#31 Santa Clara (10-5)

avg: 1353.68  •  sd: 53.19  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
2 Washington** Loss 1-13 1519.94 Jan 25th Santa Barbara Invite 2020
21 Western Washington Win 9-8 1593.96 Jan 25th Santa Barbara Invite 2020
10 Colorado State University Loss 8-13 1217.36 Jan 25th Santa Barbara Invite 2020
39 California-Santa Barbara Win 12-10 1554.59 Jan 25th Santa Barbara Invite 2020
14 British Columbia Loss 9-13 1224.68 Jan 26th Santa Barbara Invite 2020
50 Northwestern Win 11-10 1325.5 Jan 26th Santa Barbara Invite 2020
23 California-San Diego Loss 5-12 848.11 Jan 26th Santa Barbara Invite 2020
125 UCLA-B** Win 13-4 1158.98 Ignored Feb 1st 2020 Mens Presidents Day Qualifier
74 Arizona State-B Win 11-3 1581.39 Feb 1st 2020 Mens Presidents Day Qualifier
127 California-Santa Barbara-B** Win 13-0 1155.9 Ignored Feb 1st 2020 Mens Presidents Day Qualifier
88 Cal State-Long Beach Win 10-4 1479.83 Feb 1st 2020 Mens Presidents Day Qualifier
110 California-Irvine Win 11-7 1196.72 Feb 2nd 2020 Mens Presidents Day Qualifier
75 Arizona State Win 10-6 1468.6 Feb 2nd 2020 Mens Presidents Day Qualifier
77 Occidental Win 11-7 1422.04 Feb 2nd 2020 Mens Presidents Day Qualifier
22 California-Santa Cruz Loss 7-9 1175.35 Feb 2nd 2020 Mens Presidents Day Qualifier
**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)