D-III College Women's USAU Rankings

2018-19 Season

Data Updated Through February 18th at 7:00pm PST

AC 2 · GL 1 · ME 1 · NE 1 · NC 1 · NW 4 · OV 1 · SC 2 · SE 1 · SW 2

FAQ
Division I // Division III
Note that UBC (among others) has not yet played a sanctioned game and is therefore not included here
Rank Change Team                                                 Record Rating Change Region Conference Div   SoS PDC %
- Lewis & Clark NW 1 2-1 1733.42 22 Northwest Northwest DIII D-III 1381.91 351.51 0.25
- Portland NW 2 2-2 1435.8 57 Northwest Northwest DIII D-III 1362.14 73.66 0.05
- Puget Sound NW 3 2-2 1370.14 116 Northwest Northwest DIII D-III 1252.2 117.94 0.09
- Claremont SW 1 1-2 1322.46 22 Southwest SoCal DI D-III 1511.17 -188.71 -0.12
49 8 Catholic AC 1 6-1 1317.6 58 Atlantic Coast Atlantic Coast DIII D-III 1115.62 201.98 0.18
- Richmond AC 2 2-2 1206.56 Atlantic Coast Atlantic Coast DIII D-III 1065.68 140.88 0.13
56 20 Pacific Lutheran NW 4 3-2 1164.64 174 Northwest Northwest DIII D-III 1072.8 91.84 0.09
64 43 Trinity SC 1 7-0 1110.64 504 South Central South Central DIII D-III 648.35 462.29 0.71
87 9 North Georgia SE 1 5-6 793.68 204 Southeast Southern Appalachian DI D-III 895.31 -101.63 -0.11
88 43 Rice SC 2 7-6 770.8 452 South Central Texas DIII D-III 847.78 -76.98 -0.09
- Occidental SW 2 0-3 628.08 190 Southwest SoCal DI D-III 1033.88 -405.8 -0.39
112 Hendrix 0-7 211.8 South Central South Central DIII D-III 683.72 -471.92 -0.69
- Union 0-3 31.39 Southeast Southeast DIII D-III 537.28 -505.89 -0.94
118 21 Elon 2-3 -54.31 122 Atlantic Coast Atlantic Coast DIII D-III -29.31 -25 0.85
124 23 Wooster OV 1 0-5 -595.49 122 Ohio Valley Ohio DIII D-III -85.03 -510.46 6
126 24 North Carolina-Asheville 0-6 Atlantic Coast Atlantic Coast DIII D-III NaN NaN NaN

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.