#112 Rice (8-6)

avg: 907.62  •  sd: 93.04  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
198 North Texas Win 7-4 584.17 Feb 4th Antifreeze
23 Texas-Dallas** Loss 3-13 1139.5 Ignored Feb 4th Antifreeze
109 Texas State Loss 5-11 346.78 Feb 4th Antifreeze
218 Texas-B** Win 13-1 181.01 Ignored Feb 4th Antifreeze
121 Texas A&M Win 12-11 955.38 Feb 5th Antifreeze
23 Texas-Dallas** Loss 3-11 1139.5 Ignored Feb 5th Antifreeze
83 Trinity Win 8-7 1242.18 Feb 5th Antifreeze
171 Illinois Win 13-6 1008.98 Mar 18th Womens Centex1
190 Colorado-B** Win 13-1 829.18 Ignored Mar 18th Womens Centex1
179 LSU Win 13-6 947.6 Mar 18th Womens Centex1
109 Texas State Win 13-6 1546.78 Mar 18th Womens Centex1
87 Southern California Loss 8-12 644.93 Mar 19th Womens Centex1
82 Central Florida Loss 7-10 740.46 Mar 19th Womens Centex1
104 Iowa Loss 9-10 841.86 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)