#134 Brandeis (6-6)

avg: 612.61  •  sd: 95.73  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
173 Colby Loss 8-9 133.01 Mar 4th Philly Special1
194 College of New Jersey Win 8-1 628.66 Mar 5th Philly Special1
164 Connecticut Loss 3-8 -258.56 Mar 5th Philly Special1
180 Dickinson Win 9-1 779.5 Mar 5th Philly Special1
141 West Chester Win 5-4 699.25 Mar 5th Philly Special1
35 SUNY-Binghamton Loss 4-9 889.16 Mar 25th New England Open1
73 Boston University Loss 8-9 931.8 Mar 25th New England Open1
188 Bowdoin Win 7-2 716.78 Mar 25th New England Open1
164 Connecticut Win 8-4 906.25 Mar 25th New England Open1
188 Bowdoin Win 7-4 612.94 Mar 26th New England Open1
87 Mount Holyoke Loss 6-8 654.84 Mar 26th New England Open1
136 Rhode Island Loss 5-6 479.28 Mar 26th New England Open1
**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)