#123 Nebraska (12-7)

avg: 1250.43  •  sd: 86.52  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
184 Texas-San Antonio Win 12-8 1425.26 Feb 3rd Big D in Little d Open 2018
387 North Texas-B** Win 13-0 783.67 Ignored Feb 3rd Big D in Little d Open 2018
26 Texas-Dallas Loss 4-11 1129.02 Feb 3rd Big D in Little d Open 2018
170 Kansas State Win 15-9 1575.12 Feb 3rd Big D in Little d Open 2018
27 Texas State Loss 5-15 1121.16 Feb 3rd Big D in Little d Open 2018
82 Oklahoma State Win 13-11 1636.03 Feb 4th Big D in Little d Open 2018
160 Oklahoma Win 15-11 1473.76 Feb 4th Big D in Little d Open 2018
89 John Brown Win 9-5 1911.37 Feb 24th Dust Bowl 2018
200 Rice Win 10-5 1506.55 Feb 24th Dust Bowl 2018
162 Saint Louis Win 10-5 1653.65 Feb 24th Dust Bowl 2018
112 Texas Tech Loss 5-9 756.02 Feb 24th Dust Bowl 2018
139 Luther Win 15-10 1621.64 Feb 25th Dust Bowl 2018
156 Colorado-Denver Win 14-8 1642.95 Feb 25th Dust Bowl 2018
82 Oklahoma State Loss 9-13 988.63 Feb 25th Dust Bowl 2018
130 North Texas Loss 10-13 863.99 Feb 25th Dust Bowl 2018
118 Wisconsin-Whitewater Loss 6-13 664.82 Mar 31st Illinois Invite 2018
190 Northern Iowa Win 11-9 1224.99 Mar 31st Illinois Invite 2018
246 Winona State Loss 10-11 654.81 Mar 31st Illinois Invite 2018
201 Wisconsin-Eau Claire Win 12-9 1278 Mar 31st Illinois Invite 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)