#163 SUNY-Geneseo (8-17)

avg: 646.2  •  sd: 57.09  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
36 Haverford/Bryn Mawr** Loss 1-9 1018.48 Ignored Feb 22nd Bring The Huckus 2025
96 Ithaca Loss 1-8 493.91 Feb 22nd Bring The Huckus 2025
98 Lehigh Loss 3-9 490.18 Feb 22nd Bring The Huckus 2025
39 Wesleyan** Loss 1-9 986.83 Ignored Feb 22nd Bring The Huckus 2025
96 Ithaca Win 7-6 1218.91 Feb 23rd Bring The Huckus 2025
98 Lehigh Loss 7-10 700.51 Feb 23rd Bring The Huckus 2025
39 Wesleyan** Loss 1-15 986.83 Ignored Feb 23rd Bring The Huckus 2025
226 SUNY-Cortland Win 10-4 803.23 Mar 29th Northeast Classic 2025
204 Vassar Win 12-2 987.94 Mar 29th Northeast Classic 2025
161 Syracuse Win 7-4 1159.47 Mar 29th Northeast Classic 2025
184 Skidmore Win 6-5 646.35 Mar 29th Northeast Classic 2025
138 Massachusetts Win 8-6 1098.6 Mar 30th Northeast Classic 2025
56 Rochester** Loss 2-13 821.56 Ignored Mar 30th Northeast Classic 2025
112 SUNY-Binghamton Loss 3-7 370.01 Mar 30th Northeast Classic 2025
86 Wellesley Loss 5-11 543.71 Mar 30th Northeast Classic 2025
122 Colgate Loss 4-7 431.12 Apr 12th Western NY D III Womens Conferences 2025
154 Hamilton Loss 7-8 556.07 Apr 12th Western NY D III Womens Conferences 2025
96 Ithaca Loss 5-8 640.31 Apr 12th Western NY D III Womens Conferences 2025
56 Rochester** Loss 3-11 821.56 Ignored Apr 12th Western NY D III Womens Conferences 2025
154 Hamilton Loss 4-7 184.91 Apr 26th Metro East D III College Womens Regionals 2025
218 Rensselaer Polytech Win 5-3 671.51 Apr 26th Metro East D III College Womens Regionals 2025
56 Rochester** Loss 0-13 821.56 Ignored Apr 26th Metro East D III College Womens Regionals 2025
184 Skidmore Win 8-6 821.84 Apr 26th Metro East D III College Womens Regionals 2025
154 Hamilton Loss 7-11 214.17 Apr 27th Metro East D III College Womens Regionals 2025
96 Ithaca Loss 4-8 529.1 Apr 27th Metro East 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)