#160 Richmond (6-4)

avg: 1129.56  •  sd: 90.76  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
75 Pennsylvania Loss 2-15 1101.53 Feb 24th Commonwealth Cup 2018
181 Pittsburgh-B Win 10-9 1119.41 Feb 24th Commonwealth Cup 2018
64 Columbia** Loss 3-15 1183.95 Ignored Feb 24th Commonwealth Cup 2018
135 William & Mary Loss 7-10 884.09 Feb 25th Commonwealth Cup 2018
250 Davidson** Win 13-3 1020.47 Ignored Feb 25th Commonwealth Cup 2018
218 Elon Win 12-5 1344.95 Feb 25th Commonwealth Cup 2018
270 American-B** Win 13-0 300.68 Ignored Mar 24th I 85 Rodeo 2018
137 Middlebury Loss 8-9 1137.17 Mar 24th I 85 Rodeo 2018
169 Wake Forest University Win 8-7 1178.39 Mar 24th I 85 Rodeo 2018
250 Davidson** Win 13-3 1020.47 Ignored Mar 24th I 85 Rodeo 2018
**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)