#308 Sam Houston (7-9)

avg: 651.31  •  sd: 89.47  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
280 Jacksonville State Loss 8-11 400.48 Feb 22nd Mardi Gras XXXVII
321 Mississippi Win 10-7 985.21 Feb 22nd Mardi Gras XXXVII
325 Mississippi State-B Loss 7-8 461.73 Feb 22nd Mardi Gras XXXVII
375 LSU-B Win 12-9 620.98 Feb 22nd Mardi Gras XXXVII
189 Baylor Loss 1-13 491.24 Mar 29th Huckfest 2025
274 Oklahoma Win 10-9 912.3 Mar 29th Huckfest 2025
395 Stephen F Austin Win 13-2 720.84 Mar 29th Huckfest 2025
336 Texas A&M-B Loss 6-9 137.88 Mar 29th Huckfest 2025
327 Rice Win 9-8 707.11 Mar 30th Huckfest 2025
219 Texas-B Loss 4-15 385.24 Mar 30th Huckfest 2025
283 Texas-San Antonio Loss 9-11 511.41 Apr 12th South Texas D I Mens Conferences 2025
336 Texas A&M-B Win 13-9 975.01 Apr 12th South Texas D I Mens Conferences 2025
92 Texas A&M** Loss 5-13 877.88 Ignored Apr 12th South Texas D I Mens Conferences 2025
14 Texas** Loss 0-13 1442.23 Ignored Apr 12th South Texas D I Mens Conferences 2025
287 Texas State Win 13-6 1345.81 Apr 13th South Texas D I Mens Conferences 2025
283 Texas-San Antonio Loss 10-15 307.02 Apr 13th 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)