#42 Texas-Dallas (18-6)

avg: 1679.92  •  sd: 68.77  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
113 Saint Louis Win 11-1 1731.28 Feb 17th Dust Bowl 2024
124 Arkansas** Win 11-2 1671.77 Ignored Feb 17th Dust Bowl 2024
96 Iowa State Win 14-1 1853.51 Feb 17th Dust Bowl 2024
177 Missouri State** Win 11-0 1282.97 Ignored Feb 17th Dust Bowl 2024
53 Texas Win 9-4 2161.29 Feb 17th Dust Bowl 2024
133 Truman State** Win 11-0 1581.98 Ignored Feb 17th Dust Bowl 2024
77 Missouri Win 12-7 1889.47 Feb 18th Dust Bowl 2024
47 Southern California Win 12-10 1874.79 Mar 16th Womens Centex 2024
25 Utah Win 12-10 2124.15 Mar 16th Womens Centex 2024
34 Washington University Loss 10-12 1546.03 Mar 16th Womens Centex 2024
27 Brown Loss 6-11 1322.45 Mar 17th Womens Centex 2024
99 Chicago Win 11-6 1766.47 Mar 17th Womens Centex 2024
24 Ohio State Loss 5-13 1293.87 Mar 17th Womens Centex 2024
34 Washington University Loss 10-11 1659.16 Mar 17th Womens Centex 2024
197 Sam Houston** Win 13-2 1067.35 Ignored Apr 13th Texas D I Womens Conferences 2024
149 Texas A&M** Win 12-4 1477.51 Ignored Apr 13th Texas D I Womens Conferences 2024
206 Texas-B** Win 13-1 986.61 Ignored Apr 13th Texas D I Womens Conferences 2024
53 Texas Win 8-7 1686.29 Apr 14th Texas D I Womens Conferences 2024
153 Texas State** Win 12-5 1461.23 Ignored Apr 14th Texas D I Womens Conferences 2024
18 Colorado State Loss 4-11 1471.59 Apr 27th South Central D I College Womens Regionals 2024
213 Houston** Win 14-2 921.58 Ignored Apr 27th South Central D I College Womens Regionals 2024
77 Missouri Win 7-5 1697.1 Apr 27th South Central D I College Womens Regionals 2024
34 Washington University Loss 8-12 1343 Apr 28th South Central D I College Womens Regionals 2024
53 Texas Win 8-6 1861.79 Apr 28th 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)