#123 Liberty (6-12)

avg: 978.52  •  sd: 66.97  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
59 Georgetown Loss 5-10 945.75 Jan 27th Winta Binta Vinta 2024
70 James Madison Loss 2-9 819.57 Jan 27th Winta Binta Vinta 2024
181 Virginia-B Win 9-1 1061.64 Jan 27th Winta Binta Vinta 2024
21 Ohio State** Loss 2-13 1482.16 Ignored Jan 27th Winta Binta Vinta 2024
59 Georgetown Loss 2-9 919.65 Jan 28th Winta Binta Vinta 2024
39 Virginia** Loss 0-9 1138.89 Ignored Jan 28th Winta Binta Vinta 2024
88 Virginia Tech Loss 5-7 969.11 Jan 28th Winta Binta Vinta 2024
144 Catholic Win 8-2 1448.98 Feb 17th Commonwealth Cup Weekend 1 2024
207 Georgetown-B** Win 10-1 709.48 Ignored Feb 17th Commonwealth Cup Weekend 1 2024
100 Davenport Loss 5-7 865.83 Feb 17th Commonwealth Cup Weekend 1 2024
151 George Washington Win 8-7 916.23 Feb 18th Commonwealth Cup Weekend 1 2024
59 Georgetown Loss 5-9 990.59 Feb 18th Commonwealth Cup Weekend 1 2024
127 Dartmouth Win 9-4 1547.68 Mar 23rd Rodeo 2024
94 Duke Loss 7-8 1142.64 Mar 23rd Rodeo 2024
150 North Carolina-B Loss 5-8 339.18 Mar 23rd Rodeo 2024
80 Williams Loss 3-10 743.83 Mar 23rd Rodeo 2024
127 Dartmouth Win 8-7 1072.68 Mar 24th Rodeo 2024
80 Williams Loss 6-9 925.27 Mar 24th Rodeo 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)