(3) #373 Northwestern-B (4-14)

332.42 (538)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
63 Iowa** Loss 0-13 0 349 0% Ignored (Why) Mar 2nd Midwest Throwdown 2024
185 Minnesota-Duluth** Loss 2-13 0 227 0% Ignored (Why) Mar 2nd Midwest Throwdown 2024
45 St Olaf** Loss 4-13 0 308 0% Ignored (Why) Mar 2nd Midwest Throwdown 2024
353 Carleton College-Karls-C Loss 6-9 -18.3 282 6.66% Counts Mar 3rd Midwest Throwdown 2024
294 Knox Loss 5-7 4.89 307 5.95% Counts Mar 3rd Midwest Throwdown 2024
227 St John's (Minnesota) Loss 6-9 19.88 445 6.66% Counts Mar 3rd Midwest Throwdown 2024
101 Colorado Mines** Loss 1-13 0 143 0% Ignored (Why) Mar 30th Old Capitol Open 2024
122 Minnesota-B** Loss 3-13 0 323 0% Ignored (Why) Mar 30th Old Capitol Open 2024
414 Wisconsin-Milwaukee-B** Win 9-3 0 277 0% Ignored (Why) Mar 30th Old Capitol Open 2024
227 St John's (Minnesota)** Loss 0-13 0 445 0% Ignored (Why) Mar 30th Old Capitol Open 2024
368 Iowa State-B Loss 6-9 -32.07 431 8.39% Counts Mar 31st Old Capitol Open 2024
337 Wisconsin-Stevens Point Loss 4-12 -36.53 475 9.06% Counts (Why) Mar 31st Old Capitol Open 2024
357 Michigan State-B Loss 7-11 -37.33 530 10.31% Counts Apr 13th Great Lakes Dev Mens Conferences 2024
384 Notre Dame-B Win 15-10 45 244 10.59% Counts Apr 13th Great Lakes Dev Mens Conferences 2024
323 Purdue-B Loss 6-14 -35.76 368 10.59% Counts (Why) Apr 13th Great Lakes Dev Mens Conferences 2024
369 Illinois-B Win 11-9 32.84 240 10.59% Counts Apr 14th Great Lakes Dev Mens Conferences 2024
323 Purdue-B Win 13-9 84.94 368 10.59% Counts Apr 14th Great Lakes Dev Mens Conferences 2024
323 Purdue-B Loss 7-12 -26.34 368 10.59% Counts Apr 14th Great Lakes Dev 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.