#112 Texas Tech (12-11)

avg: 1285.08  •  sd: 48.51  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
344 Dallas** Win 13-4 993.39 Ignored Feb 3rd Big D in Little d Open 2018
160 Oklahoma Win 13-10 1420.74 Feb 3rd Big D in Little d Open 2018
82 Oklahoma State Win 15-11 1788.36 Feb 3rd Big D in Little d Open 2018
199 Stephen F Austin Win 12-8 1375.63 Feb 3rd Big D in Little d Open 2018
217 Texas Christian Win 10-6 1384.47 Feb 3rd Big D in Little d Open 2018
68 Baylor Loss 8-9 1329.82 Feb 4th Big D in Little d Open 2018
287 Central Arkansas** Win 15-4 1241.28 Ignored Feb 4th Big D in Little d Open 2018
27 Texas State Loss 9-15 1205.68 Feb 4th Big D in Little d Open 2018
89 John Brown Loss 7-8 1257.31 Feb 24th Dust Bowl 2018
123 Nebraska Win 9-5 1779.49 Feb 24th Dust Bowl 2018
200 Rice Win 8-6 1233.15 Feb 24th Dust Bowl 2018
162 Saint Louis Win 9-6 1498.32 Feb 24th Dust Bowl 2018
70 Arkansas Loss 9-14 965.66 Feb 25th Dust Bowl 2018
139 Luther Win 15-11 1549.2 Feb 25th Dust Bowl 2018
82 Oklahoma State Loss 10-11 1282.19 Feb 25th Dust Bowl 2018
40 Iowa Loss 7-12 1104.3 Mar 10th Mens Centex 2018
114 Minnesota-Duluth Win 11-10 1406.07 Mar 10th Mens Centex 2018
31 LSU Loss 6-12 1120.25 Mar 10th Mens Centex 2018
39 Northwestern Loss 6-13 1028.7 Mar 10th Mens Centex 2018
27 Texas State Loss 7-11 1254.26 Mar 10th Mens Centex 2018
160 Oklahoma Win 15-12 1393.09 Mar 11th Mens Centex 2018
130 North Texas Loss 8-11 826.52 Mar 11th Mens Centex 2018
41 Northeastern Loss 6-10 1107.2 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)