#44 William & Mary (6-5)

avg: 1340.13  •  sd: 91.88  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
73 Liberty Win 12-7 1422.14 Jan 25th Winta Binta Vinta Fest 2020
18 Virginia Loss 4-11 1209.86 Jan 25th Winta Binta Vinta Fest 2020
117 West Virginia** Win 12-2 804.55 Ignored Jan 25th Winta Binta Vinta Fest 2020
73 Liberty Win 9-7 1180.97 Jan 26th Winta Binta Vinta Fest 2020
120 Virginia Commonwealth** Win 15-0 737.47 Ignored Jan 26th Winta Binta Vinta Fest 2020
48 James Madison Loss 9-10 1140.59 Jan 26th Winta Binta Vinta Fest 2020
74 Appalachian State Win 12-6 1464.59 Feb 8th Queen City Tune Up 2020 Women
24 Pittsburgh Loss 3-11 1065.48 Feb 8th Queen City Tune Up 2020 Women
14 Georgia Tech Loss 5-10 1303.8 Feb 8th Queen City Tune Up 2020 Women
3 North Carolina Loss 5-8 1716.11 Feb 8th Queen City Tune Up 2020 Women
37 Vanderbilt Win 8-7 1561.33 Feb 9th Queen City Tune Up 2020 Women
**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)