#332 Texas A&M-B (5-12)

avg: 572.98  •  sd: 59.24  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
86 Colorado-B Loss 6-13 909.79 Mar 15th Mens Centex 2025
191 Oklahoma State Loss 7-13 542.87 Mar 15th Mens Centex 2025
245 Texas-Dallas Loss 6-11 354.68 Mar 15th Mens Centex 2025
220 Texas-B Loss 9-11 751.46 Mar 15th Mens Centex 2025
189 Baylor Loss 11-15 726.53 Mar 16th Mens Centex 2025
220 Texas-B Loss 13-15 786.48 Mar 16th Mens Centex 2025
189 Baylor Loss 3-13 507.7 Mar 29th Huckfest 2025
272 Oklahoma Loss 6-13 205.29 Mar 29th Huckfest 2025
305 Sam Houston Win 9-6 1086.27 Mar 29th Huckfest 2025
394 Stephen F Austin Win 12-11 262.93 Mar 29th Huckfest 2025
400 Angelo State Win 15-5 683.15 Mar 30th Huckfest 2025
367 Dallas Win 11-8 711.83 Mar 30th Huckfest 2025
394 Stephen F Austin Win 13-9 556.5 Mar 30th Huckfest 2025
305 Sam Houston Loss 9-13 249.14 Apr 12th South Texas D I Mens Conferences 2025
90 Texas A&M** Loss 4-13 896.44 Ignored Apr 12th South Texas D I Mens Conferences 2025
283 Texas State Loss 8-10 499.9 Apr 12th South Texas D I Mens Conferences 2025
280 Texas-San Antonio Loss 7-9 497.93 Apr 12th South Texas D I Mens Conferences 2025
**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)