#130 North Texas (16-7)

avg: 1330.22  •  sd: 67.43  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
272 Oklahoma Win 11-5 1405.29 Feb 1st Big D in Little D 2025
275 Texas Tech Win 11-4 1388.22 Feb 1st Big D in Little D 2025
293 Trinity Win 11-7 1195.52 Feb 1st Big D in Little D 2025
394 Stephen F Austin** Win 11-1 737.93 Ignored Feb 1st Big D in Little D 2025
189 Baylor Win 15-10 1561.3 Feb 2nd Big D in Little D 2025
293 Trinity** Win 15-1 1328.63 Ignored Feb 2nd Big D in Little D 2025
367 Dallas** Win 13-1 946.22 Ignored Feb 22nd Dust Bowl 2025
87 Missouri S&T Loss 9-10 1382.58 Feb 22nd Dust Bowl 2025
191 Oklahoma State Win 8-4 1665.21 Feb 22nd Dust Bowl 2025
158 Grinnell Win 9-8 1358.99 Feb 23rd Dust Bowl 2025
194 John Brown Win 12-5 1685.63 Feb 23rd Dust Bowl 2025
57 Oklahoma Christian Loss 7-8 1548.33 Feb 23rd Dust Bowl 2025
225 Arkansas Win 13-5 1576.3 Mar 15th Mens Centex 2025
73 Iowa State Loss 8-13 1105.73 Mar 15th Mens Centex 2025
117 Mississippi State Win 11-6 1921.94 Mar 15th Mens Centex 2025
283 Texas State Win 11-7 1229.46 Mar 15th Mens Centex 2025
120 Arizona State Loss 9-13 947.35 Mar 16th Mens Centex 2025
86 Colorado-B Loss 10-15 1056.18 Mar 16th Mens Centex 2025
96 Missouri Loss 10-15 1023.97 Mar 16th Mens Centex 2025
400 Angelo State** Win 15-2 683.15 Ignored Apr 12th North Texas D I Mens Conferences 2025
275 Texas Tech Win 14-3 1388.22 Apr 12th North Texas D I Mens Conferences 2025
189 Baylor Win 10-7 1497.36 Apr 13th North Texas D I Mens Conferences 2025
183 Tarleton State Loss 8-11 782.77 Apr 13th North Texas D I Mens Conferences 2025
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)