#128 Cedarville (6-5)

avg: 946.97  •  sd: 90.83  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
162 South Carolina-B Win 8-3 1240.77 Feb 17th Commonwealth Cup Weekend 1 2024
129 Richmond Loss 6-7 821.74 Feb 17th Commonwealth Cup Weekend 1 2024
59 Georgetown Loss 6-13 919.65 Feb 17th Commonwealth Cup Weekend 1 2024
144 Catholic Win 8-4 1413.79 Feb 18th Commonwealth Cup Weekend 1 2024
207 Georgetown-B** Win 13-3 709.48 Ignored Feb 18th Commonwealth Cup Weekend 1 2024
191 Elon Win 11-7 814.29 Feb 18th Commonwealth Cup Weekend 1 2024
66 Tennessee Loss 7-13 877.32 Mar 23rd Needle in a Ho Stack 2024
50 Davidson** Loss 3-9 1013.93 Mar 23rd Needle in a Ho Stack 2024
162 South Carolina-B Win 13-2 1240.77 Mar 24th Needle in a Ho Stack 2024
217 Emory-B** Win 13-0 568.5 Ignored Mar 24th Needle in a Ho Stack 2024
133 Charleston Loss 5-10 343.04 Mar 24th Needle in a Ho Stack 2024
**Blowout Eligible

FAQ

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
  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
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)