#177 Winona State (11-7)

avg: 1062.04  •  sd: 52.43  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
156 Minnesota-B Loss 8-12 695.12 Feb 9th Ugly Dome I 2019
364 Minnesota-C** Win 13-3 1011.26 Ignored Feb 9th Ugly Dome I 2019
306 Bethel Win 13-6 1241.12 Feb 9th Ugly Dome I 2019
321 Carleton Hot Karls Win 13-6 1189.49 Feb 9th Ugly Dome I 2019
186 Macalester Loss 11-12 906.62 Feb 9th Ugly Dome I 2019
302 Rose-Hulman Win 7-3 1252.23 Mar 9th D III Midwestern Invite 2019
84 Brandeis Loss 8-9 1306.89 Mar 9th D III Midwestern Invite 2019
183 Oberlin Win 6-5 1166.96 Mar 9th D III Midwestern Invite 2019
121 Puget Sound Loss 8-11 915.41 Mar 10th D III Midwestern Invite 2019
332 Milwaukee School of Engineering Win 10-7 948.86 Mar 10th D III Midwestern Invite 2019
332 Milwaukee School of Engineering Win 13-6 1159.2 Mar 23rd Meltdown 2019
237 Loyola-Chicago Win 11-8 1265.47 Mar 23rd Meltdown 2019
86 Marquette Loss 4-13 826.08 Mar 23rd Meltdown 2019
424 Coe** Win 13-2 635.35 Ignored Mar 23rd Meltdown 2019
360 Illinois-Chicago** Win 13-3 1031.2 Ignored Mar 24th Meltdown 2019
97 Grand Valley State Loss 3-13 763.8 Mar 24th Meltdown 2019
112 Wisconsin-Whitewater Win 12-11 1431.21 Mar 24th Meltdown 2019
109 Truman State Loss 9-13 904.73 Mar 24th Meltdown 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)