#182 George Mason (3-15)

avg: 628.31  •  sd: 64.46  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
59 Duke Loss 5-6 1320.96 Jan 26th Winta Binta Vinta Fest 2019
71 William & Mary** Loss 5-12 726.62 Ignored Jan 26th Winta Binta Vinta Fest 2019
105 Liberty Loss 3-8 487.19 Jan 26th Winta Binta Vinta Fest 2019
45 Virginia** Loss 2-11 945.7 Ignored Jan 26th Winta Binta Vinta Fest 2019
157 Virginia Commonwealth Loss 7-9 562.57 Jan 27th Winta Binta Vinta Fest 2019
271 Virginia-B** Win 10-3 416.54 Ignored Jan 27th Winta Binta Vinta Fest 2019
52 Columbia** Loss 3-13 903.27 Ignored Feb 23rd Commonwealth Cup 2019
105 Liberty Loss 12-13 962.19 Feb 23rd Commonwealth Cup 2019
153 Virginia Tech Win 13-12 985.33 Feb 23rd Commonwealth Cup 2019
117 Catholic Loss 7-9 764.46 Feb 23rd Commonwealth Cup 2019
81 Ohio** Loss 4-13 668.3 Ignored Feb 24th Commonwealth Cup 2019
135 Princeton Loss 7-13 384.94 Feb 24th Commonwealth Cup 2019
61 James Madison** Loss 3-12 835.16 Ignored Mar 30th Atlantic Coast Open 2019
71 William & Mary** Loss 1-12 726.62 Ignored Mar 30th Atlantic Coast Open 2019
147 George Washington Loss 4-13 281.09 Mar 30th Atlantic Coast Open 2019
197 Christopher Newport Loss 7-9 285.79 Mar 30th Atlantic Coast Open 2019
259 East Carolina Win 15-4 673.16 Mar 31st Atlantic Coast Open 2019
147 George Washington Loss 7-9 601.76 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)