#205 Wisconsin-B (11-8)

avg: 970.59  •  sd: 81.13  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
218 St. Thomas Loss 8-11 558.92 Feb 23rd Ugly Dome II 2019
156 Minnesota-B Loss 10-11 1011.27 Feb 23rd Ugly Dome II 2019
313 Drake Win 13-5 1213.8 Feb 23rd Ugly Dome II 2019
249 Wisconsin- La Crosse Loss 6-11 310.32 Feb 23rd Ugly Dome II 2019
240 Wisconsin-Eau Claire Loss 8-10 627.17 Feb 23rd Ugly Dome II 2019
324 Stephen F. Austin Win 12-6 1164.31 Mar 16th Centex 2019 Men
273 Colorado State-B Win 10-9 900.73 Mar 16th Centex 2019 Men
409 Texas-Dallas-B Win 9-7 442.44 Mar 16th Centex 2019 Men
298 Texas A&M-B Win 15-10 1137.55 Mar 16th Centex 2019 Men
130 Baylor Loss 7-15 670.94 Mar 17th Centex 2019 Men
175 North Texas Win 13-10 1395.24 Mar 17th Centex 2019 Men
209 Trinity Win 15-4 1557.89 Mar 17th Centex 2019 Men
124 Wisconsin-Milwaukee Loss 9-11 1029.52 Mar 30th Illinois Invite 8
286 Toledo Win 9-8 847.93 Mar 30th Illinois Invite 8
140 Missouri State Win 8-7 1350.36 Mar 31st Illinois Invite 8
219 Michigan State Win 7-5 1250.98 Mar 31st Illinois Invite 8
148 Michigan-B Loss 4-11 581.95 Mar 31st Illinois Invite 8
112 Wisconsin-Whitewater Loss 4-8 741.4 Mar 31st Illinois Invite 8
236 Wisconsin-Platteville Win 10-6 1398.16 Mar 31st Illinois Invite 8
**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)