#170 Kansas State (13-9)

avg: 1059.64  •  sd: 53.67  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
123 Nebraska Loss 9-15 734.95 Feb 3rd Big D in Little d Open 2018
336 Texas-Dallas-B** Win 13-3 1024.3 Ignored Feb 3rd Big D in Little d Open 2018
287 Central Arkansas Win 13-1 1241.28 Feb 3rd Big D in Little d Open 2018
27 Texas State** Loss 4-12 1121.16 Ignored Feb 3rd Big D in Little d Open 2018
305 Oklahoma-B Win 12-3 1161.22 Feb 3rd Big D in Little d Open 2018
82 Oklahoma State Loss 5-13 807.19 Feb 3rd Big D in Little d Open 2018
130 North Texas Loss 6-13 592.13 Feb 4th Big D in Little d Open 2018
217 Texas Christian Win 11-10 1013.31 Feb 4th Big D in Little d Open 2018
152 Denver Win 7-5 1444.46 Feb 24th Dust Bowl 2018
99 Missouri S&T Loss 12-13 1212.88 Feb 24th Dust Bowl 2018
391 Kansas B-B** Win 11-3 747.05 Ignored Feb 24th Dust Bowl 2018
187 Texas A&M-B Loss 6-9 562.9 Feb 24th Dust Bowl 2018
287 Central Arkansas Win 5-0 1241.28 Feb 24th Dust Bowl 2018
387 North Texas-B** Win 15-0 783.67 Ignored Feb 24th Dust Bowl 2018
284 Tulsa Win 15-3 1246.62 Feb 25th Dust Bowl 2018
287 Central Arkansas Win 9-2 1241.28 Feb 25th Dust Bowl 2018
348 Iowa State-B** Win 15-4 969.71 Ignored Mar 3rd Midwest Throwdown 2018
99 Missouri S&T Win 15-14 1462.88 Mar 3rd Midwest Throwdown 2018
363 Wisconsin-Oshkosh** Win 15-3 932.91 Ignored Mar 3rd Midwest Throwdown 2018
51 Ohio State Loss 5-15 937.69 Mar 4th Midwest Throwdown 2018
105 Wisconsin-Milwaukee Loss 10-11 1192.43 Mar 4th Midwest Throwdown 2018
190 Northern Iowa Loss 14-15 850.78 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)