#217 Texas Christian (12-9)

avg: 888.31  •  sd: 55.28  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
344 Dallas Win 9-5 922.45 Feb 3rd Big D in Little d Open 2018
160 Oklahoma Loss 4-9 492.6 Feb 3rd Big D in Little d Open 2018
199 Stephen F Austin Loss 5-13 334.47 Feb 3rd Big D in Little d Open 2018
112 Texas Tech Loss 6-10 788.92 Feb 3rd Big D in Little d Open 2018
27 Texas State** Loss 1-15 1121.16 Ignored Feb 3rd Big D in Little d Open 2018
170 Kansas State Loss 10-11 934.64 Feb 4th Big D in Little d Open 2018
351 Texas-Arlington Win 9-5 892.54 Feb 4th Big D in Little d Open 2018
114 Minnesota-Duluth Loss 4-13 681.07 Mar 10th Mens Centex 2018
130 North Texas Loss 10-12 954.01 Mar 10th Mens Centex 2018
39 Northwestern** Loss 4-11 1028.7 Ignored Mar 10th Mens Centex 2018
297 Trinity University Win 10-7 983.22 Mar 10th Mens Centex 2018
264 LSU-B Win 9-6 1153.36 Mar 11th Mens Centex 2018
187 Texas A&M-B Loss 6-15 381.47 Mar 11th Mens Centex 2018
336 Texas-Dallas-B Win 12-7 944.82 Mar 11th Mens Centex 2018
284 Tulsa Win 10-9 771.62 Mar 24th Greatest Crusade IV
258 Texas A&M-C Win 13-9 1173.77 Mar 24th Greatest Crusade IV
351 Texas-Arlington Win 13-8 859.64 Mar 24th Greatest Crusade IV
336 Texas-Dallas-B Win 13-5 1024.3 Mar 24th Greatest Crusade IV
212 Baylor University-B Win 15-9 1421.77 Mar 25th Greatest Crusade IV
241 Harding Win 10-9 912.38 Mar 25th Greatest Crusade IV
387 North Texas-B** Win 15-5 783.67 Ignored Mar 25th Greatest Crusade IV
**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)