College Women's USAU Rankings (OV)

2022-23 Season

Data updated through September 25 at 10:00am EDT

FAQ
Division I // Division III
Rank    Change Team                                                 Record Rating Change Region Conference Div   SoS PDC %
13 Pittsburgh OV 1 10-4 1933.35 145 Ohio Valley Pennsylvania DI D-I 1725.8 207.54 0.12
33 1 Ohio State 12-7 1633.9 145 Ohio Valley Ohio DI D-I 1607.03 26.87 0.02
44 Pennsylvania 6-8 1484.31 147 Ohio Valley Pennsylvania DI D-I 1589.57 -105.26 -0.07
59 3 Penn State 9-10 1301.24 119 Ohio Valley Pennsylvania DI D-I 1325.51 -24.27 -0.02
60 1 Ohio 12-7 1299.34 133 Ohio Valley Ohio DI D-I 1305.83 -6.49 0
63 10 Haverford/Bryn Mawr 14-4 1276.27 234 Ohio Valley Pennsylvania DIII D-III 984.6 291.67 0.3
65 2 Carnegie Mellon 2-3 1273.97 115 Ohio Valley Pennsylvania DI D-I 1298.97 -25 -0.02
69 1 Case Western Reserve 4-9 1250.46 134 Ohio Valley Ohio DI D-I 1335.72 -85.26 -0.06
95 3 Temple 7-11 1029.11 173 Ohio Valley Pennsylvania DI D-I 1069.39 -40.28 -0.04
98 17 Temple -B 3-3 1005.46 Ohio Valley Ohio Valley Dev Dev 1031.85 -26.39 -0.03
111 13 Lehigh 9-9 918.2 248 Ohio Valley Pennsylvania DIII D-III 1036.75 -118.55 -0.11
116 3 Cedarville 5-5 890.53 134 Ohio Valley Ohio DIII D-III 901.49 -10.96 -0.01
128 15 West Chester 4-2 792.71 24 Ohio Valley Pennsylvania DI D-I 586.14 206.57 0.35
137 1 Cincinnati 4-2 716.58 133 Ohio Valley Ohio DI D-I 659.73 56.85 0.09
159 Franciscan 2-4 513.9 142 Ohio Valley Ohio DIII D-III 601.4 -87.5 -0.15
173 6 Swarthmore 1-4 398.34 189 Ohio Valley Pennsylvania DIII D-III 482.91 -84.57 -0.18
176 2 Dayton 3-4 382.6 133 Ohio Valley Ohio DI D-I 81.04 301.56 3.72
185 3 Messiah 1-11 263.48 161 Ohio Valley Pennsylvania DIII D-III 585.54 -322.06 -0.55
187 Dickinson 3-6 251.91 139 Ohio Valley Pennsylvania DIII D-III 502.85 -250.94 -0.5
191 1 Oberlin 4-3 226.94 133 Ohio Valley Ohio DIII D-III 259.85 -32.91 -0.13
205 Miami (Ohio) 1-4 30.24 133 Ohio Valley Ohio DI D-I 58.05 -27.81 -0.48

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.