#278 Texas Tech (7-8)

avg: 768.35  •  sd: 32.59  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
133 North Texas Loss 4-11 713 Feb 1st Big D in Little D 2025
274 Oklahoma Win 9-7 1066.63 Feb 1st Big D in Little D 2025
395 Stephen F Austin Win 9-5 649.9 Feb 1st Big D in Little D 2025
296 Trinity Win 9-8 836.59 Feb 1st Big D in Little D 2025
189 Baylor Loss 11-13 862.4 Feb 2nd Big D in Little D 2025
283 Texas-San Antonio Win 10-9 885.62 Feb 2nd Big D in Little D 2025
296 Trinity Loss 6-7 586.59 Feb 2nd Big D in Little D 2025
158 Grinnell Loss 2-11 620.19 Feb 22nd Dust Bowl 2025
288 Harding Win 8-7 870.34 Feb 22nd Dust Bowl 2025
139 Wichita State Loss 6-9 878.8 Feb 22nd Dust Bowl 2025
245 Texas-Dallas Loss 7-8 761.38 Feb 22nd Dust Bowl 2025
200 Nebraska Loss 7-9 777.19 Feb 23rd Dust Bowl 2025
400 Angelo State Win 14-7 648.77 Apr 12th North Texas D I Mens Conferences 2025
133 North Texas Loss 3-14 713 Apr 12th North Texas D I Mens Conferences 2025
400 Angelo State** Win 15-6 665.88 Ignored 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)