#150 Boston College (6-17)

avg: 693.33  •  sd: 83.66  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
34 Cornell** Loss 2-13 1041.42 Ignored Feb 22nd 2025 Commonwealth Cup Weekend 2
70 Carnegie Mellon Loss 7-10 889.48 Feb 22nd 2025 Commonwealth Cup Weekend 2
42 Duke** Loss 2-9 965.5 Ignored Feb 22nd 2025 Commonwealth Cup Weekend 2
63 Chicago** Loss 2-11 744.44 Ignored Feb 22nd 2025 Commonwealth Cup Weekend 2
53 Maryland** Loss 6-14 857.11 Ignored Feb 23rd 2025 Commonwealth Cup Weekend 2
129 Harvard Win 11-9 1082.48 Feb 23rd 2025 Commonwealth Cup Weekend 2
118 Swarthmore Win 8-7 1064.23 Mar 22nd Jersey Devil 2025
89 Williams Loss 5-9 589.68 Mar 22nd Jersey Devil 2025
175 RIT Loss 5-11 -18.2 Mar 22nd Jersey Devil 2025
62 Brown** Loss 0-13 751.28 Ignored Mar 22nd Jersey Devil 2025
175 RIT Win 10-7 971.46 Mar 23rd Jersey Devil 2025
62 Brown** Loss 1-12 751.28 Ignored Mar 23rd Jersey Devil 2025
118 Swarthmore Loss 5-9 410.17 Mar 23rd Jersey Devil 2025
25 Northeastern** Loss 1-15 1202.53 Ignored Apr 13th Metro Boston D I Womens Conferences 2025
38 MIT** Loss 1-15 988.82 Ignored Apr 13th Metro Boston D I Womens Conferences 2025
147 Boston University Win 11-7 1189.5 Apr 13th Metro Boston D I Womens Conferences 2025
129 Harvard Win 12-9 1178.64 Apr 13th Metro Boston D I Womens Conferences 2025
3 Tufts** Loss 1-15 1888.77 Ignored Apr 26th New England D I College Womens Regionals 2025
136 Massachusetts Loss 10-12 560.1 Apr 26th New England D I College Womens Regionals 2025
93 Rhode Island Loss 7-15 502.07 Apr 26th New England D I College Womens Regionals 2025
147 Boston University Loss 9-10 597.6 Apr 27th New England D I College Womens Regionals 2025
241 Maine Win 10-6 546.95 Apr 27th New England D I College Womens Regionals 2025
119 Brown-B Loss 4-14 337.46 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)