#102 Richmond (10-8)

avg: 1326.88  •  sd: 59.56  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
234 Haverford Win 13-5 1407.5 Feb 3rd Mid Atlantic Warmup 2018
51 Ohio State Win 13-11 1766.53 Feb 3rd Mid Atlantic Warmup 2018
161 Boston University Win 13-6 1687.79 Feb 3rd Mid Atlantic Warmup 2018
177 Virginia Commonwealth Win 10-9 1147.23 Feb 3rd Mid Atlantic Warmup 2018
44 Illinois Loss 14-15 1464.03 Feb 4th Mid Atlantic Warmup 2018
51 Ohio State Loss 9-10 1412.69 Feb 4th Mid Atlantic Warmup 2018
194 George Washington Win 15-7 1564.43 Feb 4th Mid Atlantic Warmup 2018
124 Indiana Loss 11-12 1101.26 Feb 17th Easterns Qualifier 2018
64 North Carolina-Charlotte Loss 7-10 1072.84 Feb 17th Easterns Qualifier 2018
113 Lehigh Win 12-10 1522.2 Feb 17th Easterns Qualifier 2018
34 William & Mary Loss 7-13 1090.67 Feb 17th Easterns Qualifier 2018
224 Georgia Southern Win 13-4 1461.24 Feb 17th Easterns Qualifier 2018
61 James Madison Loss 11-15 1091.36 Feb 18th Easterns Qualifier 2018
78 Georgetown Loss 12-14 1194.12 Feb 18th Easterns Qualifier 2018
98 Clemson Win 12-8 1779.2 Feb 18th Easterns Qualifier 2018
281 Navy Win 15-8 1230.2 Mar 31st DIII EastUR Powered by SAVAGE
266 Swarthmore Win 15-6 1331.25 Mar 31st DIII EastUR Powered by SAVAGE
83 Middlebury Loss 9-15 891.13 Mar 31st DIII EastUR Powered by SAVAGE
**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)