#157 Virginia Commonwealth (5-13)

avg: 841.9  •  sd: 59.33  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
61 James Madison Loss 7-11 968.27 Jan 26th Winta Binta Vinta Fest 2019
40 Michigan** Loss 0-11 969.43 Ignored Jan 26th Winta Binta Vinta Fest 2019
271 Virginia-B** Win 12-1 416.54 Ignored Jan 26th Winta Binta Vinta Fest 2019
82 Georgetown Loss 5-9 737.4 Jan 26th Winta Binta Vinta Fest 2019
105 Liberty Loss 3-8 487.19 Jan 27th Winta Binta Vinta Fest 2019
182 George Mason Win 9-7 907.65 Jan 27th Winta Binta Vinta Fest 2019
82 Georgetown Loss 4-10 666.46 Jan 27th Winta Binta Vinta Fest 2019
61 James Madison Loss 4-15 835.16 Mar 16th Bonanza 2019
27 Delaware** Loss 3-15 1214.9 Ignored Mar 16th Bonanza 2019
56 Pennsylvania** Loss 3-12 887.36 Ignored Mar 16th Bonanza 2019
155 Appalachian State Loss 12-13 725.07 Mar 17th Bonanza 2019
197 Christopher Newport Win 15-8 1129.93 Mar 17th Bonanza 2019
61 James Madison Loss 3-13 835.16 Mar 30th Atlantic Coast Open 2019
130 Connecticut Loss 8-9 867.45 Mar 30th Atlantic Coast Open 2019
259 East Carolina** Win 13-2 673.16 Ignored Mar 30th Atlantic Coast Open 2019
147 George Washington Win 10-6 1377.25 Mar 30th Atlantic Coast Open 2019
61 James Madison Loss 3-11 835.16 Mar 31st Atlantic Coast Open 2019
130 Connecticut Loss 6-14 392.45 Mar 31st Atlantic Coast Open 2019
**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)