#98 Kansas (8-12)

avg: 1363.18  •  sd: 45.93  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
48 Kennesaw State Loss 10-12 1408.36 Feb 8th Florida Warm Up 2019
150 Cornell Win 12-9 1523.45 Feb 8th Florida Warm Up 2019
73 Temple Loss 12-13 1355.87 Feb 8th Florida Warm Up 2019
7 Carleton College-CUT** Loss 5-13 1518.64 Ignored Feb 9th Florida Warm Up 2019
65 Florida Loss 5-13 935.75 Feb 9th Florida Warm Up 2019
106 Illinois State Win 11-10 1452.34 Feb 9th Florida Warm Up 2019
68 Cincinnati Win 8-7 1640.37 Feb 9th Florida Warm Up 2019
54 Virginia Tech Loss 11-12 1494.44 Feb 10th Florida Warm Up 2019
150 Cornell Win 15-6 1778.08 Feb 10th Florida Warm Up 2019
40 Dartmouth Loss 9-13 1267.9 Mar 16th Centex 2019 Men
67 Oklahoma State Loss 9-12 1188.6 Mar 16th Centex 2019 Men
152 Arkansas Win 10-8 1415.87 Mar 16th Centex 2019 Men
82 Texas State Loss 11-13 1213.81 Mar 17th Centex 2019 Men
80 Oklahoma Loss 11-13 1223.13 Mar 17th Centex 2019 Men
152 Arkansas Win 13-11 1382.04 Mar 17th Centex 2019 Men
164 Arizona State Win 11-7 1570.12 Mar 30th Huck Finn XXIII
131 Chicago Win 11-8 1632.1 Mar 30th Huck Finn XXIII
23 Texas Tech Loss 4-7 1334.97 Mar 31st Huck Finn XXIII
57 Carnegie Mellon Loss 5-9 1058.32 Mar 31st Huck Finn XXIII
39 Vermont Loss 3-7 1105.77 Mar 31st Huck Finn XXIII
**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)