#77 Mount Holyoke (12-5)

avg: 1222.52  •  sd: 47.91  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
79 Columbia Win 9-8 1343.99 Mar 29th East Coast Invite 2025
36 Haverford/Bryn Mawr Loss 7-10 1228.81 Mar 29th East Coast Invite 2025
98 Lehigh Win 10-6 1586.34 Mar 29th East Coast Invite 2025
53 Maryland Loss 4-11 856.32 Mar 29th East Coast Invite 2025
47 American Loss 8-11 1175.96 Mar 30th East Coast Invite 2025
64 Connecticut Loss 5-6 1194.26 Mar 30th East Coast Invite 2025
105 Amherst Win 9-8 1165.14 Apr 13th South New England D III Womens Conferences 2025
206 Clark Win 9-4 909.64 Apr 13th South New England D III Womens Conferences 2025
90 Williams Win 9-6 1537.16 Apr 13th South New England D III Womens Conferences 2025
166 Smith Win 7-4 1124.41 Apr 13th South New England D III Womens Conferences 2025
105 Amherst Win 10-9 1165.14 Apr 26th New England D III College Womens Regionals 2025
191 Brandeis** Win 12-4 1088.45 Ignored Apr 26th New England D III College Womens Regionals 2025
149 Dartmouth Win 14-4 1308.35 Apr 26th New England D III College Womens Regionals 2025
51 Middlebury Loss 6-10 985.39 Apr 26th New England D III College Womens Regionals 2025
105 Amherst Win 11-7 1507.03 Apr 27th New England D III College Womens Regionals 2025
158 Bates Win 10-6 1167.87 Apr 27th New England D III College Womens Regionals 2025
90 Williams Win 8-7 1243.59 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)