#23 Texas Tech (16-2)

avg: 1831.13  •  sd: 75.34  •  top 16/20: 28.7%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
63 Rice Win 13-8 2042.3 Feb 2nd Big D in Little d Open 2019
381 Southern Methodist** Win 13-2 926.65 Ignored Feb 2nd Big D in Little d Open 2019
80 Oklahoma Win 13-9 1870.53 Feb 2nd Big D in Little d Open 2019
92 John Brown Win 15-4 1977.68 Feb 3rd Big D in Little d Open 2019
63 Rice Win 14-11 1859.48 Feb 3rd Big D in Little d Open 2019
194 Kansas State** Win 15-6 1607.61 Ignored Feb 3rd Big D in Little d Open 2019
342 Oklahoma-B** Win 15-3 1124.79 Ignored Feb 3rd Big D in Little d Open 2019
36 Alabama Loss 8-13 1226.98 Mar 2nd Mardi Gras XXXII
65 Florida Win 13-9 1954.31 Mar 2nd Mardi Gras XXXII
161 Sul Ross State Win 15-9 1633.39 Mar 2nd Mardi Gras XXXII
112 Wisconsin-Whitewater Win 13-10 1634.35 Mar 2nd Mardi Gras XXXII
159 Mississippi State Win 13-7 1683.34 Mar 3rd Mardi Gras XXXII
103 Georgia State Win 13-2 1948.38 Mar 3rd Mardi Gras XXXII
18 Michigan Win 10-9 2033.77 Mar 30th Huck Finn XXIII
39 Vermont Win 11-5 2305.77 Mar 30th Huck Finn XXIII
98 Kansas Win 7-4 1859.34 Mar 31st Huck Finn XXIII
38 Purdue Win 10-9 1832.04 Mar 31st Huck Finn XXIII
37 Illinois Loss 7-8 1595.39 Mar 31st Huck Finn XXIII
**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)