#184 Wisconsin-La Crosse (12-10)

avg: 1186.43  •  sd: 50.67  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
348 Kansas State** Win 11-4 1125.33 Ignored Mar 23rd Free State Classic
153 Missouri S&T Loss 7-11 838.26 Mar 23rd Free State Classic
228 Oklahoma State Win 10-6 1518.39 Mar 23rd Free State Classic
356 Wichita State** Win 13-4 1089.08 Ignored Mar 23rd Free State Classic
263 Illinois State Win 13-4 1498.19 Mar 24th Free State Classic
45 St Olaf** Loss 4-15 1211.34 Ignored Mar 24th Free State Classic
100 Colorado-B Loss 10-11 1388.43 Mar 30th Old Capitol Open 2024
368 Iowa State-B** Win 11-4 1000.66 Ignored Mar 30th Old Capitol Open 2024
134 Macalester Loss 3-10 772.12 Mar 30th Old Capitol Open 2024
193 Grinnell Win 6-5 1279.63 Mar 31st Old Capitol Open 2024
263 Illinois State Win 8-6 1198.68 Mar 31st Old Capitol Open 2024
122 Minnesota-B Loss 7-8 1275.63 Mar 31st Old Capitol Open 2024
140 Marquette Win 10-9 1468.49 Apr 13th Lake Superior D I Mens Conferences 2024
94 Wisconsin-Eau Claire Loss 6-13 934.71 Apr 13th Lake Superior D I Mens Conferences 2024
337 Wisconsin-Stevens Point** Win 13-1 1165.68 Ignored Apr 13th Lake Superior D I Mens Conferences 2024
216 Wisconsin-Whitewater Loss 10-13 732.73 Apr 13th Lake Superior D I Mens Conferences 2024
337 Wisconsin-Stevens Point Win 15-8 1130.49 Apr 14th Lake Superior D I Mens Conferences 2024
105 Wisconsin-Milwaukee Loss 11-15 1103.71 Apr 14th Lake Superior D I Mens Conferences 2024
216 Wisconsin-Whitewater Win 15-6 1660.88 Apr 14th Lake Superior D I Mens Conferences 2024
257 Wisconsin-B Win 14-10 1316.55 Apr 27th North Central D I College Mens Regionals 2024
94 Wisconsin-Eau Claire Loss 10-15 1081.1 Apr 27th North Central D I College Mens Regionals 2024
105 Wisconsin-Milwaukee Loss 10-15 1031.27 Apr 27th North Central D I College Mens Regionals 2024
**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)