(4) #91 Penn State (7-15)

1373.9 (3)

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# Opponent Result Effect % of Ranking Status Date Event
12 North Carolina State Loss 6-13 -1.92 3.37% Jan 20th Carolina Kickoff 2018 NC Ultimate
37 Central Florida Loss 9-11 0.41 3.37% Jan 20th Carolina Kickoff 2018 NC Ultimate
69 Carleton College-GoP Win 13-6 23.54 3.37% Jan 20th Carolina Kickoff 2018 NC Ultimate
14 Florida Loss 8-13 0.58 3.37% Jan 20th Carolina Kickoff 2018 NC Ultimate
1 North Carolina Loss 6-13 12.94 3.37% Jan 21st Carolina Kickoff 2018 NC Ultimate
16 North Carolina-Wilmington Loss 7-13 -1.64 3.37% Jan 21st Carolina Kickoff 2018 NC Ultimate
30 Auburn Loss 4-11 -9.51 3.47% Feb 3rd Queen City Tune Up 2018 College Open
64 North Carolina-Charlotte Loss 9-11 -6.31 3.78% Feb 3rd Queen City Tune Up 2018 College Open
16 North Carolina-Wilmington Loss 8-10 9.47 3.68% Feb 3rd Queen City Tune Up 2018 College Open
133 Case Western Reserve Win 10-8 2.46 3.68% Feb 3rd Queen City Tune Up 2018 College Open
151 George Mason Loss 8-11 -24.46 3.78% Feb 3rd Queen City Tune Up 2018 College Open
169 Johns Hopkins Win 13-8 10.33 5.34% Mar 17th Oak Creek Invite 2018
119 Bates Win 13-9 17.47 5.34% Mar 17th Oak Creek Invite 2018
134 Princeton Win 13-9 12.4 5.34% Mar 17th Oak Creek Invite 2018
34 William & Mary Loss 9-12 -4.01 5.34% Mar 17th Oak Creek Invite 2018
61 James Madison Loss 11-14 -12.12 5.34% Mar 18th Oak Creek Invite 2018
54 Mary Washington Loss 12-13 1.43 5.34% Mar 18th Oak Creek Invite 2018
78 Georgetown Loss 9-11 -11.75 5.34% Mar 18th Oak Creek Invite 2018
49 Marquette Loss 7-14 -26.07 6% Mar 31st Huck Finn 2018
162 Saint Louis Win 16-14 -5.48 6% Mar 31st Huck Finn 2018
81 Florida State Win 14-13 10.2 6% Mar 31st Huck Finn 2018
11 Emory Loss 7-12 1.68 6% Mar 31st Huck Finn 2018
**Blowout Eligible

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.