#356 Wichita State (3-16)

avg: 489.08  •  sd: 101.44  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
200 Northern Iowa Loss 7-11 654.42 Feb 17th Dust Bowl 2024
41 Oklahoma Christian** Loss 0-13 1252.87 Ignored Feb 17th Dust Bowl 2024
190 Texas-Dallas** Loss 3-11 558.34 Ignored Feb 17th Dust Bowl 2024
320 Washington University-B Win 12-7 1171.85 Feb 17th Dust Bowl 2024
243 Nebraska Loss 9-15 467.96 Feb 18th Dust Bowl 2024
200 Northern Iowa Loss 9-13 702.75 Feb 18th Dust Bowl 2024
348 Kansas State Win 10-8 788 Mar 23rd Free State Classic
153 Missouri S&T** Loss 3-13 705.15 Ignored Mar 23rd Free State Classic
228 Oklahoma State Loss 4-13 422.23 Mar 23rd Free State Classic
184 Wisconsin-La Crosse** Loss 4-13 586.43 Ignored Mar 23rd Free State Classic
348 Kansas State Loss 9-11 276.13 Mar 24th Free State Classic
337 Wisconsin-Stevens Point Loss 7-10 176.02 Mar 24th Free State Classic
95 Arkansas** Loss 3-15 934.59 Ignored Apr 13th Ozarks D I Mens Conferences 2024
233 Oklahoma Loss 6-14 405.7 Apr 13th Ozarks D I Mens Conferences 2024
20 Washington University** Loss 6-15 1486.16 Ignored Apr 13th Ozarks D I Mens Conferences 2024
320 Washington University-B Win 12-5 1251.34 Apr 13th Ozarks D I Mens Conferences 2024
348 Kansas State Loss 6-15 -74.67 Apr 14th Ozarks D I Mens Conferences 2024
296 Missouri State Loss 12-15 429.36 Apr 14th Ozarks D I Mens Conferences 2024
320 Washington University-B Loss 5-15 51.34 Apr 14th Ozarks D I Mens Conferences 2024
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)