#151 SUNY-Binghamton (11-9)

avg: 1162.14  •  sd: 82.29  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
85 Richmond Loss 12-13 1304.7 Feb 2nd Mid Atlantic Warmup 2019
158 Lehigh Win 13-9 1547.64 Feb 2nd Mid Atlantic Warmup 2019
197 George Mason Win 13-6 1601.39 Feb 2nd Mid Atlantic Warmup 2019
195 George Washington Win 13-4 1603.81 Feb 2nd Mid Atlantic Warmup 2019
87 Case Western Reserve Loss 6-15 822.56 Feb 3rd Mid Atlantic Warmup 2019
32 William & Mary Loss 10-15 1293.08 Feb 3rd Mid Atlantic Warmup 2019
129 Marist Loss 8-11 906.19 Mar 9th No Sleep Till Brooklyn
290 Hofstra Win 11-4 1308.47 Mar 9th No Sleep Till Brooklyn
389 Cornell-B** Win 11-3 874.9 Ignored Mar 9th No Sleep Till Brooklyn
262 Tufts-B Win 11-2 1419.8 Mar 9th No Sleep Till Brooklyn
149 SUNY-Stony Brook Win 10-7 1568.88 Mar 9th No Sleep Till Brooklyn
242 Rowan Win 9-7 1165.79 Mar 10th No Sleep Till Brooklyn
213 Columbia Loss 4-9 348.26 Mar 10th No Sleep Till Brooklyn
187 NYU Loss 4-8 465.79 Mar 10th No Sleep Till Brooklyn
139 Pennsylvania Loss 6-13 629.67 Mar 30th Atlantic Coast Open 2019
118 MIT Loss 10-13 959.59 Mar 30th Atlantic Coast Open 2019
345 American** Win 13-3 1101.81 Ignored Mar 30th Atlantic Coast Open 2019
91 Mary Washington Loss 9-10 1257.51 Mar 30th Atlantic Coast Open 2019
242 Rowan Win 15-6 1486.46 Mar 31st Atlantic Coast Open 2019
166 Virginia Commonwealth Win 12-10 1329.95 Mar 31st Atlantic Coast Open 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)