#22 Georgia (13-9)

avg: 1834.49  •  sd: 59.01  •  top 16/20: 26.6%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
83 Rutgers Win 13-6 2032.97 Feb 8th Florida Warm Up 2019
2 Brown Loss 9-13 1810.59 Feb 8th Florida Warm Up 2019
31 Texas A&M Win 13-12 1873.41 Feb 8th Florida Warm Up 2019
18 Michigan Win 13-8 2404.93 Feb 9th Florida Warm Up 2019
17 Minnesota Win 11-9 2200.26 Feb 9th Florida Warm Up 2019
27 LSU Win 11-8 2143.35 Feb 9th Florida Warm Up 2019
80 Oklahoma Win 10-6 1948.13 Feb 9th Florida Warm Up 2019
2 Brown Loss 11-15 1847.99 Feb 10th Florida Warm Up 2019
12 Texas Loss 10-13 1681.76 Feb 10th Florida Warm Up 2019
57 Carnegie Mellon Win 13-9 2005.94 Mar 9th Classic City Invite 2019
26 North Carolina-Wilmington Loss 10-13 1452.83 Mar 9th Classic City Invite 2019
28 Northeastern Win 11-10 1900.83 Mar 9th Classic City Invite 2019
81 Georgia Tech Win 11-10 1572.32 Mar 9th Classic City Invite 2019
25 South Carolina Loss 9-13 1368.12 Mar 10th Classic City Invite 2019
4 Pittsburgh Loss 3-13 1584.92 Mar 10th Classic City Invite 2019
7 Carleton College-CUT Loss 5-13 1518.64 Mar 30th Easterns 2019 Men
43 Harvard Win 13-10 2000.42 Mar 30th Easterns 2019 Men
1 North Carolina Loss 7-13 1674.39 Mar 30th Easterns 2019 Men
54 Virginia Tech Win 12-10 1857.57 Mar 30th Easterns 2019 Men
17 Minnesota Loss 9-12 1605.68 Mar 31st Easterns 2019 Men
24 Auburn Win 15-5 2396.78 Mar 31st Easterns 2019 Men
47 Maryland Win 14-13 1781.33 Mar 31st Easterns 2019 Men
**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)