#212 Georgetown-B (6-7)

avg: 274.99  •  sd: 80.21  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
116 Cedarville** Loss 3-9 347.19 Ignored Feb 15th 2025 Commonwealth Cup Weekend 1
150 Davidson Loss 3-9 104.69 Feb 15th 2025 Commonwealth Cup Weekend 1
188 Wake Forest Loss 2-7 -104.9 Feb 15th 2025 Commonwealth Cup Weekend 1
49 Kenyon** Loss 2-11 898.7 Ignored Feb 15th 2025 Commonwealth Cup Weekend 1
213 Georgia-B Win 6-3 819.67 Feb 16th 2025 Commonwealth Cup Weekend 1
152 North Carolina-B Loss 1-11 89.66 Feb 16th 2025 Commonwealth Cup Weekend 1
252 American-B Win 8-5 393.97 Apr 12th Atlantic Coast Dev Womens Conferences 2025
152 North Carolina-B Loss 7-10 300 Apr 12th Atlantic Coast Dev Womens Conferences 2025
260 Virginia-B Win 7-4 87.44 Apr 12th Atlantic Coast Dev Womens Conferences 2025
239 William & Mary-B Win 3-2 201.63 Apr 12th Atlantic Coast Dev Womens Conferences 2025
227 South Carolina-B Win 5-4 313.14 Apr 13th Atlantic Coast Dev Womens Conferences 2025
239 William & Mary-B Win 9-5 605.69 Apr 13th Atlantic Coast Dev Womens Conferences 2025
152 North Carolina-B Loss 4-8 124.85 Apr 13th Atlantic Coast Dev Womens Conferences 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)