#152 Arkansas (4-12)

avg: 1153.2  •  sd: 65.98  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
229 Missouri Win 9-3 1513.85 Mar 10th Dust Bowl 2019
179 Nebraska Loss 10-11 933.84 Mar 10th Dust Bowl 2019
352 Nebraska-Omaha** Win 15-3 1075.83 Ignored Mar 10th Dust Bowl 2019
144 Colorado College Loss 9-14 717.9 Mar 10th Dust Bowl 2019
98 Kansas Loss 8-10 1100.52 Mar 16th Centex 2019 Men
67 Oklahoma State Loss 8-13 1037.8 Mar 16th Centex 2019 Men
40 Dartmouth Loss 4-13 1086.47 Mar 16th Centex 2019 Men
98 Kansas Loss 11-13 1134.34 Mar 17th Centex 2019 Men
80 Oklahoma Loss 11-15 1070.8 Mar 17th Centex 2019 Men
82 Texas State Win 13-9 1861.22 Mar 17th Centex 2019 Men
38 Purdue Loss 2-11 1107.04 Mar 30th Huck Finn XXIII
57 Carnegie Mellon Loss 2-11 987.38 Mar 30th Huck Finn XXIII
68 Cincinnati Loss 5-9 986.31 Mar 31st Huck Finn XXIII
86 Marquette Loss 6-9 1007.51 Mar 31st Huck Finn XXIII
106 Illinois State Win 8-6 1627.83 Mar 31st Huck Finn XXIII
111 Washington University Loss 5-6 1188.46 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)