#193 North Texas (6-8)

avg: 974.85  •  sd: 76.99  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
278 Baylor Win 9-5 1119.37 Feb 25th Dust Bowl 2023
96 Arkansas Loss 8-9 1296.49 Feb 25th Dust Bowl 2023
156 Wichita State Loss 8-9 1002.46 Feb 25th Dust Bowl 2023
269 Harding Win 9-6 1068.09 Feb 25th Dust Bowl 2023
146 Kansas Loss 5-11 568.89 Feb 26th Dust Bowl 2023
207 Illinois State Win 9-6 1329.43 Feb 26th Dust Bowl 2023
93 Iowa Loss 7-9 1146.95 Feb 26th Dust Bowl 2023
93 Iowa Loss 5-12 826.29 Mar 11th Centex Tier 2
264 Oklahoma State Loss 8-10 418.57 Mar 11th Centex Tier 2
336 Trinity** Win 13-0 814.33 Ignored Mar 11th Centex Tier 2
252 Texas-B Win 12-11 855 Mar 12th Centex Tier 2
143 Colorado-B Loss 3-7 582.73 Mar 12th Centex Tier 2
48 Iowa State Loss 8-15 1081.53 Mar 12th Centex Tier 2
217 Texas-Dallas Win 15-9 1388.44 Mar 12th Centex Tier 2
**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)