#130 Liberty (7-15)

avg: 773.74  •  sd: 47.58  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
80 American Loss 7-11 706.94 Jan 28th Winta Binta Vinta
33 Ohio State** Loss 2-9 1033.9 Ignored Jan 28th Winta Binta Vinta
62 William & Mary Loss 4-11 677.48 Jan 28th Winta Binta Vinta
145 Virginia-B Win 11-4 1255.94 Jan 28th Winta Binta Vinta
51 Georgetown** Loss 3-9 821.83 Ignored Jan 29th Winta Binta Vinta
145 Virginia-B Win 9-8 780.94 Jan 29th Winta Binta Vinta
41 James Madison** Loss 5-13 924.02 Ignored Feb 18th Commonwealth Cup Weekend1 2023
57 Virginia Tech Loss 6-11 790.45 Feb 18th Commonwealth Cup Weekend1 2023
116 Cedarville Loss 6-12 311.22 Feb 19th Commonwealth Cup Weekend1 2023
135 Mary Washington Loss 6-10 230.59 Feb 19th Commonwealth Cup Weekend1 2023
145 Virginia-B Win 7-6 780.94 Feb 19th Commonwealth Cup Weekend1 2023
58 Williams Loss 3-13 724.77 Mar 25th Rodeo 2023
59 Penn State Loss 6-13 701.24 Mar 25th Rodeo 2023
144 North Carolina-B Win 9-7 940.11 Mar 25th Rodeo 2023
21 North Carolina State** Loss 4-13 1156.31 Ignored Mar 26th Rodeo 2023
71 Massachusetts Loss 9-12 888.11 Mar 26th Rodeo 2023
60 Ohio Loss 6-10 803.18 Mar 26th Rodeo 2023
- George Mason Win 13-2 895.3 Apr 1st Atlantic Coast Open 2023
181 George Washington Win 11-5 907.11 Apr 1st Atlantic Coast Open 2023
55 Cornell Loss 5-13 745.99 Apr 1st Atlantic Coast Open 2023
181 George Washington Win 12-5 907.11 Apr 2nd Atlantic Coast Open 2023
55 Cornell Loss 4-13 745.99 Apr 2nd Atlantic Coast Open 2023
**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)