College Women's USAU Rankings (OV)

2022-23 Season

Data updated through March 27 at 5:00pm EDT (probabilistic bids also now updated for 22-23 season)

FAQ
Division I // Division III
Rank    Change Team                                                 Record Rating Change Region Conference Div   SoS PDC %
13 1 Pittsburgh OV 1 10-4 1812.03 3 Ohio Valley Pennsylvania DI D-I 1604.49 207.54 0.13
32 3 Ohio State 12-7 1516.6 5 Ohio Valley Ohio DI D-I 1456.11 60.49 0.04
44 2 Pennsylvania 6-8 1368.71 1 Ohio Valley Pennsylvania DI D-I 1473.97 -105.26 -0.07
53 21 Haverford/Bryn Mawr 10-2 1271.17 211 Ohio Valley Pennsylvania DIII D-III 905.17 366 0.4
57 3 Penn State 5-8 1226.18 23 Ohio Valley Pennsylvania DI D-I 1322.01 -95.82 -0.07
59 6 Ohio 12-7 1178.04 151 Ohio Valley Ohio DI D-I 1184.52 -6.49 -0.01
65 7 Carnegie Mellon 2-3 1123.91 18 Ohio Valley Pennsylvania DI D-I 1148.91 -25 -0.02
66 3 Case Western Reserve 4-9 1123.47 2 Ohio Valley Ohio DI D-I 1290.38 -166.91 -0.13
80 2 Temple -B 3-3 1005.46 Ohio Valley Ohio Valley Dev Dev 1031.85 -26.39 -0.03
83 9 Temple 3-8 978.57 35 Ohio Valley Pennsylvania DI D-I 1180.98 -202.41 -0.17
92 47 Lehigh 7-5 943.24 431 Ohio Valley Pennsylvania DIII D-III 983.66 -40.42 -0.04
113 13 Cedarville 5-5 799.29 108 Ohio Valley Ohio DIII D-III 810.25 -10.96 -0.01
138 16 Cincinnati 4-2 595.28 151 Ohio Valley Ohio DI D-I 538.43 56.85 0.11
141 50 West Chester 4-2 575.75 372 Ohio Valley Pennsylvania DI D-I 287.58 288.17 1
156 12 Franciscan 2-4 446.17 42 Ohio Valley Ohio DIII D-III 533.67 -87.5 -0.16
160 Swarthmore 1-4 372.09 353 Ohio Valley Pennsylvania DIII D-III 556.34 -184.25 -0.33
172 22 Dayton 3-4 261.3 151 Ohio Valley Ohio DI D-I -40.26 301.56 -7.49
180 39 Dickinson 3-6 180.96 347 Ohio Valley Pennsylvania DIII D-III 431.9 -250.94 -0.58
189 25 Oberlin 4-3 105.64 151 Ohio Valley Ohio DIII D-III 138.55 -32.91 -0.24
202 25 Miami (Ohio) 1-4 -91.06 151 Ohio Valley Ohio DI D-I -63.25 -27.81 0.44

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.