#250 Mississippi State-C (4-2)

avg: 950.27  •  sd: 69.64  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
303 Alabama-B Win 13-9 1129.68 Jan 20th Starkville Qualifiers
261 Georgia Tech-B Win 9-8 1029.28 Jan 20th Starkville Qualifiers
403 Southern Mississippi** Win 13-1 636.96 Ignored Jan 20th Starkville Qualifiers
231 Harding Loss 10-12 777.19 Jan 21st Starkville Qualifiers
242 Mississippi State -B Loss 11-12 861.06 Jan 21st Starkville Qualifiers
370 LSU-B Win 15-1 958.46 Jan 21st Starkville Qualifiers
**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)