#157 Bates (7-13)

avg: 671.73  •  sd: 58.17  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
161 Colby Loss 7-8 534.88 Mar 9th Too Hot to Handle
85 Wellesley Loss 2-11 543.84 Mar 9th Too Hot to Handle
47 McGill** Loss 3-8 908.53 Ignored Mar 9th Too Hot to Handle
174 New Hampshire Win 7-6 706.93 Mar 9th Too Hot to Handle
147 Boston University Win 6-5 847.6 Mar 29th New England Open 2025
93 Rhode Island Loss 4-8 537.26 Mar 29th New England Open 2025
232 Northeastern-B Win 11-3 731.39 Mar 29th New England Open 2025
129 Harvard Loss 5-10 259.38 Mar 30th New England Open 2025
174 New Hampshire Loss 7-8 456.93 Mar 30th New England Open 2025
147 Boston University Loss 8-10 459.94 Mar 30th New England Open 2025
161 Colby Loss 5-8 206.28 Apr 12th North New England D III Womens Conferences 2025
51 Middlebury** Loss 2-11 881.68 Ignored Apr 12th North New England D III Womens Conferences 2025
148 Dartmouth Win 8-6 1008.93 Apr 12th North New England D III Womens Conferences 2025
134 Bowdoin Win 10-7 1193.94 Apr 12th North New England D III Womens Conferences 2025
89 Williams Loss 4-9 518.74 Apr 26th New England D III College Womens Regionals 2025
85 Wellesley Loss 4-9 543.84 Apr 26th New England D III College Womens Regionals 2025
201 Stonehill Win 11-7 874.02 Apr 26th New England D III College Womens Regionals 2025
165 Smith Win 10-6 1124.55 Apr 26th New England D III College Womens Regionals 2025
75 Mount Holyoke Loss 6-10 726.64 Apr 27th New England D III College Womens Regionals 2025
85 Wellesley Loss 6-12 564.53 Apr 27th New England D III 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)