#72 Texas-Dallas (11-6)

avg: 1326.4  •  sd: 54.52  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
109 Texas State Win 11-10 1189.56 Feb 16th Big D in lil d Women
237 North Texas** Win 11-1 859.68 Ignored Feb 16th Big D in lil d Women
189 Tulane** Win 11-2 1193.98 Ignored Feb 16th Big D in lil d Women
120 Arizona State Win 12-7 1547.68 Feb 17th Big D in lil d Women
173 Baylor Win 11-7 1189.21 Feb 17th Big D in lil d Women
102 LSU Win 12-8 1560.59 Feb 17th Big D in lil d Women
137 Illinois Win 10-5 1510.54 Mar 2nd Midwest Throwdown 2019
181 Arkansas** Win 10-1 1229.19 Ignored Mar 2nd Midwest Throwdown 2019
207 Wisconsin-Eau Claire** Win 9-3 1098.84 Ignored Mar 2nd Midwest Throwdown 2019
37 Washington University Loss 8-10 1407.89 Mar 2nd Midwest Throwdown 2019
90 Colorado State Loss 8-9 1092.13 Mar 23rd Womens College Centex 2019
40 Michigan Loss 9-10 1444.43 Mar 23rd Womens College Centex 2019
30 Utah Loss 6-13 1158.87 Mar 23rd Womens College Centex 2019
90 Colorado State Win 9-7 1496.46 Mar 24th Womens College Centex 2019
99 MIT Win 14-11 1440.81 Mar 24th Womens College Centex 2019
59 Duke Loss 5-11 845.96 Mar 24th Womens College Centex 2019
43 Georgia Tech Loss 8-9 1430.59 Mar 24th Womens College Centex 2019
**Blowout Eligible

FAQ

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)