#200 Rice (7-15)

avg: 932.65  •  sd: 58.24  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
68 Baylor Win 10-9 1579.82 Feb 3rd Big D in Little d Open 2018
130 North Texas Loss 8-9 1067.13 Feb 3rd Big D in Little d Open 2018
351 Texas-Arlington Win 13-7 921.01 Feb 3rd Big D in Little d Open 2018
379 Southern Methodist** Win 13-4 837.65 Ignored Feb 3rd Big D in Little d Open 2018
199 Stephen F Austin Win 14-13 1059.47 Feb 3rd Big D in Little d Open 2018
26 Texas-Dallas** Loss 5-15 1129.02 Ignored Feb 3rd Big D in Little d Open 2018
160 Oklahoma Loss 10-11 967.6 Feb 4th Big D in Little d Open 2018
89 John Brown Loss 6-8 1081.81 Feb 24th Dust Bowl 2018
123 Nebraska Loss 5-10 676.54 Feb 24th Dust Bowl 2018
162 Saint Louis Loss 8-9 954.75 Feb 24th Dust Bowl 2018
112 Texas Tech Loss 6-8 984.59 Feb 24th Dust Bowl 2018
130 North Texas Loss 8-13 695.97 Feb 25th Dust Bowl 2018
156 Colorado-Denver Win 13-11 1335.75 Feb 25th Dust Bowl 2018
187 Texas A&M-B Loss 13-15 767.29 Feb 25th Dust Bowl 2018
162 Saint Louis Loss 7-11 612.86 Feb 25th Dust Bowl 2018
160 Oklahoma Loss 8-13 596.44 Mar 10th Mens Centex 2018
41 Northeastern** Loss 2-13 1003.36 Ignored Mar 10th Mens Centex 2018
184 Texas-San Antonio Loss 11-12 859.1 Mar 10th Mens Centex 2018
199 Stephen F Austin Loss 6-13 334.47 Mar 10th Mens Centex 2018
258 Texas A&M-C Win 15-10 1208.8 Mar 11th Mens Centex 2018
187 Texas A&M-B Win 13-10 1309.61 Mar 11th Mens Centex 2018
176 Colorado State-B Loss 12-13 901.62 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)