#251 Alabama-B (11-15)

avg: 732.69  •  sd: 53.68  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
87 Tennessee-Chattanooga** Loss 2-13 836.6 Ignored Jan 21st Tupelo Tuneup
304 Mississippi State-B Win 12-5 1032.02 Jan 21st Tupelo Tuneup
234 Xavier Loss 7-8 648.54 Jan 21st Tupelo Tuneup
347 Mississippi College** Win 12-4 686.61 Ignored Jan 22nd Tupelo Tuneup
266 Southern Mississippi Win 11-9 902.65 Jan 22nd Tupelo Tuneup
269 Harding Win 12-8 1090.68 Jan 23rd Tupelo Tuneup
89 Mississippi State** Loss 4-13 834.06 Ignored Jan 28th T Town Throwdown1
233 Florida-B Loss 7-13 222.38 Jan 28th T Town Throwdown1
259 Jacksonville State Loss 10-13 368.08 Jan 28th T Town Throwdown1
368 North Florida** Win 7-1 600 Ignored Jan 28th T Town Throwdown1
268 Georgia Southern Loss 7-8 526.41 Jan 29th T Town Throwdown1
233 Florida-B Loss 4-13 179.91 Jan 29th T Town Throwdown1
131 Georgia State Loss 5-13 642.58 Jan 29th T Town Throwdown1
269 Harding Loss 6-8 349.03 Feb 18th ‘Ole Muddy Classic
304 Mississippi State-B Win 9-8 557.02 Feb 18th ‘Ole Muddy Classic
209 Alabama-Birmingham Loss 6-10 403.92 Feb 18th ‘Ole Muddy Classic
347 Mississippi College Win 10-5 660.5 Feb 18th ‘Ole Muddy Classic
304 Mississippi State-B Win 13-6 1032.02 Feb 19th ‘Ole Muddy Classic
269 Harding Win 13-9 1068.09 Feb 19th ‘Ole Muddy Classic
209 Alabama-Birmingham Loss 6-8 599.59 Feb 19th ‘Ole Muddy Classic
89 Mississippi State Loss 8-13 937.9 Mar 25th Magic City Invite 2023
141 LSU Loss 4-11 583.87 Mar 25th Magic City Invite 2023
242 Samford Win 13-10 1084.46 Mar 25th Magic City Invite 2023
85 Alabama Loss 6-13 846.99 Mar 26th Magic City Invite 2023
259 Jacksonville State Win 11-7 1163.11 Mar 26th Magic City Invite 2023
141 LSU Loss 5-9 654.81 Mar 26th Magic City Invite 2023
**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)