#395 Stephen F Austin (1-15)

avg: 120.84  •  sd: 62.19  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
133 North Texas** Loss 1-11 713 Ignored Feb 1st Big D in Little D 2025
274 Oklahoma Loss 5-11 187.3 Feb 1st Big D in Little D 2025
296 Trinity Loss 5-11 111.59 Feb 1st Big D in Little D 2025
278 Texas Tech Loss 5-9 239.29 Feb 1st Big D in Little D 2025
274 Oklahoma** Loss 4-15 187.3 Ignored Feb 2nd Big D in Little D 2025
327 Rice Loss 1-15 -17.89 Feb 2nd Big D in Little D 2025
189 Baylor** Loss 2-13 491.24 Ignored Mar 29th Huckfest 2025
274 Oklahoma** Loss 5-13 187.3 Ignored Mar 29th Huckfest 2025
308 Sam Houston Loss 2-13 51.31 Mar 29th Huckfest 2025
336 Texas A&M-B Loss 11-12 431.45 Mar 29th Huckfest 2025
400 Angelo State Loss 10-11 -59.12 Mar 30th Huckfest 2025
368 Dallas Loss 4-8 -235.22 Mar 30th Huckfest 2025
336 Texas A&M-B Loss 9-13 137.88 Mar 30th Huckfest 2025
189 Baylor** Loss 5-15 491.24 Ignored Apr 12th North Texas D I Mens Conferences 2025
245 Texas-Dallas** Loss 4-12 286.38 Ignored Apr 12th North Texas D I Mens Conferences 2025
400 Angelo State Win 13-11 294.72 Apr 13th North 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)