#206 Texas-B (5-18)

avg: 386.61  •  sd: 75.93  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
197 Sam Houston Loss 4-5 342.35 Feb 17th Antifreeze 2024
213 Houston Loss 3-5 -96.99 Feb 17th Antifreeze 2024
149 Texas A&M Loss 3-11 277.51 Feb 17th Antifreeze 2024
153 Texas State Loss 3-7 261.23 Feb 17th Antifreeze 2024
232 North Texas Loss 4-5 -25.82 Feb 18th Antifreeze 2024
196 Texas-San Antonio Loss 7-15 -113.73 Feb 18th Antifreeze 2024
221 LSU Loss 6-8 -43.85 Mar 16th Womens Centex 2024
90 MIT** Loss 4-13 706.4 Ignored Mar 16th Womens Centex 2024
93 Rice** Loss 1-13 681.27 Ignored Mar 16th Womens Centex 2024
153 Texas State Loss 3-13 261.23 Mar 16th Womens Centex 2024
90 MIT** Loss 0-8 706.4 Ignored Mar 17th Womens Centex 2024
66 Trinity** Loss 4-10 856.11 Ignored Mar 17th Womens Centex 2024
196 Texas-San Antonio Win 7-5 814.41 Mar 17th Womens Centex 2024
197 Sam Houston Loss 7-8 342.35 Apr 13th Texas D I Womens Conferences 2024
149 Texas A&M Loss 3-9 277.51 Apr 13th Texas D I Womens Conferences 2024
42 Texas-Dallas** Loss 1-13 1079.92 Ignored Apr 13th Texas D I Womens Conferences 2024
213 Houston Win 4-3 446.58 Apr 14th Texas D I Womens Conferences 2024
232 North Texas Win 8-6 399.67 Apr 14th Texas D I Womens Conferences 2024
196 Texas-San Antonio Win 7-5 814.41 Apr 14th Texas D I Womens Conferences 2024
213 Houston Win 11-5 921.58 Apr 27th South Central D I College Womens Regionals 2024
77 Missouri** Loss 1-15 768.96 Ignored Apr 27th South Central D I College Womens Regionals 2024
53 Texas** Loss 2-14 961.29 Ignored Apr 27th South Central D I College Womens Regionals 2024
149 Texas A&M Loss 5-7 549.37 Apr 27th South Central D I College Womens Regionals 2024
**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)