#115 Cedarville (13-8)

avg: 947.08  •  sd: 47.33  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
211 Georgetown-B** Win 9-3 874.31 Ignored Feb 15th 2025 Commonwealth Cup Weekend 1
212 Georgia-B** Win 8-1 872.62 Ignored Feb 15th 2025 Commonwealth Cup Weekend 1
49 Kenyon Loss 3-11 898.8 Feb 15th 2025 Commonwealth Cup Weekend 1
151 North Carolina-B Win 7-6 814.22 Feb 15th 2025 Commonwealth Cup Weekend 1
187 Wake Forest Win 9-6 913.38 Feb 16th 2025 Commonwealth Cup Weekend 1
149 Davidson Win 4-3 829.52 Feb 16th 2025 Commonwealth Cup Weekend 1
226 South Carolina-B** Win 12-5 787.46 Ignored Mar 29th Needle in a Ho Stack 2025
102 Virginia Tech Loss 7-9 770.02 Mar 29th Needle in a Ho Stack 2025
33 Georgia Tech** Loss 4-13 1051.61 Ignored Mar 29th Needle in a Ho Stack 2025
159 Charleston Win 15-5 1265 Mar 30th Needle in a Ho Stack 2025
126 North Carolina-Wilmington Win 9-6 1265.85 Mar 30th Needle in a Ho Stack 2025
83 Clemson Loss 10-11 1038.67 Mar 30th Needle in a Ho Stack 2025
199 Oberlin Win 15-2 1040.83 Apr 12th Ohio D III Womens Conferences 2025
154 Xavier Win 14-3 1277.47 Apr 12th Ohio D III Womens Conferences 2025
49 Kenyon Loss 8-15 933.99 Apr 12th Ohio D III Womens Conferences 2025
199 Oberlin Win 11-9 690.04 Apr 26th Ohio Valley D III College Womens Regionals 2025
97 Lehigh Loss 7-12 569.77 Apr 26th Ohio Valley D III College Womens Regionals 2025
36 Haverford/Bryn Mawr** Loss 3-13 1019.05 Ignored Apr 26th Ohio Valley D III College Womens Regionals 2025
154 Xavier Win 15-4 1277.47 Apr 26th Ohio Valley D III College Womens Regionals 2025
118 Swarthmore Win 11-10 1064.23 Apr 27th Ohio Valley D III College Womens Regionals 2025
97 Lehigh Loss 6-13 490.28 Apr 27th Ohio Valley D III 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)