#19 Georgia (10-12)

avg: 1950.85  •  sd: 58.86  •  top 16/20: 57%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
14 Carleton College Loss 10-12 1811.75 Feb 3rd Florida Warm Up 2023
112 Illinois** Win 13-5 1915.98 Ignored Feb 3rd Florida Warm Up 2023
11 Brown Win 9-7 2354.05 Feb 4th Florida Warm Up 2023
88 Central Florida Win 13-7 1991.75 Feb 4th Florida Warm Up 2023
21 Northeastern Win 9-8 2032.17 Feb 4th Florida Warm Up 2023
8 Pittsburgh Win 13-12 2280.18 Feb 4th Florida Warm Up 2023
14 Carleton College Win 13-10 2378.02 Feb 5th Florida Warm Up 2023
12 Minnesota Loss 12-13 1945.91 Feb 5th Florida Warm Up 2023
6 Colorado Loss 10-13 1869.43 Mar 4th Smoky Mountain Invite
5 Vermont Loss 9-12 1864.7 Mar 4th Smoky Mountain Invite
30 Ohio State Loss 10-13 1507.83 Mar 4th Smoky Mountain Invite
1 North Carolina Loss 11-13 2163.81 Mar 4th Smoky Mountain Invite
12 Minnesota Loss 12-15 1770.42 Mar 5th Smoky Mountain Invite
107 Tennessee** Win 15-6 1941.72 Ignored Mar 5th Smoky Mountain Invite
30 Ohio State Loss 10-14 1437.27 Mar 5th Smoky Mountain Invite
13 Tufts Loss 11-13 1839.38 Apr 1st Easterns 2023
7 Cal Poly-SLO Loss 8-11 1809.74 Apr 1st Easterns 2023
23 Wisconsin Loss 8-10 1631.85 Apr 1st Easterns 2023
5 Vermont Loss 7-13 1652.53 Apr 1st Easterns 2023
72 Auburn Win 15-3 2097.8 Apr 2nd Easterns 2023
34 Michigan Win 15-8 2353.63 Apr 2nd Easterns 2023
30 Ohio State Win 14-6 2435.97 Apr 2nd Easterns 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)