#327 SUNY-Binghamton-B (10-15)

avg: 614.21  •  sd: 66.66  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
275 Central Connecticut State Loss 4-8 283.85 Feb 24th Bring The Huckus 2024
244 Dickinson Loss 3-8 379.09 Feb 24th Bring The Huckus 2024
331 Rutgers-B Loss 3-6 49.59 Feb 24th Bring The Huckus 2024
318 Swarthmore Loss 8-10 392.8 Feb 24th Bring The Huckus 2024
331 Rutgers-B Win 11-8 961.89 Feb 25th Bring The Huckus 2024
245 Skidmore Loss 8-13 481.91 Feb 25th Bring The Huckus 2024
374 New Jersey Tech Win 8-7 448.7 Mar 23rd King of New York 2024
345 Cornell-B Loss 4-7 37.94 Mar 24th King of New York 2024
244 Dickinson Loss 4-9 379.09 Mar 24th King of New York 2024
401 Siena Win 9-7 319.32 Mar 24th King of New York 2024
159 Rhode Island** Loss 5-12 688.15 Ignored Mar 30th Northeast Classic 2024
318 Swarthmore Win 9-8 780.47 Mar 30th Northeast Classic 2024
315 Vermont-C Win 13-7 1218.1 Mar 30th Northeast Classic 2024
283 Hofstra Loss 2-13 209.27 Mar 31st Northeast Classic 2024
378 SUNY-Buffalo-B Win 12-7 822.2 Mar 31st Northeast Classic 2024
345 Cornell-B Win 9-4 1134.1 Apr 13th Metro East Dev Mens Conferences 2024
378 SUNY-Buffalo-B Win 12-6 881 Apr 13th Metro East Dev Mens Conferences 2024
343 Connecticut-B Win 11-9 798.53 Apr 14th Metro East Dev Mens Conferences 2024
331 Rutgers-B Win 15-12 896.78 Apr 14th Metro East Dev Mens Conferences 2024
93 Princeton Loss 7-15 938.03 Apr 27th Metro East D I College Mens Regionals 2024
91 SUNY-Buffalo** Loss 3-15 943.75 Ignored Apr 27th Metro East D I College Mens Regionals 2024
131 Yale** Loss 6-15 779.37 Ignored Apr 27th Metro East D I College Mens Regionals 2024
275 Central Connecticut State Loss 9-10 723.66 Apr 28th Metro East D I College Mens Regionals 2024
283 Hofstra Loss 1-6 209.27 Apr 28th Metro East D I College Mens Regionals 2024
272 Rowan Loss 5-15 256.78 Apr 28th Metro East D I College Mens 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)