#163 Catholic (8-10)

avg: 812.68  •  sd: 74.32  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
100 Davenport Loss 4-9 619.44 Feb 17th Commonwealth Cup Weekend 1 2024
217 Georgetown-B Win 9-4 892.68 Feb 17th Commonwealth Cup Weekend 1 2024
138 Liberty Loss 2-8 346.76 Feb 17th Commonwealth Cup Weekend 1 2024
183 South Carolina-B Win 9-4 1206.54 Feb 18th Commonwealth Cup Weekend 1 2024
188 Wake Forest Win 10-4 1166.45 Feb 18th Commonwealth Cup Weekend 1 2024
132 Cedarville Loss 4-8 437.72 Feb 18th Commonwealth Cup Weekend 1 2024
214 Miami (Florida) Win 7-3 917.24 Mar 2nd Cherry Blossom Classic 2024
219 William & Mary-B Win 9-3 883.01 Mar 2nd Cherry Blossom Classic 2024
38 American Loss 5-11 1123.68 Mar 3rd Cherry Blossom Classic 2024
94 Lehigh Loss 5-7 947.56 Mar 3rd Cherry Blossom Classic 2024
152 Delaware Win 10-6 1361.67 Mar 3rd Cherry Blossom Classic 2024
125 Johns Hopkins Loss 5-7 734.03 Mar 3rd Cherry Blossom Classic 2024
204 Elon Win 10-5 981.11 Apr 13th Atlantic Coast D III Womens Conferences 2024
74 Davidson Loss 3-10 775.12 Apr 13th Atlantic Coast D III Womens Conferences 2024
176 Mary Washington Win 7-6 815.11 Apr 13th Atlantic Coast D III Womens Conferences 2024
114 Richmond Loss 3-15 526.29 Apr 14th Atlantic Coast D III Womens Conferences 2024
114 Richmond Loss 6-7 1001.29 Apr 14th Atlantic Coast D III Womens Conferences 2024
176 Mary Washington Loss 2-9 90.11 Apr 14th Atlantic Coast D III Womens Conferences 2024
**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)