#218 North Texas (0-11)

avg: 66.58  •  sd: 129.78  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
87 Texas A&M** Loss 1-13 561.1 Ignored Feb 22nd Big D in lil d 2020 Women
142 Colorado-B** Loss 1-11 195 Ignored Feb 22nd Big D in lil d 2020 Women
77 Texas State** Loss 5-13 640.46 Ignored Feb 22nd Big D in lil d 2020 Women
51 Texas-Dallas** Loss 4-13 829.97 Ignored Feb 22nd Big D in lil d 2020 Women
179 Baylor Loss 8-11 111.63 Feb 23rd Big D in lil d 2020 Women
190 Arkansas Loss 7-9 108.38 Feb 23rd Big D in lil d 2020 Women
119 Rice** Loss 5-13 342.13 Ignored Feb 23rd Big D in lil d 2020 Women
160 St Benedict Loss 6-13 -7.32 Feb 29th Mardi Gras XXXIII
103 Mississippi State** Loss 1-12 454.64 Ignored Feb 29th Mardi Gras XXXIII
47 Alabama** Loss 2-13 859.81 Ignored Feb 29th Mardi Gras XXXIII
165 Tennessee-Chattanooga Loss 6-10 60.96 Feb 29th Mardi Gras XXXIII
**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)