#284 Northeastern-C (14-13)

avg: 757.13  •  sd: 60.16  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
229 Harvard Loss 5-8 485.43 Mar 9th MIT Invite
374 Harvard-B Win 7-5 615.34 Mar 9th MIT Invite
222 MIT Win 9-8 1101.13 Mar 9th MIT Invite
361 MIT-B Loss 7-9 105.69 Mar 9th MIT Invite
356 Lehigh-B Win 8-4 976.25 Mar 22nd Jersey Devil 2025
386 New Jersey Tech Win 10-7 584.22 Mar 22nd Jersey Devil 2025
332 Villanova Loss 8-9 444.58 Mar 22nd Jersey Devil 2025
209 Penn State-B Loss 8-9 899.7 Mar 22nd Jersey Devil 2025
384 West Chester-B Win 10-8 487.21 Mar 22nd Jersey Devil 2025
405 Dartmouth-B** Win 8-3 566.19 Ignored Mar 23rd Jersey Devil 2025
209 Penn State-B Loss 12-15 724.21 Mar 23rd Jersey Devil 2025
90 Bowdoin** Loss 5-13 878.93 Ignored Mar 29th New England Open 2025
329 Connecticut-B Win 9-8 700 Mar 29th New England Open 2025
374 Harvard-B Win 10-8 549.87 Mar 29th New England Open 2025
185 Northeastern-B Loss 7-11 649.38 Mar 29th New England Open 2025
316 Massachusetts-Lowell Win 9-8 744.5 Mar 29th New England Open 2025
293 Amherst Win 13-11 949.85 Mar 30th New England Open 2025
250 Worcester Polytechnic Loss 5-14 267.13 Mar 30th New England Open 2025
361 MIT-B Win 12-2 985.03 Apr 12th New England Dev Mens Conferences 2025
201 Tufts-B Win 10-9 1172.6 Apr 12th New England Dev Mens Conferences 2025
165 Massachusetts -B Loss 5-10 632.16 Apr 12th New England Dev Mens Conferences 2025
409 Tufts-C** Win 11-3 455.94 Ignored Apr 12th New England Dev Mens Conferences 2025
185 Northeastern-B Win 10-7 1505.94 Apr 13th New England Dev Mens Conferences 2025
165 Massachusetts -B Loss 7-10 816.39 Apr 13th New England Dev Mens Conferences 2025
84 Boston College Loss 8-15 942.75 May 3rd New England D I College Mens Regionals 2025
19 Vermont** Loss 5-15 1398.44 Ignored May 3rd New England D I College Mens Regionals 2025
165 Massachusetts -B Loss 9-15 690.58 May 3rd 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)