#208 Wisconsin-Whitewater (7-11)

avg: 1034.01  •  sd: 82.15  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
176 Northern Iowa Loss 5-13 570.83 Mar 22nd Meltdown 2025
338 Rose-Hulman Win 12-4 1142.77 Mar 22nd Meltdown 2025
253 St Thomas Win 9-8 987.99 Mar 22nd Meltdown 2025
204 Winona State Loss 5-9 516.86 Mar 22nd Meltdown 2025
242 Grace Win 7-4 1405.86 Mar 23rd Meltdown 2025
176 Northern Iowa Loss 4-11 570.83 Mar 23rd Meltdown 2025
423 Northern Michigan** Win 15-5 600 Ignored Apr 12th Lake Superior D I Mens Conferences 2025
44 Wisconsin Loss 9-13 1358.66 Apr 12th Lake Superior D I Mens Conferences 2025
155 Wisconsin-La Crosse Loss 9-13 834.31 Apr 12th Lake Superior D I Mens Conferences 2025
143 Wisconsin-Milwaukee Loss 4-15 688.58 Apr 12th Lake Superior D I Mens Conferences 2025
155 Wisconsin-La Crosse Loss 12-15 952.38 Apr 13th Lake Superior D I Mens Conferences 2025
143 Wisconsin-Milwaukee Loss 9-14 814.72 Apr 13th Lake Superior D I Mens Conferences 2025
300 Wisconsin-Stevens Point Win 15-5 1281.61 Apr 13th Lake Superior D I Mens Conferences 2025
103 Marquette Loss 11-13 1224.42 Apr 26th North Central D I College Mens Regionals 2025
44 Wisconsin** Loss 3-15 1177.22 Ignored Apr 26th North Central D I College Mens Regionals 2025
222 Wisconsin-B Win 15-8 1554.77 Apr 26th North Central D I College Mens Regionals 2025
135 Wisconsin-Eau Claire Loss 7-15 721.39 Apr 26th North Central D I College Mens Regionals 2025
195 Minnesota-B Win 14-8 1619.9 Apr 27th North Central D I College Mens 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)