#350 Sam Houston State (3-8)

avg: 482.48  •  sd: 91.6  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
324 Stephen F. Austin Win 11-10 710 Feb 2nd Big D in Little d Open 2019
130 Baylor** Loss 4-12 670.94 Ignored Feb 2nd Big D in Little d Open 2019
363 Texas State -B Loss 9-11 166.13 Feb 2nd Big D in Little d Open 2019
175 North Texas Loss 0-13 467.1 Feb 2nd Big D in Little d Open 2019
67 Oklahoma State** Loss 0-15 933.96 Ignored Feb 3rd Big D in Little d Open 2019
277 Texas-San Antonio Loss 5-12 169.49 Feb 3rd Big D in Little d Open 2019
342 Oklahoma-B Win 6-3 1071.48 Feb 3rd Big D in Little d Open 2019
227 Florida State-B Loss 7-11 448.44 Mar 2nd Mardi Gras XXXII
103 Georgia State** Loss 3-11 748.38 Ignored Mar 2nd Mardi Gras XXXII
161 Sul Ross State Loss 5-11 517.91 Mar 2nd Mardi Gras XXXII
430 Texas A&M-C Win 11-6 482.48 Mar 2nd Mardi Gras XXXII
**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)