#442 SUNY Oneonta-B (2-9)

avg: -400.84  •  sd: 142.57  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
428 American-B Loss 3-7 -627.16 Mar 23rd Towson Cup 2019
440 Lancaster Bible Loss 5-7 -702.3 Mar 23rd Towson Cup 2019
270 Delaware-B** Loss 2-13 182.13 Ignored Mar 23rd Towson Cup 2019
438 St Mary's (Maryland) Loss 5-7 -560.51 Mar 24th Towson Cup 2019
225 SUNY-Oneonta** Loss 1-15 316.54 Ignored Mar 24th Towson Cup 2019
437 Towson -B Win 11-6 318.91 Mar 24th Towson Cup 2019
372 Rutgers-B** Loss 1-13 -237.41 Ignored Mar 30th Tea Cup 2019
337 Southern Connecticut State** Loss 1-13 -65.87 Ignored Mar 30th Tea Cup 2019
445 Amherst College-B Win 12-3 -192.83 Mar 30th Tea Cup 2019
211 University of Massachusetts Amherst-B** Loss 0-13 352.02 Ignored Mar 30th Tea Cup 2019
436 Yale-B Loss 4-9 -813.49 Mar 31st Tea Cup 2019
**Blowout Eligible

FAQ

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
  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
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)