#125 Liberty (8-4)

avg: 927.42  •  sd: 59.26  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
83 Carnegie Mellon Loss 9-12 798.97 Jan 27th Mid Atlantic Warm Up
142 Johns Hopkins Win 9-8 917.02 Jan 27th Mid Atlantic Warm Up
172 RIT Win 13-9 984.94 Jan 27th Mid Atlantic Warm Up
87 Richmond Loss 7-12 606.08 Jan 27th Mid Atlantic Warm Up
197 Mary Washington Win 12-9 701.81 Jan 28th Mid Atlantic Warm Up
132 Pennsylvania Win 11-8 1232.18 Jan 28th Mid Atlantic Warm Up
94 Connecticut Loss 10-12 834.13 Jan 28th Mid Atlantic Warm Up
162 Davenport Win 12-8 1096.26 Feb 17th Commonwealth Cup Weekend 1 2024
204 Wake Forest University Win 13-2 928.71 Feb 17th Commonwealth Cup Weekend 1 2024
145 Messiah Win 13-7 1328.12 Feb 18th Commonwealth Cup Weekend 1 2024
112 Elon Loss 9-11 725.91 Feb 18th Commonwealth Cup Weekend 1 2024
161 Pittsburgh-B Win 11-9 908.11 Feb 18th Commonwealth Cup Weekend 1 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)