#220 Dickinson (9-5)

avg: 857.63  •  sd: 46.95  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
340 Lehigh-B** Win 12-5 771.65 Ignored Feb 25th Bring The Huckus1
359 Pennsylvania-B** Win 13-2 466.65 Ignored Feb 25th Bring The Huckus1
300 Rutgers-B Win 12-7 966.08 Feb 25th Bring The Huckus1
330 Edinboro Win 11-5 869.37 Feb 25th Bring The Huckus1
95 Massachusetts-B Loss 6-13 822.22 Feb 26th Bring The Huckus1
206 Colby Loss 11-13 690.56 Mar 4th Philly Special1
165 Penn State-B Loss 4-8 533.64 Mar 4th Philly Special1
247 Wisconsin-Whitewater Win 8-6 1048.71 Mar 4th Philly Special1
206 Colby Loss 10-11 794.4 Mar 5th Philly Special1
292 Connecticut-B Win 11-6 1061.06 Mar 5th Philly Special1
124 Towson Loss 7-13 711.6 Apr 1st Fuego2
330 Edinboro Win 13-0 869.37 Apr 1st Fuego2
239 Stevens Tech Win 11-10 886.75 Apr 1st Fuego2
307 West Chester-B Win 13-4 1004.29 Apr 2nd Fuego2
**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)