#225 Brandeis (9-11)

avg: 730.24  •  sd: 61.99  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
103 Bowdoin Loss 2-9 616 Mar 2nd Philly Special 2024
127 College of New Jersey Loss 2-7 544.42 Mar 2nd Philly Special 2024
213 SUNY-Albany Loss 3-7 166.14 Mar 2nd Philly Special 2024
71 Penn State-B** Loss 2-13 766.46 Ignored Mar 3rd Philly Special 2024
213 SUNY-Albany Loss 8-10 503.47 Mar 3rd Philly Special 2024
277 Stevens Tech Win 12-7 1017.96 Mar 3rd Philly Special 2024
143 Brown-B Loss 6-13 465.97 Mar 3rd Philly Special 2024
315 Bentley Win 12-5 881.98 Mar 23rd Ocean State Invite
295 Harvard-B Win 13-4 969.58 Mar 23rd Ocean State Invite
272 Northeastern-C Win 10-4 1107.98 Mar 23rd Ocean State Invite
312 Rutgers-B Win 8-5 741.89 Mar 23rd Ocean State Invite
181 Northeastern-B Loss 3-10 298.07 Mar 24th Ocean State Invite
182 Worcester Polytechnic Institute Loss 6-9 472.26 Mar 24th Ocean State Invite
183 Connecticut College Win 10-7 1278.36 Mar 30th New England Open 2024 Open Division
141 Bryant Loss 4-12 474.31 Mar 30th New England Open 2024 Open Division
272 Northeastern-C Win 10-7 897.64 Mar 30th New England Open 2024 Open Division
269 Western New England Win 10-9 637.32 Mar 30th New England Open 2024 Open Division
86 Bates Loss 5-13 717.07 Mar 31st New England Open 2024 Open Division
181 Northeastern-B Win 8-7 1023.07 Mar 31st New England Open 2024 Open Division
138 Tufts-B Loss 7-10 695.17 Mar 31st New England Open 2024 Open Division
**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)