#322 Central Connecticut State (9-15)

avg: 595.07  •  sd: 50.8  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
311 Swarthmore Win 9-7 912.05 Mar 1st Garden State 2025
316 Massachusetts-Lowell Loss 6-7 494.5 Mar 1st Garden State 2025
337 Pennsylvania Western Win 8-7 676.1 Mar 1st Garden State 2025
277 Salisbury Loss 7-8 644.52 Mar 1st Garden State 2025
359 Cornell-B Loss 8-10 135.11 Mar 2nd Garden State 2025
389 Stevens Tech Win 8-2 786.02 Mar 2nd Garden State 2025
411 Boston University-B Win 8-4 362.68 Mar 29th Ocean State Invite 2025
201 Tufts-B Loss 2-13 447.6 Mar 29th Ocean State Invite 2025
246 Connecticut College Win 6-5 1010.45 Mar 29th Ocean State Invite 2025
387 Providence Win 11-4 794.13 Mar 29th Ocean State Invite 2025
125 Maine** Loss 1-15 740.26 Ignored Mar 30th Ocean State Invite 2025
201 Tufts-B Loss 4-15 447.6 Mar 30th Ocean State Invite 2025
382 Southern Connecticut State Win 11-4 839.83 Apr 12th Hudson Valley D I Mens Conferences 2025
119 Connecticut Loss 7-9 1070.84 Apr 12th Hudson Valley D I Mens Conferences 2025
213 SUNY-Albany Loss 5-10 438.52 Apr 12th Hudson Valley D I Mens Conferences 2025
93 Yale** Loss 1-13 876.76 Ignored Apr 12th Hudson Valley D I Mens Conferences 2025
213 SUNY-Albany Loss 8-14 476.38 Apr 13th Hudson Valley D I Mens Conferences 2025
382 Southern Connecticut State Win 12-9 585.2 Apr 13th Hudson Valley D I Mens Conferences 2025
150 Toronto** Loss 5-15 662.59 Ignored Apr 26th Metro East D I College Mens Regionals 2025
51 Cornell** Loss 3-15 1106.16 Ignored Apr 26th Metro East D I College Mens Regionals 2025
216 Princeton Loss 9-15 481.58 Apr 26th Metro East D I College Mens Regionals 2025
331 Hofstra Win 11-10 695.89 Apr 27th Metro East D I College Mens Regionals 2025
329 Connecticut-B Loss 8-9 450 Apr 27th Metro East D I College Mens Regionals 2025
314 Rowan Loss 6-8 323.12 Apr 27th Metro East D I College Mens 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)