#51 Georgetown (12-7)

avg: 1303.3  •  sd: 79.3  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
104 Virginia Commonwealth Loss 7-8 727.06 Jan 28th Winta Binta Vinta
14 Virginia Loss 5-9 1279.58 Jan 28th Winta Binta Vinta
42 James Madison Loss 7-10 1016.41 Jan 28th Winta Binta Vinta
58 Virginia Tech Win 9-7 1494.9 Jan 28th Winta Binta Vinta
70 American Loss 7-10 716.62 Jan 29th Winta Binta Vinta
130 Liberty** Win 9-3 1230.2 Ignored Jan 29th Winta Binta Vinta
104 Virginia Commonwealth Win 7-3 1452.06 Jan 29th Winta Binta Vinta
94 South Carolina-B Win 8-3 1536.03 Feb 11th Cutlass Classic
111 Charleston Win 10-1 1404.34 Feb 11th Cutlass Classic
36 East Carolina Win 7-5 1789.25 Feb 11th Cutlass Classic
182 Georgetown-B** Win 13-0 771.54 Ignored Feb 11th Cutlass Classic
182 Georgetown-B** Win 13-1 771.54 Ignored Feb 12th Cutlass Classic
36 East Carolina Loss 5-9 932.05 Feb 12th Cutlass Classic
89 Catholic Win 11-6 1499.85 Mar 25th Bonanza 2023
42 James Madison Loss 7-8 1281.08 Mar 25th Bonanza 2023
104 Virginia Commonwealth Win 13-3 1452.06 Mar 25th Bonanza 2023
133 Mary Washington** Win 11-2 1216.99 Ignored Mar 25th Bonanza 2023
58 Virginia Tech Win 12-6 1794.88 Mar 26th Bonanza 2023
42 James Madison Loss 7-11 939.19 Mar 26th Bonanza 2023
**Blowout Eligible

FAQ

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)