#182 Texas-San Antonio (5-7)

avg: 275.87  •  sd: 64.64  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
147 Sam Houston Loss 8-9 498.17 Feb 4th Antifreeze
212 Houston Win 7-6 -10.03 Feb 4th Antifreeze
121 Texas A&M Loss 3-13 230.38 Feb 4th Antifreeze
83 Trinity** Loss 4-10 517.18 Ignored Feb 4th Antifreeze
198 North Texas Win 6-2 688.01 Feb 5th Antifreeze
218 Texas-B Win 10-5 154.91 Feb 5th Antifreeze
198 North Texas Loss 8-9 -36.99 Mar 18th Womens Centex1
121 Texas A&M Loss 7-13 272.84 Mar 18th Womens Centex1
83 Trinity** Loss 5-13 517.18 Ignored Mar 18th Womens Centex1
190 Colorado-B Win 8-6 529.68 Mar 19th Womens Centex1
207 Northwestern-B Win 9-7 289.89 Mar 19th Womens Centex1
109 Texas State** Loss 4-15 346.78 Ignored Mar 19th Womens Centex1
**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)