#121 Texas A&M (8-4)

avg: 830.38  •  sd: 64.99  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
147 Sam Houston Win 7-6 748.17 Feb 4th Antifreeze
212 Houston** Win 10-3 464.97 Ignored Feb 4th Antifreeze
182 Texas-San Antonio Win 13-3 875.87 Feb 4th Antifreeze
83 Trinity Loss 5-10 543.28 Feb 4th Antifreeze
147 Sam Houston Win 12-2 1223.17 Feb 5th Antifreeze
112 Rice Loss 11-12 782.62 Feb 5th Antifreeze
207 Northwestern-B** Win 13-3 610.56 Ignored Mar 18th Womens Centex1
83 Trinity Loss 7-10 727.52 Mar 18th Womens Centex1
182 Texas-San Antonio Win 13-7 833.4 Mar 18th Womens Centex1
91 Colorado College Win 14-13 1180.06 Mar 19th Womens Centex1
198 North Texas Win 13-7 645.54 Mar 19th Womens Centex1
70 Northwestern Loss 9-15 723.29 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)