#20 Virginia (18-6)

avg: 2268.93  •  sd: 92.35  •  top 16/20: 52.5%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
32 Georgetown Win 9-6 2425.58 Jan 25th Winta Binta Vinta 2025
48 Liberty Win 10-4 2419.58 Jan 25th Winta Binta Vinta 2025
140 North Carolina-Wilmington** Win 13-0 1613.23 Ignored Jan 25th Winta Binta Vinta 2025
32 Georgetown Win 9-6 2425.58 Jan 26th Winta Binta Vinta 2025
21 Ohio State Win 8-7 2391.62 Jan 26th Winta Binta Vinta 2025
245 Virginia-B** Win 13-1 600 Ignored Jan 26th Winta Binta Vinta 2025
55 Appalachian State Win 12-4 2299.38 Feb 15th Queen City Tune Up 2025
31 Pittsburgh Win 13-7 2572.04 Feb 15th Queen City Tune Up 2025
3 Tufts Loss 4-13 2240.06 Feb 15th Queen City Tune Up 2025
62 North Carolina State Win 7-3 2251.53 Feb 16th Queen City Tune Up 2025
27 Northeastern Loss 4-8 1527.8 Feb 16th Queen City Tune Up 2025
58 Brown Win 12-6 2246.8 Feb 22nd 2025 Commonwealth Cup Weekend 2
19 Notre Dame Win 10-8 2548.76 Feb 22nd 2025 Commonwealth Cup Weekend 2
23 Pennsylvania Loss 6-9 1827.28 Feb 22nd 2025 Commonwealth Cup Weekend 2
29 Georgia Tech Win 10-8 2321.39 Feb 23rd 2025 Commonwealth Cup Weekend 2
23 Pennsylvania Win 11-8 2611.46 Feb 23rd 2025 Commonwealth Cup Weekend 2
6 Vermont Loss 7-15 2071.2 Feb 23rd 2025 Commonwealth Cup Weekend 2
33 Cornell Win 12-3 2568.51 Mar 29th East Coast Invite 2025
27 Northeastern Win 11-10 2217.61 Mar 29th East Coast Invite 2025
19 Notre Dame Loss 6-12 1706.78 Mar 29th East Coast Invite 2025
3 Tufts Loss 7-15 2240.06 Mar 29th East Coast Invite 2025
32 Georgetown Win 11-5 2607.01 Mar 30th East Coast Invite 2025
56 Maryland Win 11-7 2165.56 Mar 30th East Coast Invite 2025
24 Minnesota Win 7-6 2259.28 Mar 30th East Coast Invite 2025
**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)