win 15-2 16.15 6.51% Counts (Why) Apr 14th Illinois D I Mens Conferences 2024
39 Illinois** Loss 5-12 0 306 0% Ignored (Why) Apr 27th Great Lakes D I College Mens Regionals 2024
118 Kentucky Loss 9-10 23.64 113 7.3% Counts Apr 27th Great Lakes D I College Mens Regionals 2024
145 Southern Illinois-Edwardsville Loss 4-13 -20.42 271 7.3% Counts (Why) Apr 27th Great Lakes D I College Mens Regionals 2024
222 Ball State Win 14-13 14 238 7.3% Counts Apr 28th Great Lakes D I College Mens Regionals 2024
135 Grand Valley Loss 5-15 -17.31 183 7.3% Counts (Why) Apr 28th Great Lakes D I College Mens Regionals 2024
145 Southern Illinois-Edwardsville Loss 7-13 -17.07 271 7.3% Counts Apr 28th Great Lakes D I College Mens Regionals 2024
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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.