#26 Georgia (12-8)

avg: 1848.3  •  sd: 62.54  •  top 16/20: 7.3%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
3 Ohio State Loss 3-11 1773 Jan 19th Florida Winter Classic 2019
20 North Carolina-Wilmington Win 9-7 2239.51 Jan 19th Florida Winter Classic 2019
8 Dartmouth Loss 7-11 1691.96 Jan 19th Florida Winter Classic 2019
51 Florida State Win 10-6 2004.51 Jan 19th Florida Winter Classic 2019
8 Dartmouth Loss 7-12 1638.34 Jan 20th Florida Winter Classic 2019
38 Florida Win 14-8 2147.15 Jan 20th Florida Winter Classic 2019
51 Florida State Win 12-11 1633.35 Jan 20th Florida Winter Classic 2019
65 Massachusetts Win 10-4 1996.29 Feb 9th Queen City Tune Up 2019 Women
40 Michigan Win 9-6 1988 Feb 9th Queen City Tune Up 2019 Women
28 North Carolina State Win 10-8 2036.32 Feb 9th Queen City Tune Up 2019 Women
5 Carleton College-Syzygy Loss 3-11 1665.5 Feb 9th Queen City Tune Up 2019 Women
20 North Carolina-Wilmington Loss 10-13 1632.03 Feb 10th Queen City Tune Up 2019 Women
38 Florida Loss 6-11 1064.42 Feb 10th Queen City Tune Up 2019 Women
11 Pittsburgh Loss 10-15 1629.66 Feb 10th Queen City Tune Up 2019 Women
46 Middlebury Win 13-6 2138.47 Mar 30th I 85 Rodeo 2019
166 Richmond** Win 13-3 1370.47 Ignored Mar 30th I 85 Rodeo 2019
82 Georgetown Win 13-6 1866.46 Mar 30th I 85 Rodeo 2019
57 Cornell Win 15-1 2060.62 Mar 31st I 85 Rodeo 2019
85 Dayton** Win 15-1 1843.31 Ignored Mar 31st I 85 Rodeo 2019
18 South Carolina Loss 10-11 1846.42 Mar 31st I 85 Rodeo 2019
**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)