#104 Alabama (14-10)

avg: 1238.96  •  sd: 62.49  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
160 LSU Win 13-6 1592.08 Jan 25th T Town Throwdown XX
110 Berry Loss 12-13 1082.55 Jan 25th T Town Throwdown XX
268 Harding** Win 13-0 1135.96 Ignored Jan 25th T Town Throwdown XX
373 Tennessee-Chattanooga -B** Win 13-0 509.38 Ignored Jan 25th T Town Throwdown XX
250 Illinois State** Win 13-4 1212.58 Ignored Jan 26th T Town Throwdown XX
172 Alabama-Birmingham Win 15-10 1403.81 Jan 26th T Town Throwdown XX
135 Mississippi State Win 15-12 1411.77 Jan 26th T Town Throwdown XX
110 Berry Loss 12-15 907.06 Jan 26th T Town Throwdown XX
27 South Carolina Loss 9-13 1361.03 Feb 15th Queen City Tune Up 2025
37 North Carolina-Wilmington Win 13-11 1863.91 Feb 15th Queen City Tune Up 2025
75 Carnegie Mellon Loss 9-13 952.68 Feb 15th Queen City Tune Up 2025
101 Yale Loss 7-8 1137.48 Feb 16th Queen City Tune Up 2025
65 Tennessee Loss 7-9 1174.2 Feb 16th Queen City Tune Up 2025
232 Georgia Southern Win 13-2 1278.26 Feb 22nd Mardi Gras XXXVII
209 Arkansas Win 13-6 1362.15 Feb 22nd Mardi Gras XXXVII
79 Florida Loss 10-13 1019.74 Feb 22nd Mardi Gras XXXVII
94 Tennessee-Chattanooga Win 13-12 1404.56 Feb 22nd Mardi Gras XXXVII
194 Saint Louis Win 13-8 1348.16 Mar 29th Huck Finn 2025
63 Notre Dame Loss 7-14 876.57 Mar 29th Huck Finn 2025
77 Iowa State Loss 7-12 829.52 Mar 29th Huck Finn 2025
72 Southern Illinois-Edwardsville Loss 6-15 796.61 Mar 29th Huck Finn 2025
194 Saint Louis Win 11-8 1217.61 Mar 30th Huck Finn 2025
135 Mississippi State Win 11-6 1657.98 Mar 30th Huck Finn 2025
90 Missouri S&T Win 9-7 1574.65 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)