#39 SUNY-Binghamton (16-8)

avg: 1720.35  •  sd: 59.43  •  top 16/20: 0.1%

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# Opponent Result Game Rating Status Date Event
112 Carnegie Mellon Win 14-12 1354.99 Feb 24th Commonwealth Cup Weekend 2 2024
12 Michigan Loss 9-15 1684.21 Feb 24th Commonwealth Cup Weekend 2 2024
56 North Carolina State Loss 7-12 1023.26 Feb 24th Commonwealth Cup Weekend 2 2024
8 Tufts Loss 5-10 1781.8 Feb 25th Commonwealth Cup Weekend 2 2024
24 Ohio State Loss 3-12 1293.87 Feb 25th Commonwealth Cup Weekend 2 2024
22 Pittsburgh Loss 3-10 1334.21 Feb 25th Commonwealth Cup Weekend 2 2024
44 Yale Win 10-9 1771.48 Feb 25th Commonwealth Cup Weekend 2 2024
27 Brown Loss 8-9 1744.14 Mar 30th East Coast Invite 2024
71 Columbia Win 11-8 1773.43 Mar 30th East Coast Invite 2024
6 North Carolina Loss 7-14 1916.51 Mar 30th East Coast Invite 2024
108 West Chester Win 13-6 1779.47 Mar 30th East Coast Invite 2024
112 Carnegie Mellon Win 12-6 1713.34 Mar 31st East Coast Invite 2024
58 Cornell Win 10-9 1651.29 Mar 31st East Coast Invite 2024
29 UCLA Loss 7-8 1728.18 Mar 31st East Coast Invite 2024
58 Cornell Win 13-6 2126.29 Apr 20th Western NY D I Womens Conferences 2024
150 RIT** Win 11-2 1468.05 Ignored Apr 20th Western NY D I Womens Conferences 2024
191 Syracuse** Win 10-1 1152.54 Ignored Apr 20th Western NY D I Womens Conferences 2024
110 Rutgers Win 11-6 1711.81 Apr 27th Metro East D I College Womens Regionals 2024
71 Columbia Win 9-8 1532.82 Apr 27th Metro East D I College Womens Regionals 2024
54 Ottawa Win 13-8 2051.07 Apr 27th Metro East D I College Womens Regionals 2024
180 SUNY-Buffalo** Win 13-5 1220.02 Ignored Apr 27th Metro East D I College Womens Regionals 2024
57 Connecticut Win 9-8 1654.14 Apr 28th Metro East D I College Womens Regionals 2024
58 Cornell Win 15-12 1826.78 Apr 28th Metro East D I College Womens Regionals 2024
44 Yale Win 13-10 1974.63 Apr 28th Metro East D I College Womens Regionals 2024
**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)