College Women's USAU Rankings (ME)

2023-24 Season

Data updated through April 1 at 8:00pm EDT

FAQ
Division I // Division III
Rank    Change Team                                                 Record Rating Change Region Conference Div   SoS PDC %
41 10 SUNY-Binghamton ME 1 6-8 1703.59 76 Metro East Western NY DI D-I 1777.96 -74.36 -0.04
47 3 Connecticut 10-3 1655.8 4 Metro East Eastern Metro East DI D-I 1360.44 295.37 0.22
52 7 Yale 6-7 1594.05 52 Metro East Eastern Metro East DI D-I 1675 -80.95 -0.05
58 2 Cornell 8-4 1527.53 21 Metro East Eastern Metro East DI D-I 1371.02 156.51 0.11
75 8 Columbia 8-10 1374.91 75 Metro East Eastern Metro East DI D-I 1449.08 -74.16 -0.05
93 20 Wesleyan 7-6 1272.46 231 Metro East Eastern Metro East DIII D-III 1193.19 79.28 0.07
98 8 Rochester 10-5 1217.18 73 Metro East Western NY DIII D-III 1260.66 -43.47 -0.03
105 11 Rutgers 7-5 1156.74 132 Metro East Eastern Metro East DI D-I 1043.11 113.63 0.11
135 25 NYU 4-8 910.84 176 Metro East Eastern Metro East DI D-I 1107.89 -197.05 -0.18
164 17 Ithaca 5-3 634 98 Metro East Western NY DIII D-III 594.77 39.24 0.07
165 12 SUNY-Geneseo 3-9 598.3 76 Metro East Western NY DIII D-III 806.92 -208.62 -0.26
172 2 Skidmore 6-7 549.06 126 Metro East Eastern Metro East DIII D-III 456.75 92.31 0.2
176 10 SUNY-Stony Brook 1-4 505.03 244 Metro East Eastern Metro East DI D-I 566.78 -61.74 -0.11
192 5 Syracuse 5-8 309.87 53 Metro East Western NY DI D-I 431.87 -121.99 -0.28
193 11 Columbia-B 0-5 299.26 371 Metro East Eastern Metro East DI Dev 709.32 -410.05 -0.58
208 23 Connecticut College 3-7 105.54 172 Metro East Eastern Metro East DIII D-III 282.61 -177.06 -0.63
223 15 SUNY-Albany 1-7 -360.51 143 Metro East Eastern Metro East DI D-I -46.79 -313.71 6.7
226 13 Cornell-B 1-9 -492.7 70 Metro East Western NY DI Dev -24.87 -467.82 18.81

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.