#185 Northeastern-B (17-8)

avg: 1116.27  •  sd: 62.2  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
123 Bates Loss 5-8 890.89 Mar 8th Grand Northeast Kickoff 2025
253 Brown-B Win 7-4 1353.27 Mar 8th Grand Northeast Kickoff 2025
392 Middlebury-B** Win 11-0 738.34 Ignored Mar 8th Grand Northeast Kickoff 2025
376 New Hampshire** Win 10-1 874.79 Ignored Mar 8th Grand Northeast Kickoff 2025
293 Amherst Win 8-4 1285.82 Mar 9th Grand Northeast Kickoff 2025
123 Bates Loss 10-15 890.89 Mar 9th Grand Northeast Kickoff 2025
231 Colby Win 8-7 1052.19 Mar 9th Grand Northeast Kickoff 2025
90 Bowdoin Loss 7-13 921.4 Mar 29th New England Open 2025
329 Connecticut-B Win 13-3 1175 Mar 29th New England Open 2025
374 Harvard-B** Win 13-2 887.2 Ignored Mar 29th New England Open 2025
316 Massachusetts-Lowell Win 13-3 1219.5 Mar 29th New England Open 2025
284 Northeastern-C Win 11-7 1224.02 Mar 29th New England Open 2025
293 Amherst Win 12-9 1066.38 Mar 30th New England Open 2025
159 Brandeis Loss 7-15 617.2 Mar 30th New England Open 2025
250 Worcester Polytechnic Win 15-6 1467.13 Mar 30th New England Open 2025
411 Boston University-B** Win 12-3 397.87 Ignored Apr 12th New England Dev Mens Conferences 2025
253 Brown-B Win 10-7 1246.77 Apr 12th New England Dev Mens Conferences 2025
374 Harvard-B** Win 15-1 887.2 Ignored Apr 12th New England Dev Mens Conferences 2025
91 Vermont-B Loss 7-11 1011.4 Apr 12th New England Dev Mens Conferences 2025
253 Brown-B Win 11-9 1106.31 Apr 13th New England Dev Mens Conferences 2025
284 Northeastern-C Loss 7-10 367.47 Apr 13th New England Dev Mens Conferences 2025
116 Boston University Loss 8-12 922.46 May 3rd New England D I College Mens Regionals 2025
222 MIT Win 14-7 1559.01 May 3rd New England D I College Mens Regionals 2025
20 Tufts Loss 8-15 1430.36 May 3rd New England D I College Mens Regionals 2025
165 Massachusetts -B Win 13-12 1331.06 May 4th New England D I College Mens Regionals 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)