#266 Southern Mississippi (2-6)

avg: 653.44  •  sd: 102.3  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
209 Alabama-Birmingham Loss 7-13 342.55 Jan 21st Tupelo Tuneup
89 Mississippi State** Loss 2-13 834.06 Ignored Jan 21st Tupelo Tuneup
269 Harding Win 10-6 1145.69 Jan 21st Tupelo Tuneup
251 Alabama-B Loss 9-11 483.48 Jan 22nd Tupelo Tuneup
284 Memphis Win 13-12 682.6 Jan 22nd Tupelo Tuneup
89 Mississippi State** Loss 1-15 834.06 Ignored Mar 11th 2023 College Huckfest
87 Tennessee-Chattanooga** Loss 4-15 836.6 Ignored Mar 11th 2023 College Huckfest
43 Alabama-Huntsville** Loss 3-15 1103.13 Ignored Mar 12th 2023 College Huckfest
**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)