#305 Northeastern-C (10-8)

avg: 358.84  •  sd: 61.52  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
222 Harvard Loss 5-8 256.3 Mar 9th MIT Invite
356 Harvard-B Win 7-5 357.08 Mar 9th MIT Invite
218 MIT Win 9-8 864.3 Mar 9th MIT Invite
340 MIT-B Loss 7-9 -119.98 Mar 9th MIT Invite
349 Lehigh-B Win 8-4 678.87 Mar 22nd Jersey Devil 2025
- New Jersey Tech Win 10-7 594.95 Mar 22nd Jersey Devil 2025
324 Villanova Loss 8-9 135.06 Mar 22nd Jersey Devil 2025
234 Penn State-B Loss 8-9 531.34 Mar 22nd Jersey Devil 2025
372 West Chester-B Win 10-8 185.25 Mar 22nd Jersey Devil 2025
366 Dartmouth-B Win 8-3 532.46 Mar 23rd Jersey Devil 2025
234 Penn State-B Loss 12-15 355.85 Mar 23rd Jersey Devil 2025
112 Bowdoin** Loss 5-13 604.58 Ignored Mar 29th New England Open 2025
333 Connecticut-B Win 9-8 342.87 Mar 29th New England Open 2025
356 Harvard-B Win 10-8 291.6 Mar 29th New England Open 2025
207 Northeastern-B Loss 7-11 299.55 Mar 29th New England Open 2025
299 Massachusetts-Lowell Win 9-8 510.01 Mar 29th New England Open 2025
307 Amherst Win 13-11 568.91 Mar 30th New England Open 2025
290 Worcester Polytechnic Loss 5-14 -186.3 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)