(16) #75 Lewis & Clark (10-9)

1375.08 (90)

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# Opponent Result Effect Opp. Delta % of Ranking Status Date Event
37 Carleton College-Eclipse Loss 7-11 -4.76 106 4.89% Counts Feb 10th DIII Grand Prix
118 Puget Sound Loss 7-8 -18.22 41 4.46% Counts Feb 10th DIII Grand Prix
154 Oregon State Win 8-6 -9.7 35 4.31% Counts Feb 10th DIII Grand Prix
46 Whitman Loss 3-11 -16.17 52 4.61% Counts (Why) Feb 10th DIII Grand Prix
48 Colorado College Loss 6-8 -2.8 95 4.31% Counts Feb 11th DIII Grand Prix
43 Portland Loss 4-9 -13.59 23 4.15% Counts (Why) Feb 11th DIII Grand Prix
118 Puget Sound Win 9-3 14.52 41 4.15% Counts (Why) Feb 11th DIII Grand Prix
165 Cal State-Long Beach Win 12-0 1.58 71 6.07% Counts (Why) Mar 9th Irvine Open
184 California-Davis-B** Win 13-0 0 82 0% Ignored (Why) Mar 9th Irvine Open
85 California-San Diego-B Loss 4-5 -7.32 83 4.35% Counts Mar 9th Irvine Open
137 California-B Win 10-2 10.06 51 5.53% Counts (Why) Mar 10th Irvine Open
88 California-Irvine Loss 5-6 -9.65 56 4.81% Counts Mar 10th Irvine Open
186 UCLA-B** Win 13-2 0 149 0% Ignored (Why) Mar 10th Irvine Open
189 Pacific Lutheran Win 9-6 -31.66 7.5% Counts Apr 13th Northwest D III Womens Conferences 2024
118 Puget Sound Win 11-7 18.08 41 8.22% Counts Apr 13th Northwest D III Womens Conferences 2024
46 Whitman Loss 6-14 -30.88 52 8.45% Counts (Why) Apr 13th Northwest D III Womens Conferences 2024
43 Portland Loss 9-10 14.9 23 8.45% Counts Apr 13th Northwest D III Womens Conferences 2024
118 Puget Sound Win 9-6 12.46 41 7.5% Counts Apr 14th Northwest D III Womens Conferences 2024
46 Whitman Win 12-6 75.65 52 8.22% Counts (Why) Apr 14th Northwest D III Womens 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.