#60 Cornell (7-8)

avg: 1473.23  •  sd: 54.9  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
5 Washington Loss 6-13 1451.41 Feb 17th Presidents Day Invitational Tournament 2018
148 San Diego State Win 13-2 1747.07 Feb 17th Presidents Day Invitational Tournament 2018
143 California-San Diego Win 8-5 1614.52 Feb 17th Presidents Day Invitational Tournament 2018
55 Oregon State Loss 9-10 1393.18 Feb 17th Presidents Day Invitational Tournament 2018
20 Cal Poly-SLO Loss 6-14 1243.12 Feb 18th Presidents Day Invitational Tournament 2018
3 Oregon Loss 7-14 1605.88 Feb 18th Presidents Day Invitational Tournament 2018
44 Illinois Loss 4-15 989.03 Feb 19th Presidents Day Invitational Tournament 2018
76 Chicago Win 9-8 1540.31 Feb 19th Presidents Day Invitational Tournament 2018
53 UCLA Win 9-8 1659.42 Feb 19th Presidents Day Invitational Tournament 2018
61 James Madison Loss 12-13 1347.52 Mar 17th Oak Creek Invite 2018
109 Williams Win 13-10 1624.35 Mar 17th Oak Creek Invite 2018
149 Davidson Win 13-8 1637.02 Mar 17th Oak Creek Invite 2018
34 William & Mary Loss 13-14 1523.2 Mar 18th Oak Creek Invite 2018
78 Georgetown Win 13-11 1643.91 Mar 18th Oak Creek Invite 2018
54 Mary Washington Loss 9-13 1105.66 Mar 18th Oak Creek Invite 2018
**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)