#132 Cedarville (11-7)

avg: 1002.53  •  sd: 61.81  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
183 South Carolina-B Win 8-3 1206.54 Feb 17th Commonwealth Cup Weekend 1 2024
114 Richmond Loss 6-7 1001.29 Feb 17th Commonwealth Cup Weekend 1 2024
50 Georgetown Loss 6-13 1001.27 Feb 17th Commonwealth Cup Weekend 1 2024
163 Catholic Win 8-4 1377.49 Feb 18th Commonwealth Cup Weekend 1 2024
217 Georgetown-B** Win 13-3 892.68 Ignored Feb 18th Commonwealth Cup Weekend 1 2024
204 Elon Win 11-7 874.11 Feb 18th Commonwealth Cup Weekend 1 2024
63 Tennessee Loss 7-13 920.59 Mar 23rd Needle in a Ho Stack 2024
74 Davidson Loss 3-9 775.12 Mar 23rd Needle in a Ho Stack 2024
183 South Carolina-B Win 13-2 1206.54 Mar 24th Needle in a Ho Stack 2024
249 Emory-B** Win 13-0 600 Ignored Mar 24th Needle in a Ho Stack 2024
120 Charleston Loss 5-10 508.25 Mar 24th Needle in a Ho Stack 2024
199 Xavier Win 15-5 1054.1 Apr 20th Ohio D III Womens Conferences 2024
203 Oberlin Win 15-2 1026.88 Apr 20th Ohio D III Womens Conferences 2024
159 Kenyon Win 15-6 1425.76 Apr 20th Ohio D III Womens Conferences 2024
168 Swarthmore Win 11-5 1377.22 Apr 27th Ohio Valley D III College Womens Regionals 2024
94 Lehigh Loss 6-15 675.7 Apr 27th Ohio Valley D III College Womens Regionals 2024
159 Kenyon Loss 10-11 700.76 Apr 27th Ohio Valley D III College Womens Regionals 2024
203 Oberlin Win 14-7 1009.77 Apr 28th Ohio Valley D III College Womens Regionals 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)