#198 North Texas (4-8)

avg: -25.24  •  sd: 130.3  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
217 Texas-B Win 8-1 65.84 Feb 4th Antifreeze
22 Texas-Dallas** Loss 4-10 1023.85 Ignored Feb 4th Antifreeze
112 Rice Loss 4-7 305.26 Feb 4th Antifreeze
106 Texas State** Loss 1-12 239.68 Ignored Feb 4th Antifreeze
210 Houston Win 8-4 314.65 Feb 5th Antifreeze
183 Texas-San Antonio Loss 2-6 -441.02 Feb 5th Antifreeze
203 Northwestern-B Win 8-6 205.29 Mar 18th Womens Centex1
183 Texas-San Antonio Win 9-8 283.98 Mar 18th Womens Centex1
81 Trinity** Loss 3-10 398.62 Ignored Mar 18th Womens Centex1
175 LSU Loss 6-10 -266.03 Mar 19th Womens Centex1
120 Texas A&M Loss 7-13 153.01 Mar 19th Womens Centex1
203 Northwestern-B Loss 2-13 -695.2 Mar 19th Womens Centex1
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)