#68 Baylor (11-6)

avg: 1454.82  •  sd: 82.21  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
379 Southern Methodist Win 10-6 733.81 Feb 3rd Big D in Little d Open 2018
200 Rice Loss 9-10 807.65 Feb 3rd Big D in Little d Open 2018
351 Texas-Arlington** Win 13-4 963.48 Ignored Feb 3rd Big D in Little d Open 2018
130 North Texas Win 9-7 1471.47 Feb 3rd Big D in Little d Open 2018
184 Texas-San Antonio Win 15-4 1584.1 Feb 3rd Big D in Little d Open 2018
160 Oklahoma Win 15-8 1657.41 Feb 3rd Big D in Little d Open 2018
26 Texas-Dallas Loss 9-14 1255.15 Feb 4th Big D in Little d Open 2018
112 Texas Tech Win 9-8 1410.08 Feb 4th Big D in Little d Open 2018
44 Illinois Loss 8-10 1326.36 Mar 10th Mens Centex 2018
130 North Texas Win 13-6 1792.13 Mar 10th Mens Centex 2018
14 Florida Loss 7-13 1329.28 Mar 10th Mens Centex 2018
82 Oklahoma State Win 11-9 1656.4 Mar 10th Mens Centex 2018
58 Kansas Loss 8-11 1135.25 Mar 10th Mens Centex 2018
40 Iowa Win 13-12 1749.81 Mar 11th Mens Centex 2018
47 Iowa State Win 14-10 1966.95 Mar 11th Mens Centex 2018
56 Temple Win 11-10 1634.6 Mar 11th Mens Centex 2018
26 Texas-Dallas Loss 10-13 1400.88 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)