#167 Texas-San Antonio (9-9)

avg: 1261.11  •  sd: 62.63  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
95 Arkansas Loss 7-13 977.06 Feb 24th Mardi Gras XXXVI college
89 Florida State Loss 8-10 1288.61 Feb 24th Mardi Gras XXXVI college
43 Tulane Loss 7-13 1280.88 Feb 24th Mardi Gras XXXVI college
82 Mississippi State Loss 9-11 1333.7 Feb 25th Mardi Gras XXXVI college
197 Texas State Win 13-5 1729.97 Feb 25th Mardi Gras XXXVI college
370 LSU-B** Win 13-4 958.46 Ignored Feb 25th Mardi Gras XXXVI college
63 Iowa Loss 3-13 1086.69 Mar 9th Centex Tier 2 2024
295 Texas A&M-B Win 13-9 1154.93 Mar 9th Centex Tier 2 2024
207 Texas-B Win 11-10 1213.69 Mar 9th Centex Tier 2 2024
281 Trinity Win 11-5 1427.06 Mar 9th Centex Tier 2 2024
228 Oklahoma State Win 15-8 1587.04 Mar 10th Centex Tier 2 2024
109 Tarleton State Loss 11-15 1086.44 Mar 10th Centex Tier 2 2024
128 Houston Loss 10-11 1263.32 Apr 13th South Texas D I Mens Conferences 2024
38 Texas A&M Loss 8-11 1519.85 Apr 13th South Texas D I Mens Conferences 2024
295 Texas A&M-B Win 12-7 1256.87 Apr 13th South Texas D I Mens Conferences 2024
286 Sam Houston Win 15-9 1304.96 Apr 14th South Texas D I Mens Conferences 2024
197 Texas State Loss 5-15 529.97 Apr 14th South Texas D I Mens Conferences 2024
207 Texas-B Win 13-9 1507.26 Apr 14th South Texas D I Mens Conferences 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)