#297 Trinity University (4-8)

avg: 593.55  •  sd: 51.24  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
114 Minnesota-Duluth Loss 7-13 723.54 Mar 10th Mens Centex 2018
82 Oklahoma State** Loss 2-13 807.19 Ignored Mar 10th Mens Centex 2018
130 North Texas Loss 7-13 634.6 Mar 10th Mens Centex 2018
217 Texas Christian Loss 7-10 498.65 Mar 10th Mens Centex 2018
176 Colorado State-B Loss 10-12 788.49 Mar 11th Mens Centex 2018
336 Texas-Dallas-B Loss 10-11 299.3 Mar 11th Mens Centex 2018
387 North Texas-B Win 10-9 308.67 Mar 24th Greatest Crusade IV
379 Southern Methodist Win 11-5 837.65 Mar 24th Greatest Crusade IV
199 Stephen F Austin Loss 9-13 515.91 Mar 24th Greatest Crusade IV
411 Texas State -B Win 13-6 524.42 Mar 24th Greatest Crusade IV
241 Harding Loss 14-15 662.38 Mar 25th Greatest Crusade IV
387 North Texas-B Win 15-8 748.48 Mar 25th Greatest Crusade IV
**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)