#89 John Brown (8-4)

avg: 1382.31  •  sd: 92.76  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
200 Rice Win 8-6 1233.15 Feb 24th Dust Bowl 2018
112 Texas Tech Win 8-7 1410.08 Feb 24th Dust Bowl 2018
123 Nebraska Loss 5-9 721.37 Feb 24th Dust Bowl 2018
162 Saint Louis Win 10-6 1575.91 Feb 24th Dust Bowl 2018
96 Missouri State Win 15-14 1475.5 Feb 25th Dust Bowl 2018
70 Arkansas Loss 8-15 874.72 Feb 25th Dust Bowl 2018
139 Luther Win 14-13 1293.04 Feb 25th Dust Bowl 2018
130 North Texas Loss 6-7 1067.13 Feb 25th Dust Bowl 2018
47 Iowa State Win 12-11 1693.24 Mar 31st Huck Finn 2018
95 Purdue Win 13-9 1771.5 Mar 31st Huck Finn 2018
76 Chicago Loss 13-14 1290.31 Mar 31st Huck Finn 2018
75 Tennessee-Chattanooga Win 15-12 1716.16 Mar 31st Huck Finn 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)