#59 Santa Clara (12-7)

avg: 1499.77  •  sd: 73.74  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
316 Cal Poly-SLO-B** Win 13-4 1123.91 Ignored Feb 3rd Presidents Day Qualifier 2018
332 California-San Diego-B** Win 13-1 1042.18 Ignored Feb 3rd Presidents Day Qualifier 2018
195 Sonoma State Win 13-2 1563.31 Feb 3rd Presidents Day Qualifier 2018
87 Las Positas Win 12-6 1972.51 Feb 4th Presidents Day Qualifier 2018
333 California-Davis-B** Win 13-1 1030.48 Ignored Feb 4th Presidents Day Qualifier 2018
131 Chico State Win 12-7 1709.15 Feb 4th Presidents Day Qualifier 2018
146 Nevada-Reno Win 11-6 1696 Feb 10th Stanford Open 2018
87 Las Positas Win 10-9 1518.2 Feb 10th Stanford Open 2018
316 Cal Poly-SLO-B** Win 13-1 1123.91 Ignored Feb 10th Stanford Open 2018
131 Chico State Win 13-9 1607.21 Feb 11th Stanford Open 2018
26 Texas-Dallas Loss 11-12 1604.02 Feb 11th Stanford Open 2018
69 Carleton College-GoP Loss 11-12 1324.46 Feb 11th Stanford Open 2018
44 Illinois Loss 7-13 1031.5 Feb 17th Presidents Day Invitational Tournament 2018
3 Oregon Loss 8-13 1692.61 Feb 17th Presidents Day Invitational Tournament 2018
53 UCLA Win 13-10 1862.56 Feb 17th Presidents Day Invitational Tournament 2018
67 Utah Loss 11-12 1332.97 Feb 17th Presidents Day Invitational Tournament 2018
143 California-San Diego Win 11-8 1526.53 Feb 18th Presidents Day Invitational Tournament 2018
76 Chicago Loss 7-14 832.43 Feb 18th Presidents Day Invitational Tournament 2018
44 Illinois Loss 9-11 1339.82 Feb 19th Presidents Day Invitational Tournament 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)