(5) #330 Cornell-B (10-10)

232.58 (98)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
347 Army Loss 3-5 -23.37 51 4.2% Counts Mar 1st Garden State 2025
237 Connecticut College Loss 4-14 -12.68 69 6.48% Counts (Why) Mar 1st Garden State 2025
362 Stevens Tech Win 15-3 24.3 158 6.48% Counts (Why) Mar 1st Garden State 2025
278 Central Connecticut State Win 10-8 33.61 317 6.3% Counts Mar 2nd Garden State 2025
321 Pennsylvania Western Win 9-3 36.19 552 5.36% Counts (Why) Mar 2nd Garden State 2025
254 Swarthmore Loss 3-13 -16.98 285 6.48% Counts (Why) Mar 2nd Garden State 2025
177 Towson** Loss 2-13 0 47 0% Ignored (Why) Mar 15th Natalies Animal Rescue 2025
259 Drexel Loss 9-11 7.6 56 7.27% Counts Mar 15th Natalies Animal Rescue 2025
390 Siena** Win 13-5 0 180 0% Ignored (Why) Mar 15th Natalies Animal Rescue 2025
324 Villanova Loss 7-13 -41.55 44 7.27% Counts Mar 15th Natalies Animal Rescue 2025
390 Siena** Win 13-1 0 180 0% Ignored (Why) Mar 16th Natalies Animal Rescue 2025
177 Towson** Loss 2-15 0 47 0% Ignored (Why) Mar 16th Natalies Animal Rescue 2025
324 Villanova Win 7-4 30.65 44 5.53% Counts (Why) Mar 16th Natalies Animal Rescue 2025
392 SUNY-Albany-B** Win 13-4 0 0% Ignored (Why) Mar 29th Northeast Classic 2025
362 Stevens Tech Win 10-5 25.39 158 7.25% Counts (Why) Mar 29th Northeast Classic 2025
214 Vermont-C Loss 3-7 -5.56 5.92% Counts (Why) Mar 29th Northeast Classic 2025
328 Hofstra Loss 5-9 -39.24 322 7% Counts Mar 29th Northeast Classic 2025
372 West Chester-B Win 11-10 -16.43 21 8.16% Counts Mar 30th Northeast Classic 2025
310 Rowan Win 10-9 18.65 5 8.16% Counts Mar 30th Northeast Classic 2025
328 Hofstra Loss 9-11 -21.43 322 8.16% Counts Mar 30th Northeast Classic 2025
**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.