#233 Northeastern-B (1-14)

avg: 131.43  •  sd: 85.18  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
176 Bentley Loss 4-5 452.18 Mar 23rd Get Down to Business
201 Stonehill Win 5-2 1007.04 Mar 23rd Get Down to Business
94 Rhode Island** Loss 1-10 501.95 Ignored Mar 23rd Get Down to Business
218 Rensselaer Polytech Loss 3-5 -165.62 Mar 23rd Get Down to Business
158 Bates Loss 3-11 71.71 Mar 29th New England Open 2025
148 Boston University Loss 1-11 123.49 Mar 29th New England Open 2025
191 Brandeis Loss 4-8 -76.36 Mar 29th New England Open 2025
94 Rhode Island** Loss 0-13 501.95 Ignored Mar 29th New England Open 2025
176 Bentley Loss 3-8 -22.82 Mar 30th New England Open 2025
137 Bowdoin** Loss 2-9 204.27 Ignored Mar 30th New England Open 2025
206 Clark Loss 6-9 -108.93 Mar 30th New England Open 2025
120 Brown-B** Loss 2-10 337.51 Ignored Apr 12th New England Dev Womens Conferences 2025
153 Tufts-B Loss 4-6 316.25 Apr 12th New England Dev Womens Conferences 2025
87 Vermont-B** Loss 2-10 539.77 Ignored Apr 12th New England Dev Womens Conferences 2025
183 Vermont-C Loss 3-6 -20.92 Apr 12th New England Dev Womens 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)