#26 Texas-Dallas (18-4)

avg: 1729.02  •  sd: 72.94  •  top 16/20: 12.3%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
123 Nebraska Win 11-4 1850.43 Feb 3rd Big D in Little d Open 2018
387 North Texas-B** Win 13-0 783.67 Ignored Feb 3rd Big D in Little d Open 2018
200 Rice** Win 15-5 1532.65 Ignored Feb 3rd Big D in Little d Open 2018
351 Texas-Arlington** Win 15-1 963.48 Ignored Feb 3rd Big D in Little d Open 2018
184 Texas-San Antonio Win 13-6 1584.1 Feb 3rd Big D in Little d Open 2018
68 Baylor Win 14-9 1928.69 Feb 4th Big D in Little d Open 2018
27 Texas State Loss 11-13 1492.32 Feb 4th Big D in Little d Open 2018
141 Boston College Win 9-8 1291.16 Feb 10th Stanford Open 2018
121 Puget Sound Win 10-7 1646.58 Feb 10th Stanford Open 2018
129 Claremont Win 12-7 1716.37 Feb 10th Stanford Open 2018
35 Air Force Win 12-9 1984.94 Feb 11th Stanford Open 2018
59 Santa Clara Win 12-11 1624.77 Feb 11th Stanford Open 2018
32 California Loss 8-11 1330.19 Feb 11th Stanford Open 2018
53 UCLA Win 12-8 1975.57 Feb 11th Stanford Open 2018
47 Iowa State Win 13-12 1693.24 Mar 10th Mens Centex 2018
17 Colorado State Loss 7-10 1480.09 Mar 10th Mens Centex 2018
63 Tulane Win 12-3 2063.68 Mar 10th Mens Centex 2018
21 Texas A&M Loss 8-11 1456.45 Mar 10th Mens Centex 2018
68 Baylor Win 13-10 1782.96 Mar 11th Mens Centex 2018
58 Kansas Win 14-6 2100.86 Mar 11th Mens Centex 2018
130 North Texas Win 15-6 1792.13 Mar 11th Mens Centex 2018
41 Northeastern Win 14-11 1916.7 Mar 11th Mens Centex 2018
**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)