#80 Oklahoma (14-12)

avg: 1451.97  •  sd: 69.96  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
63 Rice Loss 10-11 1421.14 Feb 2nd Big D in Little d Open 2019
381 Southern Methodist** Win 13-0 926.65 Ignored Feb 2nd Big D in Little d Open 2019
23 Texas Tech Loss 9-13 1412.57 Feb 2nd Big D in Little d Open 2019
92 John Brown Loss 9-12 1032.31 Feb 3rd Big D in Little d Open 2019
63 Rice Loss 8-12 1104.99 Feb 3rd Big D in Little d Open 2019
130 Baylor Win 15-10 1724.54 Feb 3rd Big D in Little d Open 2019
144 Colorado College Win 12-4 1791.77 Feb 3rd Big D in Little d Open 2019
69 Emory Loss 8-12 1067.3 Feb 8th Florida Warm Up 2019
43 Harvard Loss 9-13 1253.71 Feb 8th Florida Warm Up 2019
106 Illinois State Win 10-9 1452.34 Feb 8th Florida Warm Up 2019
136 South Florida Win 13-5 1837.03 Feb 9th Florida Warm Up 2019
68 Cincinnati Win 13-7 2072.91 Feb 9th Florida Warm Up 2019
49 Northwestern Win 15-13 1851.87 Feb 9th Florida Warm Up 2019
22 Georgia Loss 6-10 1338.33 Feb 9th Florida Warm Up 2019
72 Alabama-Huntsville Loss 11-13 1255.15 Feb 10th Florida Warm Up 2019
69 Emory Loss 11-13 1279.62 Feb 10th Florida Warm Up 2019
92 John Brown Win 15-8 1942.49 Mar 10th Dust Bowl 2019
244 Colorado-B Win 12-8 1318.35 Mar 10th Dust Bowl 2019
284 Dallas Baptist Win 15-10 1188.75 Mar 10th Dust Bowl 2019
67 Oklahoma State Win 15-10 1987.57 Mar 10th Dust Bowl 2019
46 Iowa State Loss 8-13 1163.07 Mar 16th Centex 2019 Men
76 Utah Loss 7-12 953.21 Mar 16th Centex 2019 Men
82 Texas State Loss 6-13 842.65 Mar 16th Centex 2019 Men
98 Kansas Win 13-11 1592.02 Mar 17th Centex 2019 Men
152 Arkansas Win 15-11 1534.36 Mar 17th Centex 2019 Men
82 Texas State Win 13-8 1938.81 Mar 17th Centex 2019 Men
**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)