#39 Virginia (11-14)

avg: 1738.89  •  sd: 52.31  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
88 Virginia Tech Win 13-0 1897.26 Jan 27th Winta Binta Vinta 2024
57 William & Mary Loss 8-9 1419.4 Jan 27th Winta Binta Vinta 2024
61 Penn State Win 9-8 1628.49 Jan 27th Winta Binta Vinta 2024
123 Liberty** Win 9-0 1578.52 Ignored Jan 28th Winta Binta Vinta 2024
61 Penn State Win 7-5 1831.63 Jan 28th Winta Binta Vinta 2024
21 Ohio State Loss 5-9 1553.1 Jan 28th Winta Binta Vinta 2024
38 South Carolina Loss 4-13 1163.12 Feb 10th Queen City Tune Up 2024
2 Vermont** Loss 2-15 2189.63 Ignored Feb 10th Queen City Tune Up 2024
29 Wisconsin Loss 9-14 1439.75 Feb 10th Queen City Tune Up 2024
57 William & Mary Win 12-5 2144.4 Feb 10th Queen City Tune Up 2024
97 Appalachian State Win 15-3 1820.34 Feb 11th Queen City Tune Up 2024
21 Ohio State Loss 9-12 1736.79 Feb 11th Queen City Tune Up 2024
16 Georgia Loss 9-10 2029.75 Feb 24th Commonwealth Cup Weekend 2 2024
7 Tufts** Loss 5-15 1920.18 Ignored Feb 24th Commonwealth Cup Weekend 2 2024
22 Notre Dame Loss 8-15 1490.36 Feb 24th Commonwealth Cup Weekend 2 2024
21 Ohio State Loss 7-11 1615.26 Feb 25th Commonwealth Cup Weekend 2 2024
26 North Carolina State Win 9-8 2053.54 Feb 25th Commonwealth Cup Weekend 2 2024
52 Yale Win 8-5 2047.65 Feb 25th Commonwealth Cup Weekend 2 2024
35 Ohio Win 8-7 1923.14 Feb 25th Commonwealth Cup Weekend 2 2024
61 Penn State Win 13-5 2103.49 Mar 30th East Coast Invite 2024
12 Michigan Loss 5-11 1711.61 Mar 30th East Coast Invite 2024
20 Northeastern Loss 2-15 1501.67 Mar 30th East Coast Invite 2024
29 Wisconsin Loss 8-12 1472.46 Mar 30th East Coast Invite 2024
31 Brown Loss 8-9 1749.19 Mar 31st East Coast Invite 2024
90 Carnegie Mellon Win 12-4 1890.85 Mar 31st East Coast Invite 2024
**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)