#254 Alabama-B (9-11)

avg: 370.08  •  sd: 55.95  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
106 Tennessee-Chattanooga** Loss 2-13 528.72 Ignored Jan 21st Tupelo Tuneup
284 Mississippi State-B Win 12-5 729.4 Jan 21st Tupelo Tuneup
232 Xavier Loss 7-8 347.01 Jan 21st Tupelo Tuneup
306 Mississippi College Win 12-4 383.55 Jan 22nd Tupelo Tuneup
259 Southern Mississippi Win 11-9 585.73 Jan 22nd Tupelo Tuneup
255 Harding Win 12-8 808.28 Jan 23rd Tupelo Tuneup
117 Mississippi State** Loss 4-13 475.61 Ignored Jan 28th T Town Throwdown1
221 Florida-B Loss 7-13 -15.84 Jan 28th T Town Throwdown1
225 Jacksonville State Loss 10-13 198.64 Jan 28th T Town Throwdown1
308 North Florida Win 7-1 370.08 Jan 28th T Town Throwdown1
247 Georgia Southern Loss 7-8 288.37 Jan 29th T Town Throwdown1
221 Florida-B Loss 4-13 -58.31 Jan 29th T Town Throwdown1
135 Georgia State** Loss 5-13 401.81 Ignored Jan 29th T Town Throwdown1
255 Harding Loss 6-8 66.63 Feb 18th ‘Ole Muddy Classic
284 Mississippi State-B Win 9-8 254.4 Feb 18th ‘Ole Muddy Classic
211 Alabama-Birmingham Loss 6-10 111.93 Feb 18th ‘Ole Muddy Classic
306 Mississippi College Win 10-5 357.44 Feb 18th ‘Ole Muddy Classic
284 Mississippi State-B Win 13-6 729.4 Feb 19th ‘Ole Muddy Classic
255 Harding Win 13-9 785.69 Feb 19th ‘Ole Muddy Classic
211 Alabama-Birmingham Loss 6-8 307.6 Feb 19th ‘Ole Muddy Classic
**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)