#287 Houston (0-13)

avg: -940.65  •  sd: 272.7  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
195 Texas A&M** Loss 1-12 -31.56 Ignored Feb 9th Antifreeze 2019
278 Texas-B Loss 3-7 -940.65 Feb 9th Antifreeze 2019
101 Trinity** Loss 0-15 525.74 Ignored Feb 9th Antifreeze 2019
168 Rice** Loss 1-12 166.54 Ignored Feb 9th Antifreeze 2019
206 Texas-San Antonio** Loss 3-8 -97.61 Ignored Feb 10th Antifreeze 2019
278 Texas-B** Loss 1-6 -940.65 Feb 10th Antifreeze 2019
206 Texas-San Antonio** Loss 0-7 -97.61 Ignored Feb 10th Antifreeze 2019
191 Texas Christian** Loss 1-10 -21.61 Ignored Mar 23rd Womens College Centex 2019
109 Texas State** Loss 0-13 464.56 Ignored Mar 23rd Womens College Centex 2019
132 Boston University** Loss 1-13 379.62 Ignored Mar 23rd Womens College Centex 2019
141 Iowa** Loss 3-11 306.31 Ignored Mar 23rd Womens College Centex 2019
195 Texas A&M** Loss 1-13 -31.56 Ignored Mar 24th Womens College Centex 2019
278 Texas-B** Loss 2-8 -940.65 Mar 24th Womens College Centex 2019
**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)