#281 Butler (2-5)

avg: 447.86  •  sd: 91.29  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
137 Ball State** Loss 4-13 448.2 Ignored Feb 22nd Music City Tune Up 2020
84 Missouri S&T** Loss 4-13 707.9 Ignored Feb 22nd Music City Tune Up 2020
212 Union (Tennessee) Loss 5-13 204.02 Feb 22nd Music City Tune Up 2020
289 Olivet Nazarene Win 10-8 674.77 Feb 22nd Music City Tune Up 2020
276 Mississippi Win 11-8 843.1 Feb 23rd Music City Tune Up 2020
212 Union (Tennessee) Loss 8-13 307.86 Feb 23rd Music City Tune Up 2020
236 Samford Loss 8-13 215.6 Feb 23rd Music City Tune Up 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)