#335 Wichita State (2-10)

avg: 121.02  •  sd: 103.42  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
229 Northern Iowa Loss 7-11 248.9 Feb 17th Dust Bowl 2024
47 Oklahoma Christian** Loss 0-13 920.17 Ignored Feb 17th Dust Bowl 2024
218 Texas-Dallas** Loss 3-11 157.85 Ignored Feb 17th Dust Bowl 2024
317 Washington University-B Win 12-7 790.73 Feb 17th Dust Bowl 2024
253 Nebraska Loss 9-15 90.19 Feb 18th Dust Bowl 2024
229 Northern Iowa Loss 9-13 297.23 Feb 18th Dust Bowl 2024
351 Kansas State Win 10-8 286.54 Mar 23rd Free State Classic
179 Missouri S&T** Loss 3-13 304.1 Ignored Mar 23rd Free State Classic
254 Oklahoma State Loss 4-13 -0.47 Mar 23rd Free State Classic
180 Wisconsin-La Crosse** Loss 4-13 298.16 Ignored Mar 23rd Free State Classic
351 Kansas State Loss 9-11 -225.34 Mar 24th Free State Classic
342 Wisconsin-Stevens Point Loss 7-10 -298.83 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)