#184 Georgetown-B (1-10)

avg: 263.71  •  sd: 154.73  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
93 South Carolina-B** Loss 1-7 447.36 Feb 11th Cutlass Classic
110 Charleston** Loss 2-7 318.95 Feb 11th Cutlass Classic
51 Georgetown** Loss 0-13 821.83 Ignored Feb 11th Cutlass Classic
37 East Carolina** Loss 1-13 976.17 Ignored Feb 11th Cutlass Classic
51 Georgetown** Loss 1-13 821.83 Ignored Feb 12th Cutlass Classic
30 South Carolina** Loss 2-13 1060.8 Ignored Mar 25th Rodeo 2023
28 Duke** Loss 1-13 1082.04 Ignored Mar 25th Rodeo 2023
215 Elon Win 7-5 80.81 Mar 25th Rodeo 2023
60 Ohio** Loss 3-11 699.34 Ignored Mar 25th Rodeo 2023
71 Massachusetts** Loss 1-13 633.48 Ignored Mar 26th Rodeo 2023
144 North Carolina-B Loss 4-6 295.16 Mar 26th Rodeo 2023
**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)