#329 Mississippi College (0-11)

avg: -103.1  •  sd: 84.8  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
292 Mississippi State-B Loss 3-13 -355.42 Jan 21st Tupelo Tuneup
240 Xavier Loss 7-10 154.11 Jan 21st Tupelo Tuneup
85 Tennessee-Chattanooga** Loss 1-13 665.17 Ignored Jan 21st Tupelo Tuneup
242 Alabama-B** Loss 4-12 -68.03 Ignored Jan 22nd Tupelo Tuneup
257 Harding Loss 1-7 -127.35 Jan 22nd Tupelo Tuneup
275 Memphis Loss 10-13 43.78 Jan 23rd Tupelo Tuneup
242 Alabama-B Loss 5-10 -41.93 Feb 18th ‘Ole Muddy Classic
205 Alabama-Birmingham** Loss 1-11 113.34 Ignored Feb 18th ‘Ole Muddy Classic
257 Harding Loss 7-11 5.76 Feb 18th ‘Ole Muddy Classic
292 Mississippi State-B Loss 5-11 -355.42 Feb 18th ‘Ole Muddy Classic
257 Harding Loss 4-13 -127.35 Feb 19th ‘Ole Muddy Classic
**Blowout Eligible


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)