#120 Brown-B (11-6)

avg: 937.51  •  sd: 79.09  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
47 American Win 8-6 1842.06 Mar 1st Cherry Blossom Classic 2025
140 George Washington Loss 7-8 666.33 Mar 1st Cherry Blossom Classic 2025
53 Maryland Loss 5-11 856.32 Mar 1st Cherry Blossom Classic 2025
245 Pennsylvania-B** Win 6-2 635.18 Ignored Mar 1st Cherry Blossom Classic 2025
169 Delaware Win 10-7 999.48 Mar 2nd Cherry Blossom Classic 2025
239 William & Mary-B** Win 13-1 676.63 Ignored Mar 2nd Cherry Blossom Classic 2025
245 Pennsylvania-B** Win 15-1 635.18 Ignored Mar 2nd Cherry Blossom Classic 2025
87 Vermont-B Loss 4-8 574.96 Apr 12th New England Dev Womens Conferences 2025
183 Vermont-C Win 7-4 1021.94 Apr 12th New England Dev Womens Conferences 2025
233 Northeastern-B** Win 10-2 731.43 Ignored Apr 12th New England Dev Womens Conferences 2025
153 Tufts-B Win 7-6 806.86 Apr 13th New England Dev Womens Conferences 2025
87 Vermont-B Loss 5-8 686.16 Apr 13th New England Dev Womens Conferences 2025
183 Vermont-C Win 9-6 944.35 Apr 13th New England Dev Womens Conferences 2025
148 Boston University Loss 10-12 485.37 Apr 26th New England D I College Womens Regionals 2025
38 MIT** Loss 4-12 987.6 Ignored Apr 26th New England D I College Womens Regionals 2025
151 Boston College Win 14-4 1293.39 Apr 27th New England D I College Womens Regionals 2025
174 New Hampshire Win 15-6 1181.82 Apr 27th New England D I College Womens 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)