#159 Kansas (14-10)

avg: 1232.13  •  sd: 59.27  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
158 Grinnell Loss 8-10 971.33 Feb 22nd Dust Bowl 2025
284 Harding Win 9-3 1361.43 Feb 22nd Dust Bowl 2025
309 Washington University-B Win 13-6 1240.96 Feb 22nd Dust Bowl 2025
137 Wichita State Loss 8-12 873.09 Feb 22nd Dust Bowl 2025
367 Dallas** Win 11-4 946.22 Ignored Feb 23rd Dust Bowl 2025
163 Truman State Loss 7-10 832.55 Feb 23rd Dust Bowl 2025
258 Colorado State-B Win 15-3 1444.48 Mar 29th Free State Classic 2025
163 Truman State Win 15-7 1822.22 Mar 29th Free State Classic 2025
322 Kansas State** Win 15-3 1201.16 Ignored Mar 29th Free State Classic 2025
191 Oklahoma State Win 14-10 1499.1 Mar 29th Free State Classic 2025
258 Colorado State-B Win 15-4 1444.48 Mar 30th Free State Classic 2025
194 John Brown Loss 11-12 960.63 Mar 30th Free State Classic 2025
163 Truman State Loss 10-13 894.08 Mar 30th Free State Classic 2025
96 Missouri Loss 9-15 962.09 Apr 12th Ozarks D I Mens Conferences 2025
322 Kansas State Win 14-11 914.5 Apr 12th Ozarks D I Mens Conferences 2025
272 Oklahoma Win 15-2 1405.29 Apr 12th Ozarks D I Mens Conferences 2025
191 Oklahoma State Win 15-9 1615.88 Apr 12th Ozarks D I Mens Conferences 2025
12 Washington University** Loss 4-15 1504.3 Ignored Apr 13th Ozarks D I Mens Conferences 2025
201 Saint Louis Win 9-6 1478.61 Apr 13th Ozarks D I Mens Conferences 2025
86 Colorado-B Loss 7-14 926.9 Apr 26th South Central D I College Mens Regionals 2025
245 Texas-Dallas Win 12-9 1246.74 Apr 26th South Central D I College Mens Regionals 2025
189 Baylor Win 12-10 1345.82 Apr 27th South Central D I College Mens Regionals 2025
5 Colorado** Loss 3-13 1758.91 Ignored Apr 27th South Central D I College Mens Regionals 2025
96 Missouri Loss 9-11 1228.36 Apr 27th South Central D I College Mens Regionals 2025
**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)