#215 Virginia-B (5-13)

avg: 776.36  •  sd: 91.29  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
80 James Madison** Loss 2-12 1047.06 Ignored Jan 27th Winta Binta Vinta Fest 2018
245 George Mason University Win 7-5 822.31 Jan 27th Winta Binta Vinta Fest 2018
88 Georgetown Loss 5-9 1049.56 Jan 27th Winta Binta Vinta Fest 2018
46 North Carolina-Wilmington** Loss 1-13 1378.21 Ignored Jan 27th Winta Binta Vinta Fest 2018
245 George Mason University Win 11-2 1094.17 Jan 28th Winta Binta Vinta Fest 2018
150 Virginia Commonwealth Loss 5-13 586.24 Jan 28th Winta Binta Vinta Fest 2018
45 Case Western Reserve** Loss 2-15 1378.58 Ignored Feb 24th Commonwealth Cup 2018
171 Catholic Loss 4-15 446.87 Feb 24th Commonwealth Cup 2018
74 Massachusetts** Loss 3-15 1111.04 Ignored Feb 24th Commonwealth Cup 2018
181 Pittsburgh-B Win 12-9 1339.78 Feb 25th Commonwealth Cup 2018
218 Elon Loss 7-11 278.06 Feb 25th Commonwealth Cup 2018
250 Davidson Win 13-2 1020.47 Feb 25th Commonwealth Cup 2018
31 Penn State** Loss 1-13 1486.19 Ignored Mar 17th Bonanza 2018
88 Georgetown** Loss 2-13 978.62 Ignored Mar 17th Bonanza 2018
86 Maryland** Loss 4-12 992.3 Ignored Mar 17th Bonanza 2018
173 East Carolina Loss 6-12 458.86 Mar 17th Bonanza 2018
159 Appalachian State Loss 6-15 537.56 Mar 18th Bonanza 2018
246 Georgetown-B Win 14-6 1081.64 Mar 18th Bonanza 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)