#245 Georgia College (4-6)

avg: 647.36  •  sd: 122.95  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
276 Florida-B Win 12-7 1021.5 Mar 16th Southerns 2024
244 Georgia Southern Win 12-9 995.91 Mar 16th Southerns 2024
376 Wisconsin-Eau Claire-B** Win 13-5 248.06 Ignored Mar 16th Southerns 2024
191 Georgia-B Loss 9-10 731.08 Mar 17th Southerns 2024
250 Georgia Tech-B Win 15-4 1227.86 Mar 17th Southerns 2024
95 Wisconsin-Eau Claire Loss 8-15 685.22 Mar 17th Southerns 2024
77 Cedarville** Loss 0-13 755.5 Ignored Mar 23rd Needle in a Ho Stack 2024
216 North Carolina State-B Loss 1-13 160.32 Mar 23rd Needle in a Ho Stack 2024
189 East Carolina Loss 1-12 262.04 Mar 24th Needle in a Ho Stack 2024
271 High Point Loss 5-8 57.49 Mar 24th Needle in a Ho Stack 2024
**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)