#98 Wisconsin-Eau Claire (13-9)

avg: 1085.78  •  sd: 76.23  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
96 Arkansas Loss 4-5 966.69 Mar 1st Midwest Throwdown 2025
101 Macalester Loss 4-10 454.48 Mar 1st Midwest Throwdown 2025
117 Northwestern Loss 6-11 393.18 Mar 1st Midwest Throwdown 2025
253 Northwestern-B** Win 13-0 508.95 Ignored Mar 1st Midwest Throwdown 2025
96 Arkansas Win 6-5 1216.69 Mar 2nd Midwest Throwdown 2025
66 Illinois Win 10-9 1433.53 Mar 2nd Midwest Throwdown 2025
128 Winona State Win 11-2 1444.6 Mar 2nd Midwest Throwdown 2025
173 Grinnell Win 13-0 1192.63 Mar 29th Old Capitol Open 2025
130 Iowa Win 11-4 1426.07 Mar 29th Old Capitol Open 2025
216 Wisconsin-Milwaukee** Win 13-1 860.38 Ignored Mar 29th Old Capitol Open 2025
92 Iowa State Loss 8-9 986.73 Mar 30th Old Capitol Open 2025
101 Macalester Win 7-6 1179.48 Mar 30th Old Capitol Open 2025
117 Northwestern Win 10-3 1539.88 Mar 30th Old Capitol Open 2025
245 Wisconsin-B** Win 15-2 626.43 Ignored Apr 12th Lake Superior D I Womens Conferences 2025
145 Wisconsin-La Crosse Win 13-8 1220.35 Apr 12th Lake Superior D I Womens Conferences 2025
35 Wisconsin Loss 6-8 1333.31 Apr 13th Lake Superior D I Womens Conferences 2025
130 Iowa Loss 6-7 701.07 Apr 26th North Central D I College Womens Regionals 2025
24 Minnesota** Loss 3-15 1223.12 Ignored Apr 26th North Central D I College Womens Regionals 2025
216 Wisconsin-Milwaukee** Win 15-3 860.38 Ignored Apr 26th North Central D I College Womens Regionals 2025
145 Wisconsin-La Crosse Win 15-4 1324.19 Apr 26th North Central D I College Womens Regionals 2025
95 Marquette Loss 3-9 494.02 Apr 27th North Central D I College Womens Regionals 2025
35 Wisconsin Loss 4-15 1033.8 Apr 27th North Central D I College Womens Regionals 2025
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a team’s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation
What's the algorithm again?
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded
Is there a longer explanation of all this?
There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)