#213 Georgia-B (4-18)

avg: 272.97  •  sd: 83.6  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
116 Cedarville** Loss 1-8 347.19 Ignored Feb 15th 2025 Commonwealth Cup Weekend 1
150 Davidson Loss 1-6 104.69 Feb 15th 2025 Commonwealth Cup Weekend 1
49 Kenyon** Loss 0-11 898.7 Ignored Feb 15th 2025 Commonwealth Cup Weekend 1
188 Wake Forest Loss 0-7 -104.9 Feb 15th 2025 Commonwealth Cup Weekend 1
212 Georgetown-B Loss 3-6 -271.7 Feb 16th 2025 Commonwealth Cup Weekend 1
152 North Carolina-B Loss 0-11 89.66 Feb 16th 2025 Commonwealth Cup Weekend 1
81 Case Western Reserve** Loss 1-13 600.8 Ignored Mar 29th Needle in a Ho Stack 2025
84 Clemson** Loss 3-11 563.38 Ignored Mar 29th Needle in a Ho Stack 2025
100 Emory** Loss 3-9 475.12 Ignored Mar 29th Needle in a Ho Stack 2025
128 North Carolina-Wilmington Loss 6-8 546.81 Mar 29th Needle in a Ho Stack 2025
229 Elon Win 10-2 765.37 Mar 30th Needle in a Ho Stack 2025
258 Emory-B Win 8-4 251.23 Mar 30th Needle in a Ho Stack 2025
100 Emory** Loss 3-14 475.12 Ignored Apr 12th Southern Appalachian D I Womens Conferences 2025
29 Georgia Loss 7-11 1258.07 Apr 12th Southern Appalachian D I Womens Conferences 2025
203 Tennessee-Chattanooga Loss 7-10 5.8 Apr 12th Southern Appalachian D I Womens Conferences 2025
258 Emory-B Win 11-5 286.43 Apr 13th Southern Appalachian D I Womens Conferences 2025
157 Georgia Southern Loss 4-11 74.02 Apr 13th Southern Appalachian D I Womens Conferences 2025
33 Georgia Tech** Loss 2-15 1050.8 Ignored Apr 26th Southeast D I College Womens Regionals 2025
125 Jacksonville State Loss 8-14 350.34 Apr 26th Southeast D I College Womens Regionals 2025
189 LSU Loss 8-15 -73.66 Apr 26th Southeast D I College Womens Regionals 2025
234 Auburn Loss 9-10 0.37 Apr 27th Southeast D I College Womens Regionals 2025
237 Florida-B Win 10-8 346.82 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)