#150 Kansas (7-5)

avg: 961.07  •  sd: 90.15  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
271 Texas A&M-B Win 13-1 1007.6 Feb 25th Dust Bowl 2023
278 Oklahoma Win 9-6 768.33 Feb 25th Dust Bowl 2023
198 Illinois State Win 13-5 1345.95 Feb 25th Dust Bowl 2023
32 Oklahoma Christian Loss 6-13 1027.39 Feb 25th Dust Bowl 2023
213 Texas-Dallas Loss 6-9 254.04 Feb 26th Dust Bowl 2023
187 North Texas Win 11-5 1396.77 Feb 26th Dust Bowl 2023
147 Wichita State Win 9-7 1252.09 Feb 26th Dust Bowl 2023
248 Northern Iowa Win 8-5 962.2 Mar 4th Midwest Throwdown 2023
322 Iowa State-B** Win 13-0 613.16 Ignored Mar 4th Midwest Throwdown 2023
86 Grinnell Loss 6-9 845.79 Mar 4th Midwest Throwdown 2023
153 Minnesota-Duluth Loss 9-11 697.77 Mar 5th Midwest Throwdown 2023
41 Iowa State Loss 6-11 982.34 Mar 5th Midwest Throwdown 2023
**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)