#266 Texas Tech (7-13)

avg: 888.68  •  sd: 66.44  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
229 Baylor Loss 10-11 894.47 Feb 10th Big D in Little D 2024
197 Texas State Loss 12-13 1004.97 Feb 10th Big D in Little D 2024
380 Texas-Arlington Win 13-5 900.49 Feb 10th Big D in Little D 2024
207 Texas-B Win 12-10 1326.81 Feb 10th Big D in Little D 2024
46 Florida** Loss 5-13 1185.88 Ignored Feb 24th Mardi Gras XXXVI college
83 Indiana** Loss 3-13 981.32 Ignored Feb 24th Mardi Gras XXXVI college
197 Texas State Loss 5-13 529.97 Feb 24th Mardi Gras XXXVI college
116 LSU Loss 3-13 831.09 Feb 24th Mardi Gras XXXVI college
281 Trinity Loss 7-13 269.53 Feb 25th Mardi Gras XXXVI college
73 Ave Maria** Loss 2-13 1014.71 Ignored Mar 23rd Huckfest 2024
229 Baylor Loss 7-10 629.81 Mar 23rd Huckfest 2024
258 North Texas Win 12-6 1496.42 Mar 23rd Huckfest 2024
380 Texas-Arlington Win 13-5 900.49 Mar 23rd Huckfest 2024
295 Texas A&M-B Win 14-5 1336.36 Mar 24th Huckfest 2024
190 Texas-Dallas Loss 4-13 558.34 Mar 24th Huckfest 2024
229 Baylor Win 8-7 1144.47 Apr 13th North Texas D I Mens Conferences 2024
258 North Texas Win 8-7 1042.11 Apr 13th North Texas D I Mens Conferences 2024
109 Tarleton State Loss 7-15 867.61 Apr 13th North Texas D I Mens Conferences 2024
258 North Texas Loss 9-10 792.11 Apr 14th North Texas D I Mens Conferences 2024
190 Texas-Dallas Loss 8-15 593.53 Apr 14th North Texas D I Mens Conferences 2024
**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)