#75 Pennsylvania (7-4)

avg: 1701.53  •  sd: 99.38  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
181 Pittsburgh-B** Win 15-3 1594.41 Ignored Feb 24th Commonwealth Cup 2018
160 Richmond Win 15-2 1729.56 Feb 24th Commonwealth Cup 2018
64 Columbia Win 10-8 2046.61 Feb 24th Commonwealth Cup 2018
21 Michigan Loss 6-10 1742.27 Feb 25th Commonwealth Cup 2018
48 Georgia Loss 9-12 1610.21 Feb 25th Commonwealth Cup 2018
246 Georgetown-B** Win 13-0 1081.64 Ignored Mar 17th Bonanza 2018
80 James Madison Win 10-2 2247.06 Mar 17th Bonanza 2018
150 Virginia Commonwealth Win 11-3 1786.24 Mar 17th Bonanza 2018
159 Appalachian State Win 13-6 1737.56 Mar 17th Bonanza 2018
88 Georgetown Loss 10-13 1250.48 Mar 18th Bonanza 2018
80 James Madison Loss 9-12 1301.7 Mar 18th Bonanza 2018
**Blowout Eligible


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)