#283 Texas State (8-13)

avg: 762.57  •  sd: 72.89  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
189 Baylor Loss 5-14 507.7 Feb 1st Big D in Little D 2025
323 Rice Win 15-8 1163.68 Feb 1st Big D in Little D 2025
280 Texas-San Antonio Win 15-12 1077.76 Feb 1st Big D in Little D 2025
293 Trinity Loss 11-12 603.63 Feb 2nd Big D in Little D 2025
280 Texas-San Antonio Win 11-9 1026.47 Feb 2nd Big D in Little D 2025
38 Ave Maria** Loss 3-13 1215.95 Ignored Feb 22nd Mardi Gras XXXVII
233 Georgia Southern Loss 8-11 569.65 Feb 22nd Mardi Gras XXXVII
144 South Florida Loss 8-13 792.37 Feb 22nd Mardi Gras XXXVII
373 LSU-B Win 13-3 892.13 Feb 22nd Mardi Gras XXXVII
280 Texas-San Antonio Win 11-5 1377.27 Feb 22nd Mardi Gras XXXVII
225 Arkansas Loss 5-10 402.41 Mar 15th Mens Centex 2025
284 Harding Loss 8-9 636.43 Mar 15th Mens Centex 2025
130 North Texas Loss 7-11 863.33 Mar 15th Mens Centex 2025
191 Oklahoma State Loss 8-12 659.25 Mar 15th Mens Centex 2025
189 Baylor Loss 9-14 633.83 Mar 16th Mens Centex 2025
293 Trinity Win 15-13 942.81 Mar 16th Mens Centex 2025
14 Texas** Loss 3-13 1465.09 Ignored Apr 12th South Texas D I Mens Conferences 2025
90 Texas A&M** Loss 2-13 896.44 Ignored Apr 12th South Texas D I Mens Conferences 2025
332 Texas A&M-B Win 10-8 835.65 Apr 12th South Texas D I Mens Conferences 2025
280 Texas-San Antonio Win 11-9 1026.47 Apr 12th South Texas D I Mens Conferences 2025
305 Sam Houston Loss 6-13 67.7 Apr 13th South Texas D I Mens Conferences 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)