#84 Virginia (11-11)

avg: 1402.14  •  sd: 63.81  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
48 Dartmouth Loss 10-13 1237.29 Feb 3rd Mid Atlantic Warmup 2018
107 Rutgers Win 13-9 1732.93 Feb 3rd Mid Atlantic Warmup 2018
126 Elon Loss 10-13 883.98 Feb 3rd Mid Atlantic Warmup 2018
78 Georgetown Loss 10-13 1086.93 Feb 3rd Mid Atlantic Warmup 2018
250 Maryland-Baltimore County** Win 15-4 1368.7 Ignored Feb 4th Mid Atlantic Warmup 2018
227 Syracuse Win 15-3 1436.26 Feb 4th Mid Atlantic Warmup 2018
177 Virginia Commonwealth Win 13-8 1518.39 Feb 4th Mid Atlantic Warmup 2018
33 Maryland Loss 9-13 1265.72 Feb 17th Easterns Qualifier 2018
51 Ohio State Loss 11-13 1308.85 Feb 17th Easterns Qualifier 2018
46 South Carolina Win 9-5 2108.42 Feb 17th Easterns Qualifier 2018
75 Tennessee-Chattanooga Loss 10-11 1290.67 Feb 17th Easterns Qualifier 2018
62 Vermont Win 11-8 1831.44 Feb 17th Easterns Qualifier 2018
113 Lehigh Loss 9-11 1034.87 Feb 18th Easterns Qualifier 2018
133 Case Western Reserve Win 14-6 1775.77 Feb 18th Easterns Qualifier 2018
151 George Mason Win 15-6 1716.84 Feb 18th Easterns Qualifier 2018
12 North Carolina State Loss 12-14 1697.9 Mar 16th Atlantic Coast Showcase ACS NCSU vs Virginia
61 James Madison Loss 12-13 1347.52 Mar 24th Atlantic Coast Open 2018
243 Rowan Win 10-6 1280.17 Mar 24th Atlantic Coast Open 2018
174 East Carolina Win 10-5 1606.32 Mar 24th Atlantic Coast Open 2018
78 Georgetown Loss 11-12 1290.07 Mar 24th Atlantic Coast Open 2018
194 George Washington Win 13-11 1193.27 Mar 25th Atlantic Coast Open 2018
56 Temple Loss 8-13 1013.44 Mar 25th Atlantic Coast Open 2018
**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)