#16 Northeastern (13-13)

avg: 2056.69  •  sd: 54.63  •  top 16/20: 86.6%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
13 California Loss 8-9 1978.63 Feb 15th Presidents Day Invite 2025
41 California-San Diego Win 13-11 2021.15 Feb 15th Presidents Day Invite 2025
2 Oregon Loss 9-10 2260.23 Feb 15th Presidents Day Invite 2025
173 California-Davis** Win 13-0 1782.36 Ignored Feb 16th Presidents Day Invite 2025
34 Utah Win 12-7 2360.66 Feb 16th Presidents Day Invite 2025
2 Oregon Loss 10-13 2057.09 Feb 16th Presidents Day Invite 2025
8 Washington Win 12-9 2561.95 Feb 16th Presidents Day Invite 2025
5 Colorado Win 13-11 2587.75 Feb 17th Presidents Day Invite 2025
11 Oregon State Loss 12-13 2010.73 Feb 17th Presidents Day Invite 2025
4 North Carolina Loss 7-13 1802.3 Mar 1st Smoky Mountain Invite 2025
2 Oregon Loss 9-12 2039.86 Mar 1st Smoky Mountain Invite 2025
29 Pittsburgh Win 10-9 2003.81 Mar 1st Smoky Mountain Invite 2025
14 Texas Win 15-12 2365.59 Mar 1st Smoky Mountain Invite 2025
5 Colorado Loss 9-15 1843.43 Mar 2nd Smoky Mountain Invite 2025
11 Oregon State Loss 14-15 2010.73 Mar 2nd Smoky Mountain Invite 2025
20 Vermont Loss 10-15 1574.87 Mar 2nd Smoky Mountain Invite 2025
13 California Loss 8-13 1607.47 Mar 29th Easterns 2025
62 North Carolina State Win 13-7 2208.86 Mar 29th Easterns 2025
22 Penn State Loss 11-13 1764.12 Mar 29th Easterns 2025
1 Massachusetts Loss 5-13 1816.67 Mar 29th Easterns 2025
17 Brown Loss 11-13 1826.42 Mar 30th Easterns 2025
20 Vermont Win 15-12 2328.97 Mar 30th Easterns 2025
22 Penn State Win 15-13 2207.14 Mar 30th Easterns 2025
105 Boston University Win 15-9 1965.14 Apr 12th Metro Boston D I Mens Conferences 2025
214 MIT** Win 15-0 1620.68 Ignored Apr 12th Metro Boston D I Mens Conferences 2025
19 Tufts Win 12-9 2375.89 Apr 13th Metro Boston D I Mens Conferences 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)