#197 Texas State (11-11)

avg: 1129.97  •  sd: 64.15  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
229 Baylor Loss 9-10 894.47 Feb 10th Big D in Little D 2024
266 Texas Tech Win 13-12 1013.68 Feb 10th Big D in Little D 2024
380 Texas-Arlington Win 13-6 900.49 Feb 10th Big D in Little D 2024
207 Texas-B Loss 7-13 531.16 Feb 10th Big D in Little D 2024
46 Florida** Loss 1-13 1185.88 Ignored Feb 24th Mardi Gras XXXVI college
83 Indiana Loss 7-12 1060.81 Feb 24th Mardi Gras XXXVI college
266 Texas Tech Win 13-5 1488.68 Feb 24th Mardi Gras XXXVI college
116 LSU Loss 7-10 1041.43 Feb 24th Mardi Gras XXXVI college
167 Texas-San Antonio Loss 5-13 661.11 Feb 25th Mardi Gras XXXVI college
281 Trinity Win 10-6 1323.22 Feb 25th Mardi Gras XXXVI college
228 Oklahoma State Win 8-7 1147.23 Mar 9th Centex Tier 2 2024
281 Trinity Win 9-6 1245.62 Mar 9th Centex Tier 2 2024
228 Oklahoma State Win 15-12 1322.73 Mar 10th Centex Tier 2 2024
211 San Diego State Loss 14-15 955.14 Mar 10th Centex Tier 2 2024
286 Sam Houston Win 12-7 1309.99 Apr 13th South Texas D I Mens Conferences 2024
10 Texas** Loss 3-13 1646.19 Ignored Apr 13th South Texas D I Mens Conferences 2024
207 Texas-B Win 9-8 1213.69 Apr 13th South Texas D I Mens Conferences 2024
128 Houston Loss 4-15 788.32 Apr 14th South Texas D I Mens Conferences 2024
167 Texas-San Antonio Win 15-5 1861.11 Apr 14th South Texas D I Mens Conferences 2024
137 Kansas Loss 7-10 975.68 Apr 27th South Central D I College Mens Regionals 2024
38 Texas A&M** Loss 4-15 1285.46 Ignored Apr 27th South Central D I College Mens Regionals 2024
190 Texas-Dallas Win 10-9 1283.34 Apr 27th South Central D I College Mens 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)