#40 Santa Clara (10-5)

avg: 1611.92  •  sd: 62.77  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
14 Colorado State Loss 8-13 1412 Jan 25th Santa Barbara Invite 2020
28 California-Santa Barbara Win 12-10 1954.61 Jan 25th Santa Barbara Invite 2020
2 Washington** Loss 1-13 1676.45 Ignored Jan 25th Santa Barbara Invite 2020
27 Western Washington Win 9-8 1844.07 Jan 25th Santa Barbara Invite 2020
33 Northwestern Win 11-10 1790.39 Jan 26th Santa Barbara Invite 2020
39 California-San Diego Loss 5-12 1017.36 Jan 26th Santa Barbara Invite 2020
12 British Columbia Loss 9-13 1516.24 Jan 26th Santa Barbara Invite 2020
86 Arizona State-B-B Win 11-3 1897.07 Feb 1st 2020 Mens Presidents Day Qualifier
200 California-Santa Barbara-B** Win 13-0 1434.01 Ignored Feb 1st 2020 Mens Presidents Day Qualifier
185 Cal State-Long Beach** Win 10-4 1500.75 Ignored Feb 1st 2020 Mens Presidents Day Qualifier
163 UCLA-B** Win 13-4 1580.45 Ignored Feb 1st 2020 Mens Presidents Day Qualifier
121 Arizona State Win 10-6 1626.77 Feb 2nd 2020 Mens Presidents Day Qualifier
36 California-Santa Cruz Loss 7-9 1349.5 Feb 2nd 2020 Mens Presidents Day Qualifier
159 California-Irvine Win 11-7 1458.44 Feb 2nd 2020 Mens Presidents Day Qualifier
68 Occidental Win 11-7 1852.14 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)