#156 Minnesota-B (12-5)

avg: 1136.27  •  sd: 81.42  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
306 Bethel Loss 9-10 516.12 Feb 9th Ugly Dome I 2019
321 Carleton Hot Karls Win 13-3 1189.49 Feb 9th Ugly Dome I 2019
186 Macalester Win 10-8 1294.29 Feb 9th Ugly Dome I 2019
177 Winona State Win 12-8 1503.2 Feb 9th Ugly Dome I 2019
364 Minnesota-C** Win 13-1 1011.26 Ignored Feb 9th Ugly Dome I 2019
218 St. Thomas Win 13-9 1343.1 Feb 23rd Ugly Dome II 2019
205 Wisconsin-B Win 11-10 1095.59 Feb 23rd Ugly Dome II 2019
313 Drake Win 13-3 1213.8 Feb 23rd Ugly Dome II 2019
240 Wisconsin-Eau Claire Win 13-7 1447.37 Feb 23rd Ugly Dome II 2019
249 Wisconsin- La Crosse Loss 9-10 732.01 Feb 23rd Ugly Dome II 2019
424 Coe** Win 13-0 635.35 Ignored Mar 30th Old Capitol Open 2019
433 Chicago-B** Win 13-0 421.23 Ignored Mar 30th Old Capitol Open 2019
194 Kansas State Loss 11-12 882.61 Mar 30th Old Capitol Open 2019
321 Carleton Hot Karls Win 15-5 1189.49 Mar 30th Old Capitol Open 2019
105 Iowa Loss 13-15 1123.19 Mar 31st Old Capitol Open 2019
89 Luther Win 14-13 1521.55 Mar 31st Old Capitol Open 2019
194 Kansas State Loss 13-14 882.61 Mar 31st Old Capitol Open 2019
**Blowout Eligible

FAQ

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
  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
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)