#389 Cornell-B (5-13)

avg: 274.9  •  sd: 81.45  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
242 Rowan Loss 6-11 339.76 Mar 9th No Sleep Till Brooklyn
290 Hofstra Loss 4-7 212.31 Mar 9th No Sleep Till Brooklyn
213 Columbia** Loss 4-10 348.26 Ignored Mar 9th No Sleep Till Brooklyn
151 SUNY-Binghamton** Loss 3-11 562.14 Ignored Mar 9th No Sleep Till Brooklyn
149 SUNY-Stony Brook** Loss 2-11 579.21 Ignored Mar 9th No Sleep Till Brooklyn
129 Marist Loss 5-8 818.2 Mar 10th No Sleep Till Brooklyn
262 Tufts-B Win 8-7 944.8 Mar 10th No Sleep Till Brooklyn
439 Rhode Island-B Win 8-5 162.83 Mar 23rd Spring Awakening 8
422 Saint Joseph's Win 10-4 667.09 Mar 23rd Spring Awakening 8
393 Susquehanna Loss 4-9 -332.4 Mar 23rd Spring Awakening 8
443 Rensselaer Polytech-B Win 13-6 154.09 Mar 24th Spring Awakening 8
422 Saint Joseph's Loss 8-9 -57.91 Mar 24th Spring Awakening 8
174 Cedarville** Loss 2-13 467.46 Ignored Mar 30th I 85 Rodeo 2019
279 Maryland-B Loss 4-12 158.09 Mar 30th I 85 Rodeo 2019
318 Virginia Tech-B Loss 6-13 -3.5 Mar 30th I 85 Rodeo 2019
349 William & Mary-B Loss 6-11 -61.24 Mar 31st I 85 Rodeo 2019
365 Virginia-B Win 13-9 829.51 Mar 31st I 85 Rodeo 2019
338 Wake Forest Loss 9-12 188.23 Mar 31st I 85 Rodeo 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)