#45 California-Santa Barbara (8-12)

avg: 1663.25  •  sd: 75.89  •  top 16/20: 0.2%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
6 Brigham Young Loss 10-13 1806.59 Jan 25th Santa Barbara Invite 2019
50 Stanford Win 13-11 1861.58 Jan 26th Santa Barbara Invite 2019
56 California-San Diego Win 13-8 2088.92 Jan 26th Santa Barbara Invite 2019
30 Victoria Loss 11-13 1537.06 Jan 26th Santa Barbara Invite 2019
42 British Columbia Loss 9-13 1255.04 Jan 27th Santa Barbara Invite 2019
21 California Loss 8-12 1402.31 Jan 27th Santa Barbara Invite 2019
51 Western Washington Win 14-13 1754.76 Jan 27th Santa Barbara Invite 2019
5 Cal Poly-SLO Loss 5-9 1615.4 Feb 16th Presidents Day Invite 2019
76 Utah Loss 6-9 1055.16 Feb 16th Presidents Day Invite 2019
56 California-San Diego Loss 5-8 1139.16 Feb 17th Presidents Day Invite 2019
51 Western Washington Win 10-4 2229.76 Feb 17th Presidents Day Invite 2019
93 California-Davis Win 8-7 1502.55 Feb 18th Presidents Day Invite 2019
37 Illinois Loss 6-12 1141.08 Feb 18th Presidents Day Invite 2019
17 Minnesota Loss 7-13 1393.52 Mar 30th Easterns 2019 Men
9 Massachusetts Loss 11-13 1836.66 Mar 30th Easterns 2019 Men
44 Virginia Win 13-10 1999.56 Mar 30th Easterns 2019 Men
3 Oregon Loss 8-13 1692.83 Mar 30th Easterns 2019 Men
17 Minnesota Loss 7-15 1351.05 Mar 31st Easterns 2019 Men
24 Auburn Win 13-12 1921.78 Mar 31st Easterns 2019 Men
49 Northwestern Win 15-7 2237.69 Mar 31st Easterns 2019 Men
**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)