#140 Arkansas (6-6)

avg: 1043.15  •  sd: 97.67  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
277 Texas-San Antonio Win 9-1 1066.7 Feb 1st Big D in lil d 2020 Open
124 Sul Ross State University Win 13-5 1727.33 Feb 1st Big D in lil d 2020 Open
139 Texas Tech Win 9-8 1168.55 Feb 1st Big D in lil d 2020 Open
139 Texas Tech Win 13-6 1643.55 Feb 2nd Big D in lil d 2020 Open
136 Oklahoma Win 11-10 1188.06 Feb 2nd Big D in lil d 2020 Open
124 Sul Ross State University Loss 13-15 913.15 Feb 2nd Big D in lil d 2020 Open
61 Washington University Loss 6-15 824.4 Feb 22nd Dust Bowl 2020
169 Luther Win 11-9 1214.32 Feb 22nd Dust Bowl 2020
166 Colorado College Loss 10-13 644.72 Feb 22nd Dust Bowl 2020
93 Rice Loss 6-13 660.8 Feb 23rd Dust Bowl 2020
87 Texas State Loss 8-15 721.87 Feb 23rd Dust Bowl 2020
133 Missouri Loss 9-10 967.67 Feb 23rd Dust Bowl 2020
**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)