#88 Georgetown (8-14)

avg: 1578.62  •  sd: 71.06  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
80 James Madison Loss 3-9 1047.06 Jan 27th Winta Binta Vinta Fest 2018
245 George Mason University** Win 13-1 1094.17 Ignored Jan 27th Winta Binta Vinta Fest 2018
215 Virginia-B Win 9-5 1305.42 Jan 27th Winta Binta Vinta Fest 2018
46 North Carolina-Wilmington Loss 3-8 1378.21 Jan 27th Winta Binta Vinta Fest 2018
145 Liberty Win 9-8 1339.02 Jan 28th Winta Binta Vinta Fest 2018
66 Virginia Loss 4-10 1171.7 Jan 28th Winta Binta Vinta Fest 2018
150 Virginia Commonwealth Loss 7-10 796.58 Jan 28th Winta Binta Vinta Fest 2018
21 Michigan Loss 7-15 1638.43 Feb 24th Commonwealth Cup 2018
3 North Carolina** Loss 0-15 2130.5 Ignored Feb 24th Commonwealth Cup 2018
15 North Carolina State Loss 9-15 1837.59 Feb 24th Commonwealth Cup 2018
45 Case Western Reserve Loss 5-11 1378.58 Feb 25th Commonwealth Cup 2018
62 Central Florida Loss 9-10 1673.88 Feb 25th Commonwealth Cup 2018
49 Duke Loss 10-11 1826.48 Feb 25th Commonwealth Cup 2018
31 Penn State Loss 7-10 1696.53 Mar 17th Bonanza 2018
215 Virginia-B** Win 13-2 1376.36 Ignored Mar 17th Bonanza 2018
86 Maryland Win 9-7 1871.64 Mar 17th Bonanza 2018
173 East Carolina Win 13-3 1638.17 Mar 17th Bonanza 2018
31 Penn State Loss 3-13 1486.19 Mar 18th Bonanza 2018
75 Pennsylvania Win 13-10 2029.67 Mar 18th Bonanza 2018
150 Virginia Commonwealth Win 15-4 1786.24 Mar 18th Bonanza 2018
30 Williams Loss 8-13 1605.02 Mar 24th I 85 Rodeo 2018
10 Pittsburgh** Loss 5-15 1881.75 Ignored Mar 24th I 85 Rodeo 2018
**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)