#77 Texas State (9-4)

avg: 1240.46  •  sd: 103.07  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
218 North Texas** Win 13-5 666.58 Ignored Feb 22nd Big D in lil d 2020 Women
142 Colorado-B Win 10-4 1395 Feb 22nd Big D in lil d 2020 Women
51 Texas-Dallas Loss 5-8 976.37 Feb 22nd Big D in lil d 2020 Women
119 Rice Win 11-5 1542.13 Feb 22nd Big D in lil d 2020 Women
121 Trinity Loss 5-8 474.9 Feb 23rd Big D in lil d 2020 Women
87 Texas A&M Loss 7-9 881.76 Feb 23rd Big D in lil d 2020 Women
149 Texas-B Win 9-6 1140.69 Feb 23rd Big D in lil d 2020 Women
106 Indiana Win 12-3 1643.16 Feb 29th Mardi Gras XXXIII
169 Florida-B** Win 12-4 1134.58 Ignored Feb 29th Mardi Gras XXXIII
141 LSU Win 9-6 1220.37 Feb 29th Mardi Gras XXXIII
78 Central Florida Loss 6-8 936.1 Feb 29th Mardi Gras XXXIII
103 Mississippi State Win 11-3 1654.64 Mar 1st Mardi Gras XXXIII
47 Alabama Win 9-8 1584.81 Mar 1st 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)