College Women's USAU Rankings (GL)
2023-24 Season
Data updated through April 1 at 8:00pm EDT
| Rank | Change | Team | Record | Rating | Change | Region | Conference | Div | SoS | PDC | % |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 12 | Michigan GL 1 | 14-6 | 2311.61 | 30 | Great Lakes | Eastern Great Lakes DI | D-I | 2143.63 | 167.98 | 0.08 | |
| 22 | 1 | Notre Dame | 12-6 | 2055.17 | 6 | Great Lakes | Eastern Great Lakes DI | D-I | 1886.51 | 168.66 | 0.09 |
| 42 | 1 | Purdue | 8-4 | 1700.79 | 34 | Great Lakes | Eastern Great Lakes DI | D-I | 1522.77 | 178.03 | 0.12 |
| 82 | 22 | Northwestern | 6-10 | 1332.46 | 189 | Great Lakes | Illinois DI | D-I | 1441.89 | -109.42 | -0.08 |
| 87 | 8 | Indiana | 4-2 | 1302.91 | 13 | Great Lakes | Eastern Great Lakes DI | D-I | 1077.45 | 225.47 | 0.21 |
| 96 | 19 | Chicago | 5-15 | 1251 | 104 | Great Lakes | Illinois DI | D-I | 1417.14 | -166.13 | -0.12 |
| 100 | 15 | Davenport | 5-5 | 1193.97 | 85 | Great Lakes | Great Lakes DIII | D-III | 1245.44 | -51.53 | -0.04 |
| 122 | 2 | Illinois | 12-8 | 987.6 | 51 | Great Lakes | Illinois DI | D-I | 756.16 | 231.17 | 0.31 |
| 147 | 8 | Kentucky | 2-4 | 841.73 | 23 | Great Lakes | Eastern Great Lakes DI | D-I | 913.6 | -72.21 | -0.08 |
| 160 | 7 | Knox | 5-6 | 674.39 | 207 | Great Lakes | Great Lakes DIII | D-III | 785.9 | -111.55 | -0.14 |
| 170 | 9 | Butler | 2-5 | 562.82 | 8 | Great Lakes | Great Lakes DIII | D-III | 735.18 | -172.35 | -0.23 |
| 171 | 12 | Michigan-B | 6-5 | 560.18 | 53 | Great Lakes | Great Lakes Dev | Dev | 398.26 | 162.27 | 0.41 |
| 216 | 48 | Northwestern-B | 1-11 | -26.07 | 478 | Great Lakes | Great Lakes Dev | Dev | 430.61 | -456.82 | -1.06 |
| 225 | 11 | Notre Dame-B | 1-5 | -427.77 | 2 | Great Lakes | Great Lakes Dev | Dev | -193.33 | -234.43 | 1.21 |
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:
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.
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.