#313 Kansas-B (3-9)

avg: 667.26  •  sd: 78.3  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
231 Harding Loss 4-11 415.31 Feb 17th Dust Bowl 2024
143 Truman State** Loss 2-13 737.06 Ignored Feb 17th Dust Bowl 2024
161 Saint Louis Loss 8-10 1020.81 Feb 17th Dust Bowl 2024
200 Northern Iowa Loss 10-12 883.19 Feb 17th Dust Bowl 2024
165 John Brown Loss 9-15 753.05 Feb 18th Dust Bowl 2024
320 Washington University-B Win 13-8 1147.5 Feb 18th Dust Bowl 2024
263 Illinois State Loss 4-13 298.19 Mar 23rd Free State Classic
243 Nebraska Loss 6-12 404.13 Mar 23rd Free State Classic
337 Wisconsin-Stevens Point Win 8-7 690.68 Mar 23rd Free State Classic
200 Northern Iowa Loss 2-11 521.31 Mar 23rd Free State Classic
348 Kansas State Win 8-5 978.94 Mar 24th Free State Classic
243 Nebraska Loss 1-6 383.44 Mar 24th Free State Classic
**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)