#47 Williams (10-2)

avg: 1526.41  •  sd: 75.85  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
171 NYU Win 7-4 1231.26 Mar 9th No Sleep Till Brooklyn
99 MIT Win 9-5 1656.53 Mar 9th No Sleep Till Brooklyn
44 Brown Win 9-7 1833.75 Mar 9th No Sleep Till Brooklyn
183 Marist** Win 13-4 1226.16 Ignored Mar 9th No Sleep Till Brooklyn
33 Bates Loss 4-7 1210.33 Mar 10th No Sleep Till Brooklyn
99 MIT Win 9-4 1727.48 Mar 10th No Sleep Till Brooklyn
155 Appalachian State Win 10-6 1346.23 Mar 16th Bonanza 2019
82 Georgetown Win 10-4 1866.46 Mar 16th Bonanza 2019
197 Christopher Newport** Win 10-3 1165.13 Ignored Mar 16th Bonanza 2019
156 Wisconsin-Milwaukee Win 9-6 1263.58 Mar 16th Bonanza 2019
61 James Madison Win 11-9 1684.37 Mar 17th Bonanza 2019
56 Pennsylvania Loss 7-8 1362.36 Mar 17th Bonanza 2019
**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)