#381 Southern Methodist (3-8)

avg: 326.65  •  sd: 127.47  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
63 Rice** Loss 2-13 946.14 Ignored Feb 2nd Big D in Little d Open 2019
23 Texas Tech** Loss 2-13 1231.13 Ignored Feb 2nd Big D in Little d Open 2019
80 Oklahoma** Loss 0-13 851.97 Ignored Feb 2nd Big D in Little d Open 2019
394 North Texas-B Win 11-10 385.18 Feb 3rd Big D in Little d Open 2019
324 Stephen F. Austin Loss 7-12 64.49 Feb 3rd Big D in Little d Open 2019
409 Texas-Dallas-B Win 11-7 630 Feb 3rd Big D in Little d Open 2019
284 Dallas Baptist Loss 8-11 369.54 Mar 24th Greatest Crusade V
398 Texas-Arlington Win 13-8 724.47 Mar 24th Greatest Crusade V
212 Texas Christian Loss 6-12 371.31 Mar 24th Greatest Crusade V
394 North Texas-B Loss 10-11 135.18 Mar 24th Greatest Crusade V
354 Dallas Loss 8-13 -27.2 Mar 24th Greatest Crusade V
**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)