#58 Kansas (9-9)

avg: 1500.86  •  sd: 79.92  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
5 Washington Loss 7-13 1493.88 Jan 27th Santa Barbara Invitational 2018
111 Arizona State Win 14-13 1414.21 Jan 27th Santa Barbara Invitational 2018
65 California-Santa Barbara Loss 11-13 1233.53 Jan 27th Santa Barbara Invitational 2018
32 California Win 13-8 2191.96 Jan 27th Santa Barbara Invitational 2018
17 Colorado State Loss 5-13 1269.76 Jan 28th Santa Barbara Invitational 2018
79 California-Davis Win 13-8 1910.79 Jan 28th Santa Barbara Invitational 2018
32 California Win 13-11 1924.64 Jan 28th Santa Barbara Invitational 2018
44 Illinois Loss 7-10 1199.36 Mar 10th Mens Centex 2018
68 Baylor Win 11-8 1820.43 Mar 10th Mens Centex 2018
82 Oklahoma State Win 12-6 1986.5 Mar 10th Mens Centex 2018
14 Florida Loss 7-13 1329.28 Mar 10th Mens Centex 2018
160 Oklahoma Win 15-11 1473.76 Mar 11th Mens Centex 2018
39 Northwestern Win 11-10 1753.7 Mar 11th Mens Centex 2018
26 Texas-Dallas Loss 6-14 1129.02 Mar 11th Mens Centex 2018
47 Iowa State Loss 8-13 1072.09 Mar 31st Huck Finn 2018
52 Harvard Win 12-10 1774.14 Mar 31st Huck Finn 2018
11 Emory Loss 8-15 1355.87 Mar 31st Huck Finn 2018
75 Tennessee-Chattanooga Loss 10-13 1087.53 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)