#186 Texas-San Antonio (5-7)

avg: 407.02  •  sd: 110.19  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
213 North Texas Win 5-2 653.19 Feb 17th Antifreeze 2024
107 Rice Loss 4-9 547.85 Feb 17th Antifreeze 2024
89 Trinity** Loss 2-8 694.64 Ignored Feb 17th Antifreeze 2024
198 Sam Houston Win 8-6 552.22 Feb 18th Antifreeze 2024
201 Houston Win 11-10 342.1 Feb 18th Antifreeze 2024
214 Texas-B Win 15-7 650.33 Feb 18th Antifreeze 2024
33 Central Florida** Loss 3-13 1219.58 Ignored Mar 16th Womens Centex 2024
60 Colorado College** Loss 3-13 918.43 Ignored Mar 16th Womens Centex 2024
113 Denver Loss 7-13 507.29 Mar 16th Womens Centex 2024
213 North Texas Win 13-7 610.73 Mar 16th Womens Centex 2024
153 Texas A&M Loss 4-10 165.22 Mar 17th Womens Centex 2024
214 Texas-B Loss 5-7 -277.81 Mar 17th Womens Centex 2024
**Blowout Eligible


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)