#205 Alabama-Birmingham (11-11)

avg: 713.34  •  sd: 44.83  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
87 Mississippi State Loss 4-13 660.75 Jan 21st Tupelo Tuneup
260 Southern Mississippi Win 13-7 1023.25 Jan 21st Tupelo Tuneup
275 Memphis Win 9-8 496.92 Jan 21st Tupelo Tuneup
257 Harding Loss 8-9 347.65 Jan 22nd Tupelo Tuneup
85 Tennessee-Chattanooga Loss 3-13 665.17 Jan 22nd Tupelo Tuneup
240 Xavier Win 7-4 1039.94 Jan 22nd Tupelo Tuneup
242 Alabama-B Win 10-6 1028.13 Feb 18th ‘Ole Muddy Classic
329 Mississippi College** Win 11-1 496.9 Ignored Feb 18th ‘Ole Muddy Classic
257 Harding Win 11-6 1019.34 Feb 18th ‘Ole Muddy Classic
292 Mississippi State-B Win 10-6 740.74 Feb 18th ‘Ole Muddy Classic
242 Alabama-B Win 8-6 832.46 Feb 19th ‘Ole Muddy Classic
292 Mississippi State-B Win 13-4 844.58 Feb 19th ‘Ole Muddy Classic
95 Chicago Loss 6-11 668.01 Mar 11th Tally Classic XVII
61 Harvard Loss 6-10 892.3 Mar 11th Tally Classic XVII
113 Clemson Loss 5-13 520.39 Mar 11th Tally Classic XVII
94 Tulane Loss 7-13 661.3 Mar 11th Tally Classic XVII
204 South Florida Loss 11-15 342.22 Mar 12th Tally Classic XVII
253 Georgia Southern Win 14-12 712.96 Mar 12th Tally Classic XVII
233 Jacksonville State Win 13-10 910.68 Mar 25th Magic City Invite 2023
87 Mississippi State Loss 3-13 660.75 Mar 25th Magic City Invite 2023
141 LSU Loss 10-13 678.44 Mar 26th Magic City Invite 2023
227 Samford Loss 11-12 472.91 Mar 26th Magic City Invite 2023
**Blowout Eligible

FAQ

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
  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
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)