#396 SUNY-Binghamton-B (4-8)

avg: 251.31  •  sd: 95.45  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
372 Rutgers-B Loss 2-13 -237.41 Mar 9th Atlantic City 9
201 Brown-B** Loss 4-13 382.84 Ignored Mar 9th Atlantic City 9
436 Yale-B Win 11-5 386.51 Mar 9th Atlantic City 9
397 SUNY-Albany-B Loss 6-13 -359.51 Mar 9th Atlantic City 9
245 Stevens Tech** Loss 4-12 276.22 Ignored Mar 10th Atlantic City 9
436 Yale-B Win 11-5 386.51 Mar 10th Atlantic City 9
397 SUNY-Albany-B Win 6-3 787.18 Mar 10th Atlantic City 9
413 Siena Win 10-4 748.57 Mar 30th Uprising 8
190 Maine** Loss 3-13 425.06 Mar 30th Uprising 8
204 SUNY-Buffalo** Loss 1-13 371.8 Ignored Mar 30th Uprising 8
413 Siena Loss 7-8 23.57 Mar 31st Uprising 8
204 SUNY-Buffalo** Loss 5-15 371.8 Ignored Mar 31st Uprising 8
**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)