#317 Northeastern-C (7-11)

avg: 655.82  •  sd: 67.62  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
359 Bentley Win 9-6 872.53 Mar 23rd Ocean State Invite
259 Brandeis Loss 4-10 314.93 Mar 23rd Ocean State Invite
329 Harvard-B Win 6-5 725.16 Mar 23rd Ocean State Invite
331 Rutgers-B Loss 6-9 177.72 Mar 23rd Ocean State Invite
112 Boston College** Loss 3-10 855.9 Ignored Mar 24th Ocean State Invite
275 Central Connecticut State Win 9-6 1267.23 Mar 24th Ocean State Invite
259 Brandeis Loss 7-10 525.27 Mar 30th New England Open 2024 Open Division
142 Bryant** Loss 3-8 740.78 Ignored Mar 30th New England Open 2024 Open Division
199 Connecticut College Loss 10-11 1000.75 Mar 30th New England Open 2024 Open Division
343 Connecticut-B Win 10-7 938.99 Mar 30th New England Open 2024 Open Division
343 Connecticut-B Win 6-5 674.33 Mar 31st New England Open 2024 Open Division
293 Maine Loss 7-9 466.28 Mar 31st New England Open 2024 Open Division
329 Harvard-B Win 10-7 989.83 Apr 13th Metro Boston Dev Mens Conferences 2024
377 MIT-B Loss 5-6 182.62 Apr 13th Metro Boston Dev Mens Conferences 2024
138 Tufts-B** Loss 5-13 763.74 Ignored Apr 13th Metro Boston Dev Mens Conferences 2024
329 Harvard-B Loss 9-10 475.16 Apr 14th Metro Boston Dev Mens Conferences 2024
210 Northeastern-B Loss 4-10 480.66 Apr 14th Metro Boston Dev Mens Conferences 2024
398 Tufts-C Win 13-6 684.69 Apr 14th Metro Boston Dev Mens Conferences 2024
**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)