#160 Oklahoma (7-15)

avg: 1092.6  •  sd: 55.29  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
68 Baylor Loss 8-15 890.01 Feb 3rd Big D in Little d Open 2018
344 Dallas** Win 13-5 993.39 Ignored Feb 3rd Big D in Little d Open 2018
199 Stephen F Austin Win 9-8 1059.47 Feb 3rd Big D in Little d Open 2018
217 Texas Christian Win 9-4 1488.31 Feb 3rd Big D in Little d Open 2018
112 Texas Tech Loss 10-13 956.94 Feb 3rd Big D in Little d Open 2018
123 Nebraska Loss 11-15 869.27 Feb 4th Big D in Little d Open 2018
200 Rice Win 11-10 1057.65 Feb 4th Big D in Little d Open 2018
41 Northeastern Loss 6-13 1003.36 Feb 16th Warm Up A Florida Affair 2018
10 Virginia Tech** Loss 5-13 1323.3 Ignored Feb 16th Warm Up A Florida Affair 2018
81 Florida State Win 12-10 1646.84 Feb 16th Warm Up A Florida Affair 2018
36 Michigan Loss 5-13 1038.31 Feb 16th Warm Up A Florida Affair 2018
31 LSU Loss 8-12 1258.4 Feb 17th Warm Up A Florida Affair 2018
14 Florida** Loss 5-13 1286.82 Ignored Feb 17th Warm Up A Florida Affair 2018
168 South Florida Loss 13-15 849.81 Feb 18th Warm Up A Florida Affair 2018
42 Connecticut Loss 6-13 995.56 Feb 18th Warm Up A Florida Affair 2018
111 Arizona State Loss 6-14 689.21 Feb 18th Warm Up A Florida Affair 2018
56 Temple Loss 7-10 1119.94 Mar 10th Mens Centex 2018
199 Stephen F Austin Loss 9-11 685.27 Mar 10th Mens Centex 2018
184 Texas-San Antonio Win 13-6 1584.1 Mar 10th Mens Centex 2018
200 Rice Win 13-8 1428.81 Mar 10th Mens Centex 2018
58 Kansas Loss 11-15 1119.7 Mar 11th Mens Centex 2018
112 Texas Tech Loss 12-15 984.59 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)