#296 Trinity (7-13)

avg: 711.59  •  sd: 59.83  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
133 North Texas Loss 7-11 846.1 Feb 1st Big D in Little D 2025
274 Oklahoma Win 7-6 912.3 Feb 1st Big D in Little D 2025
395 Stephen F Austin Win 11-5 720.84 Feb 1st Big D in Little D 2025
278 Texas Tech Loss 8-9 643.35 Feb 1st Big D in Little D 2025
133 North Texas** Loss 1-15 713 Ignored Feb 2nd Big D in Little D 2025
287 Texas State Win 12-11 870.81 Feb 2nd Big D in Little D 2025
278 Texas Tech Win 7-6 893.35 Feb 2nd Big D in Little D 2025
224 Arkansas Loss 7-9 680.49 Feb 22nd Mardi Gras XXXVII
233 Georgia Southern Win 11-10 1043.6 Feb 22nd Mardi Gras XXXVII
321 Mississippi Win 11-8 961.16 Feb 22nd Mardi Gras XXXVII
146 South Florida Loss 7-13 711.86 Feb 22nd Mardi Gras XXXVII
130 LSU** Loss 4-13 723.15 Ignored Feb 22nd Mardi Gras XXXVII
224 Arkansas Loss 8-9 834.82 Mar 15th Mens Centex 2025
84 Boston College** Loss 3-13 907.56 Ignored Mar 15th Mens Centex 2025
288 Harding Loss 8-9 620.34 Mar 15th Mens Centex 2025
75 Iowa State** Loss 0-13 982.5 Ignored Mar 15th Mens Centex 2025
287 Texas State Loss 13-15 531.63 Mar 16th Mens Centex 2025
245 Texas-Dallas Win 11-7 1353.27 Mar 16th Mens Centex 2025
368 Dallas Loss 11-12 204.59 Apr 12th Texas D III Mens Conferences 2025
327 Rice Loss 9-13 163.55 Apr 12th Texas D III 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)