#271 Virginia-B (1-17)

avg: -183.46  •  sd: 144.58  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
61 James Madison** Loss 0-13 835.16 Ignored Jan 26th Winta Binta Vinta Fest 2019
40 Michigan** Loss 0-13 969.43 Ignored Jan 26th Winta Binta Vinta Fest 2019
157 Virginia Commonwealth** Loss 1-12 241.9 Ignored Jan 26th Winta Binta Vinta Fest 2019
82 Georgetown** Loss 1-13 666.46 Ignored Jan 26th Winta Binta Vinta Fest 2019
71 William & Mary** Loss 1-13 726.62 Ignored Jan 27th Winta Binta Vinta Fest 2019
182 George Mason** Loss 3-10 28.31 Ignored Jan 27th Winta Binta Vinta Fest 2019
99 MIT** Loss 2-13 527.48 Ignored Feb 23rd Commonwealth Cup 2019
105 Liberty** Loss 3-10 487.19 Ignored Feb 23rd Commonwealth Cup 2019
70 Maryland** Loss 0-13 728.26 Ignored Feb 23rd Commonwealth Cup 2019
153 Virginia Tech** Loss 2-13 260.33 Ignored Feb 23rd Commonwealth Cup 2019
262 Michigan-B Loss 6-7 -90.4 Feb 24th Commonwealth Cup 2019
223 Elon Loss 5-10 -190.67 Feb 24th Commonwealth Cup 2019
97 Swarthmore** Loss 1-15 552.85 Ignored Mar 30th I 85 Rodeo 2019
280 Swarthmore-B Win 8-6 -97.44 Mar 30th I 85 Rodeo 2019
192 William & Mary-B** Loss 4-13 -22.99 Ignored Mar 30th I 85 Rodeo 2019
210 Cedarville** Loss 3-9 -115.92 Ignored Mar 30th I 85 Rodeo 2019
245 Ohio State-B Loss 3-9 -414.17 Mar 31st I 85 Rodeo 2019
209 North Carolina-B Loss 5-11 -113.56 Mar 31st I 85 Rodeo 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)