#91 Mary Washington (16-6)

avg: 1382.51  •  sd: 53.83  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
166 Virginia Commonwealth Win 13-11 1320.67 Feb 2nd Mid Atlantic Warmup 2019
137 North Carolina-B Win 13-11 1461.99 Feb 2nd Mid Atlantic Warmup 2019
39 Vermont Loss 5-13 1105.77 Feb 2nd Mid Atlantic Warmup 2019
88 Tennessee-Chattanooga Loss 10-12 1181.06 Feb 2nd Mid Atlantic Warmup 2019
120 James Madison Loss 12-15 982.31 Feb 3rd Mid Atlantic Warmup 2019
114 Liberty Win 15-9 1815.59 Feb 3rd Mid Atlantic Warmup 2019
137 North Carolina-B Win 15-13 1447.33 Feb 3rd Mid Atlantic Warmup 2019
32 William & Mary Loss 12-15 1446.19 Feb 9th Virginia Showcase Series 2019 2919
155 Elon Win 12-10 1387.71 Mar 2nd FCS D III Tune Up 2019
234 Florida Tech Win 14-12 1127.22 Mar 2nd FCS D III Tune Up 2019
173 Georgia College Win 12-10 1307.23 Mar 2nd FCS D III Tune Up 2019
183 Oberlin Win 10-9 1166.96 Mar 2nd FCS D III Tune Up 2019
138 Missouri S&T Win 13-8 1726.25 Mar 3rd FCS D III Tune Up 2019
35 Middlebury Loss 11-13 1497.66 Mar 3rd FCS D III Tune Up 2019
253 Anderson Win 12-10 1081.22 Mar 3rd FCS D III Tune Up 2019
83 Rutgers Win 11-8 1798.58 Mar 30th Atlantic Coast Open 2019
118 MIT Win 9-7 1567.07 Mar 30th Atlantic Coast Open 2019
345 American Win 13-9 920.37 Mar 30th Atlantic Coast Open 2019
151 SUNY-Binghamton Win 10-9 1287.14 Mar 30th Atlantic Coast Open 2019
66 Penn State Win 11-10 1660.24 Mar 31st Atlantic Coast Open 2019
35 Middlebury Loss 9-13 1307.93 Mar 31st Atlantic Coast Open 2019
115 Villanova Win 12-9 1641.76 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)