#194 Saint Louis (5-14)

avg: 852  •  sd: 60.26  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
127 Clemson Win 11-10 1259.14 Feb 8th Bulldog Brawl
268 Harding Win 13-7 1093.49 Feb 8th Bulldog Brawl
133 Lipscomb Loss 4-13 514.86 Feb 8th Bulldog Brawl
135 Mississippi State Loss 6-13 511.28 Feb 8th Bulldog Brawl
183 Kennesaw State Loss 8-11 525.13 Feb 9th Bulldog Brawl
135 Mississippi State Loss 13-15 897.1 Feb 9th Bulldog Brawl
72 Southern Illinois-Edwardsville Loss 6-15 796.61 Feb 9th Bulldog Brawl
225 John Brown Loss 8-9 574.24 Feb 22nd Dust Bowl 2025
203 Nebraska Win 13-5 1386.47 Feb 22nd Dust Bowl 2025
287 Oklahoma Win 9-5 953.05 Feb 22nd Dust Bowl 2025
90 Missouri S&T Loss 5-7 967.17 Feb 23rd Dust Bowl 2025
201 Northern Iowa Win 8-4 1375.93 Feb 23rd Dust Bowl 2025
104 Alabama Loss 8-13 742.8 Mar 29th Huck Finn 2025
77 Iowa State Loss 6-14 750.03 Mar 29th Huck Finn 2025
141 Northwestern Loss 8-11 707.25 Mar 29th Huck Finn 2025
63 Notre Dame** Loss 6-15 859.46 Ignored Mar 29th Huck Finn 2025
104 Alabama Loss 8-11 873.35 Mar 30th Huck Finn 2025
150 Kentucky Loss 9-10 927.26 Mar 30th Huck Finn 2025
100 Missouri Loss 2-8 665.35 Mar 30th Huck Finn 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)