#163 Alabama (7-7)

avg: 640.88  •  sd: 92.12  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
155 Berry Loss 6-10 181.27 Jan 25th T Town Throwdown XX
209 Vanderbilt Loss 7-9 1.48 Jan 25th T Town Throwdown XX
198 Harding Loss 4-5 329.21 Jan 25th T Town Throwdown XX
155 Berry Loss 6-10 181.27 Jan 26th T Town Throwdown XX
189 LSU Win 11-3 1090.29 Feb 22nd Mardi Gras XXXVII
247 Alabama-Birmingham Win 7-3 616.96 Feb 22nd Mardi Gras XXXVII
257 Mississippi State** Win 13-0 339.61 Ignored Feb 22nd Mardi Gras XXXVII
233 Auburn Win 12-0 724.76 Feb 22nd Mardi Gras XXXVII
32 Ohio Loss 6-13 1071.76 Mar 22nd Moxie Madness 2025
155 Berry Loss 7-8 552.43 Mar 22nd Moxie Madness 2025
233 Auburn Win 11-2 724.76 Mar 22nd Moxie Madness 2025
154 Xavier Loss 8-10 414.81 Mar 23rd Moxie Madness 2025
209 Vanderbilt Win 7-3 880.82 Mar 23rd Moxie Madness 2025
203 Tennessee-Chattanooga Win 8-1 995.01 Mar 23rd Moxie Madness 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)