#57 Kansas (13-7)

avg: 1836.54  •  sd: 89.51  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
13 Ohio State Loss 8-13 1908.76 Jan 13th Florida Winter Classic 2018
46 North Carolina-Wilmington Loss 10-12 1740.09 Jan 13th Florida Winter Classic 2018
21 Michigan Loss 10-11 2113.43 Jan 13th Florida Winter Classic 2018
134 Tennessee Win 15-7 1876.04 Jan 14th Florida Winter Classic 2018
46 North Carolina-Wilmington Win 14-13 2103.21 Jan 14th Florida Winter Classic 2018
8 West Chester** Loss 4-15 1905.97 Ignored Jan 14th Florida Winter Classic 2018
92 John Brown Win 8-4 2115.42 Feb 24th Dust Bowl 2018
263 Hendrix** Win 10-0 665.01 Ignored Feb 24th Dust Bowl 2018
149 Arkansas Win 6-3 1733.05 Feb 24th Dust Bowl 2018
234 Kansas State** Win 11-1 1197.66 Ignored Feb 24th Dust Bowl 2018
92 John Brown Win 15-9 2066.1 Feb 25th Dust Bowl 2018
156 Missouri S&T** Win 14-5 1742.48 Ignored Feb 25th Dust Bowl 2018
104 Denver Win 11-8 1847.12 Feb 25th Dust Bowl 2018
79 Chicago Win 12-7 2173.9 Mar 3rd Midwest Throwdown 2018
89 Iowa Win 10-5 2144.37 Mar 3rd Midwest Throwdown 2018
28 Washington University Loss 5-15 1513.89 Mar 3rd Midwest Throwdown 2018
37 Northwestern Loss 3-14 1428.18 Mar 4th Midwest Throwdown 2018
81 Purdue Win 11-6 2183.36 Mar 4th Midwest Throwdown 2018
112 Illinois Loss 8-9 1306.05 Mar 4th Midwest Throwdown 2018
142 North Park Win 9-8 1363.37 Mar 4th Midwest Throwdown 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)