#184 Texas-San Antonio (3-8)

avg: 984.1  •  sd: 72.58  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
68 Baylor Loss 4-15 854.82 Feb 3rd Big D in Little d Open 2018
123 Nebraska Loss 8-12 809.28 Feb 3rd Big D in Little d Open 2018
387 North Texas-B** Win 13-1 783.67 Ignored Feb 3rd Big D in Little d Open 2018
26 Texas-Dallas Loss 6-13 1129.02 Feb 3rd Big D in Little d Open 2018
47 Iowa State Loss 4-13 968.24 Mar 10th Mens Centex 2018
160 Oklahoma Loss 6-13 492.6 Mar 10th Mens Centex 2018
200 Rice Win 12-11 1057.65 Mar 10th Mens Centex 2018
199 Stephen F Austin Win 13-7 1492 Mar 10th Mens Centex 2018
47 Iowa State Loss 5-11 968.24 Mar 11th Mens Centex 2018
63 Tulane Loss 9-12 1118.31 Mar 11th Mens Centex 2018
56 Temple Loss 6-15 909.6 Mar 11th Mens Centex 2018
**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)