#191 Oklahoma State (16-12)

avg: 1083.79  •  sd: 54.27  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
368 Dallas Win 12-7 850.1 Feb 22nd Dust Bowl 2025
87 Missouri S&T Loss 8-10 1228.42 Feb 22nd Dust Bowl 2025
200 Nebraska Win 11-10 1181.52 Feb 22nd Dust Bowl 2025
133 North Texas Loss 4-8 748.19 Feb 22nd Dust Bowl 2025
164 Truman State Loss 8-11 841.53 Feb 22nd Dust Bowl 2025
245 Texas-Dallas Win 11-5 1486.38 Feb 23rd Dust Bowl 2025
164 Truman State Loss 4-13 607.14 Feb 23rd Dust Bowl 2025
86 Colorado-B Loss 3-13 893.4 Mar 15th Mens Centex 2025
336 Texas A&M-B Win 13-7 1113.98 Mar 15th Mens Centex 2025
245 Texas-Dallas Win 10-9 1011.38 Mar 15th Mens Centex 2025
287 Texas State Win 12-8 1186.96 Mar 15th Mens Centex 2025
118 Mississippi State Loss 11-15 976.08 Mar 16th Mens Centex 2025
183 Tarleton State Win 15-13 1346.63 Mar 16th Mens Centex 2025
92 Texas A&M Win 13-12 1602.88 Mar 16th Mens Centex 2025
259 Colorado State-B Win 15-5 1440.19 Mar 29th Free State Classic 2025
195 John Brown Win 14-8 1605.67 Mar 29th Free State Classic 2025
160 Kansas Loss 10-14 818.49 Mar 29th Free State Classic 2025
164 Truman State Win 15-13 1421.32 Mar 29th Free State Classic 2025
259 Colorado State-B Win 9-7 1119.53 Mar 30th Free State Classic 2025
324 Kansas State Win 15-7 1187.15 Mar 30th Free State Classic 2025
164 Truman State Loss 9-12 861.77 Mar 30th Free State Classic 2025
160 Kansas Loss 9-15 701.71 Apr 12th Ozarks D I Mens Conferences 2025
324 Kansas State Win 15-9 1102.64 Apr 12th Ozarks D I Mens Conferences 2025
97 Missouri Loss 9-15 944.49 Apr 12th Ozarks D I Mens Conferences 2025
274 Oklahoma Win 12-9 1132.66 Apr 12th Ozarks D I Mens Conferences 2025
207 Missouri State Win 11-8 1393.64 Apr 13th Ozarks D I Mens Conferences 2025
189 Baylor Loss 7-15 491.24 Apr 26th South Central D I College Mens Regionals 2025
92 Texas A&M Loss 11-15 1096.71 Apr 26th 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)