#105 Kansas (9-4)

avg: 1198.67  •  sd: 82.12  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
136 Oklahoma Win 14-11 1376.39 Feb 22nd Dust Bowl 2020
93 Rice Win 14-12 1481.76 Feb 22nd Dust Bowl 2020
219 Kansas State Win 13-8 1248.46 Feb 22nd Dust Bowl 2020
138 North Texas Loss 13-14 918.89 Feb 23rd Dust Bowl 2020
181 Colorado-B Win 15-13 1131.01 Feb 23rd Dust Bowl 2020
145 Nebraska Loss 11-12 892.59 Feb 23rd Dust Bowl 2020
292 Wisconsin-Oshkosh Win 12-7 927.39 Mar 7th Midwest Throwdown 2020
256 Northern Iowa Win 11-7 1065.44 Mar 7th Midwest Throwdown 2020
67 Wisconsin-Milwaukee Win 11-6 1938.34 Mar 7th Midwest Throwdown 2020
235 St. Thomas Win 12-11 838.93 Mar 7th Midwest Throwdown 2020
77 Iowa State Win 9-8 1451.05 Mar 8th Midwest Throwdown 2020
33 Northwestern Loss 6-12 1086.08 Mar 8th Midwest Throwdown 2020
67 Wisconsin-Milwaukee Loss 7-8 1266.64 Mar 8th Midwest Throwdown 2020
**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)