#29 Georgia (18-5)

avg: 1724.96  •  sd: 88.01  •  top 16/20: 2.3%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
69 North Carolina State Win 13-8 1789.4 Feb 15th Queen City Tune Up 2025
118 Northwestern** Win 13-2 1539.61 Ignored Feb 15th Queen City Tune Up 2025
11 Michigan Loss 6-13 1510.35 Feb 15th Queen City Tune Up 2025
24 Minnesota Win 8-7 1946.36 Feb 16th Queen City Tune Up 2025
11 Michigan Loss 2-11 1510.35 Feb 16th Queen City Tune Up 2025
25 Georgetown Loss 8-10 1537.13 Mar 29th East Coast Invite 2025
17 Notre Dame Loss 10-14 1570.32 Mar 29th East Coast Invite 2025
39 Wesleyan Win 10-8 1849.5 Mar 29th East Coast Invite 2025
31 Pittsburgh Win 10-8 1953.81 Mar 29th East Coast Invite 2025
24 Minnesota Loss 8-11 1455.75 Mar 30th East Coast Invite 2025
39 Wesleyan Win 11-6 2133.53 Mar 30th East Coast Invite 2025
53 Maryland Win 9-4 2056.32 Mar 30th East Coast Invite 2025
100 Emory Win 11-7 1542.01 Apr 12th Southern Appalachian D I Womens Conferences 2025
203 Tennessee-Chattanooga** Win 15-0 995.46 Ignored Apr 12th Southern Appalachian D I Womens Conferences 2025
213 Georgia-B Win 11-7 739.87 Apr 12th Southern Appalachian D I Womens Conferences 2025
82 Tennessee Win 14-8 1732.71 Apr 13th Southern Appalachian D I Womens Conferences 2025
33 Georgia Tech Win 15-12 1951.29 Apr 13th Southern Appalachian D I Womens Conferences 2025
172 Florida State** Win 15-5 1200.38 Ignored Apr 26th Southeast D I College Womens Regionals 2025
210 Vanderbilt** Win 15-3 881.56 Ignored Apr 26th Southeast D I College Womens Regionals 2025
203 Tennessee-Chattanooga** Win 15-1 995.46 Ignored Apr 26th Southeast D I College Womens Regionals 2025
65 Florida Win 15-10 1772.19 Apr 27th Southeast D I College Womens Regionals 2025
125 Jacksonville State** Win 15-4 1486.38 Ignored Apr 27th Southeast D I College Womens Regionals 2025
33 Georgia Tech Win 15-6 2250.8 Apr 27th Southeast D I College Womens Regionals 2025
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)