College Men's USAU Rankings (GL)

2019-20 Season

Data updated through 6:00pm PDT on March 12

FAQ
Division I // Division III
Rank    Change Team                                                 Record Rating Change Region Conference Div   SoS PDC %
17 1 Michigan GL 1 8-1 1847.66 12 Great Lakes Michigan DI D-I 1661.18 186.48 0.11
33 6 Northwestern 16-7 1665.39 51 Great Lakes Illinois DI D-I 1498.51 166.87 0.11
45 1 Notre Dame 8-3 1573.57 6 Great Lakes East Plains DI D-I 1428.43 145.14 0.1
50 Purdue 3-8 1497.38 188 Great Lakes East Plains DI D-I 1734.68 -237.3 -0.14
57 3 Illinois 8-6 1452.91 10 Great Lakes Illinois DI D-I 1467.1 -14.19 -0.01
71 1 Kentucky 9-5 1373.75 36 Great Lakes East Plains DI D-I 1200.28 173.47 0.14
91 1 Indiana 5-8 1270.12 33 Great Lakes East Plains DI D-I 1325.24 -55.11 -0.04
108 51 Chicago 9-5 1182.47 255 Great Lakes Illinois DI D-I 1009.34 173.13 0.17
137 31 Ball State 3-3 1048.2 158 Great Lakes East Plains DI D-I 1127.5 -79.3 -0.07
146 11 Michigan State 2-4 1014.35 84 Great Lakes Michigan DI D-I 1118.3 -103.96 -0.09
164 78 Illinois State 8-10 973.7 288 Great Lakes Illinois DI D-I 1068.04 -94.34 -0.09
173 Purdue-B 2-3 933.51 Great Lakes Great Lakes Dev Dev 971.13 -37.61 -0.04
221 Michigan-B 4-3 746.86 Great Lakes Great Lakes Dev Dev 750.71 -3.85 -0.01
231 62 Northwestern-B 5-8 719.29 163 Great Lakes Great Lakes Dev Dev 822.57 -103.28 -0.13
273 Knox 1-5 484.15 Great Lakes Illinois DIII D-III 735.59 -251.44 -0.34
281 20 Butler 2-5 447.86 121 Great Lakes East Plains DIII D-III 643.11 -195.25 -0.3
289 19 Olivet Nazarene 1-5 412.1 126 Great Lakes Illinois DIII D-III 738.47 -326.37 -0.44
305 Illinois-B 1-5 300.09 Great Lakes Great Lakes Dev Dev 530.31 -230.22 -0.43
342 52 Kentucky-B 4-8 -3.03 120 Great Lakes Great Lakes Dev D-I 258.19 -261.23 -1.01

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.