#129 Harvard (9-14)

avg: 833.28  •  sd: 82.13  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
64 Connecticut Loss 3-15 719.82 Feb 22nd 2025 Commonwealth Cup Weekend 2
26 Georgetown** Loss 5-15 1201 Ignored Feb 22nd 2025 Commonwealth Cup Weekend 2
113 West Chester Loss 6-9 533.4 Feb 22nd 2025 Commonwealth Cup Weekend 2
150 Boston College Loss 9-11 444.12 Feb 23rd 2025 Commonwealth Cup Weekend 2
70 Carnegie Mellon Win 8-7 1404.15 Feb 23rd 2025 Commonwealth Cup Weekend 2
59 Central Florida Loss 1-15 797.07 Mar 15th Tally Classic XIX
214 Minnesota-Duluth Win 8-4 832.98 Mar 15th Tally Classic XIX
177 Tulane Loss 7-8 447.18 Mar 15th Tally Classic XIX
134 Bowdoin Win 8-7 929.28 Mar 29th New England Open 2025
190 Brandeis Win 11-3 1088.54 Mar 29th New England Open 2025
47 McGill** Loss 4-12 908.53 Mar 29th New England Open 2025
157 Bates Win 10-5 1245.63 Mar 30th New England Open 2025
137 Rutgers Win 7-6 917.75 Mar 30th New England Open 2025
47 McGill Loss 6-11 961.84 Mar 30th New England Open 2025
150 Boston College Loss 9-12 347.96 Apr 13th Metro Boston D I Womens Conferences 2025
147 Boston University Win 11-10 847.6 Apr 13th Metro Boston D I Womens Conferences 2025
3 Tufts** Loss 3-15 1888.77 Ignored Apr 13th Metro Boston D I Womens Conferences 2025
38 MIT** Loss 2-11 988.82 Ignored Apr 13th Metro Boston D I Womens Conferences 2025
5 Vermont** Loss 0-15 1689.71 Ignored Apr 26th New England D I College Womens Regionals 2025
93 Rhode Island Loss 10-12 863.94 Apr 26th New England D I College Womens Regionals 2025
136 Massachusetts Loss 7-10 408.55 Apr 26th New England D I College Womens Regionals 2025
147 Boston University Win 11-7 1189.5 Apr 27th New England D I College Womens Regionals 2025
174 New Hampshire Win 11-8 947.54 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)