#268 Harding (8-18)

avg: 535.96  •  sd: 61.39  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
104 Alabama** Loss 0-13 638.96 Ignored Jan 25th T Town Throwdown XX
373 Tennessee-Chattanooga -B Win 13-9 327.95 Jan 25th T Town Throwdown XX
160 LSU Loss 0-13 392.08 Jan 25th T Town Throwdown XX
250 Illinois State Loss 6-13 12.58 Jan 25th T Town Throwdown XX
110 Berry** Loss 2-13 607.55 Ignored Jan 26th T Town Throwdown XX
269 Jacksonville State Loss 6-15 -64.7 Jan 26th T Town Throwdown XX
373 Tennessee-Chattanooga -B** Win 6-0 509.38 Ignored Jan 26th T Town Throwdown XX
194 Saint Louis Loss 7-13 294.47 Feb 8th Bulldog Brawl
135 Mississippi State Loss 3-15 511.28 Feb 8th Bulldog Brawl
133 Lipscomb Loss 5-13 514.86 Feb 8th Bulldog Brawl
127 Clemson Loss 6-14 534.14 Feb 9th Bulldog Brawl
308 Mississippi State-B Win 15-5 937.25 Feb 9th Bulldog Brawl
185 Union (Tennessee) Loss 3-15 278.92 Feb 9th Bulldog Brawl
155 Grinnell Loss 5-9 490.83 Feb 22nd Dust Bowl 2025
169 Kansas Loss 3-9 360.46 Feb 22nd Dust Bowl 2025
267 Texas Tech Loss 7-8 417.88 Feb 22nd Dust Bowl 2025
148 Wichita State Loss 7-12 534.29 Feb 22nd Dust Bowl 2025
363 Dallas Win 10-5 552.15 Feb 23rd Dust Bowl 2025
323 Kansas State Loss 7-10 -120.13 Feb 23rd Dust Bowl 2025
211 Baylor Win 13-5 1357.43 Mar 15th Mens Centex 2025
77 Iowa State** Loss 4-13 750.03 Ignored Mar 15th Mens Centex 2025
270 Texas State Win 9-8 649.53 Mar 15th Mens Centex 2025
258 Trinity Win 9-8 704.39 Mar 15th Mens Centex 2025
98 Boston College** Loss 5-15 671.7 Ignored Mar 16th Mens Centex 2025
107 Iowa Loss 12-15 921.87 Mar 16th Mens Centex 2025
227 Tarleton State Win 15-13 909.47 Mar 16th Mens Centex 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)