#166 Brandeis (12-8)

avg: 972.11  •  sd: 53.02  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
80 Rochester Loss 10-11 1215.49 Mar 1st D III River City Showdown 2025
45 Elon** Loss 4-13 1005.05 Ignored Mar 1st D III River City Showdown 2025
240 Xavier Win 13-6 1244.29 Mar 1st D III River City Showdown 2025
89 North Carolina-Asheville Loss 11-13 1073.28 Mar 1st D III River City Showdown 2025
163 Messiah Loss 10-11 856.6 Mar 2nd D III River City Showdown 2025
298 Navy Win 11-3 988.51 Mar 2nd D III River City Showdown 2025
143 Michigan Tech Loss 7-11 596.02 Mar 2nd D III River City Showdown 2025
334 Bentley** Win 15-3 794.03 Ignored Mar 22nd PBR State Open
290 Worcester Polytechnic Win 15-2 1013.7 Mar 22nd PBR State Open
218 MIT Win 10-9 864.3 Mar 22nd PBR State Open
144 Bates Win 10-8 1322.22 Mar 23rd PBR State Open
122 Boston University Loss 10-11 1042.78 Mar 23rd PBR State Open
112 Bowdoin Loss 5-9 675.52 Mar 23rd PBR State Open
290 Worcester Polytechnic Win 13-5 1013.7 Mar 29th New England Open 2025
365 Wentworth** Win 13-3 568.27 Ignored Mar 29th New England Open 2025
346 Western New England** Win 13-1 719.36 Ignored Mar 29th New England Open 2025
318 Clark Win 13-8 773.43 Mar 29th New England Open 2025
307 Amherst Win 13-7 897.6 Mar 30th New England Open 2025
112 Bowdoin Loss 7-15 604.58 Mar 30th New England Open 2025
207 Northeastern-B Win 15-7 1366.44 Mar 30th New England Open 2025
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)