#21 California (18-10)

avg: 1843.46  •  sd: 63.07  •  top 16/20: 32.2%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
16 Southern California Loss 11-13 1747.31 Jan 26th Santa Barbara Invite 2019
219 Michigan State** Win 13-4 1522.83 Ignored Jan 26th Santa Barbara Invite 2019
74 Arizona Win 13-6 2079.09 Jan 26th Santa Barbara Invite 2019
10 Washington Loss 6-13 1444.51 Jan 26th Santa Barbara Invite 2019
19 Colorado State Win 14-13 2024.55 Jan 27th Santa Barbara Invite 2019
30 Victoria Loss 9-11 1516.69 Jan 27th Santa Barbara Invite 2019
45 California-Santa Barbara Win 12-8 2104.41 Jan 27th Santa Barbara Invite 2019
10 Washington Win 12-10 2282.63 Jan 27th Santa Barbara Invite 2019
104 Portland Loss 9-10 1214.16 Feb 9th Stanford Open 2019
261 Cal Poly-SLO-B** Win 13-2 1421.14 Ignored Feb 9th Stanford Open 2019
62 Duke Win 13-6 2151 Feb 9th Stanford Open 2019
90 Santa Clara Win 10-2 1986.86 Feb 10th Stanford Open 2019
75 Air Force Win 9-8 1602.54 Feb 10th Stanford Open 2019
111 Washington University Win 7-3 1913.46 Feb 10th Stanford Open 2019
58 Whitman Win 6-5 1704.65 Feb 10th Stanford Open 2019
271 San Diego State** Win 13-4 1380.82 Ignored Feb 16th Presidents Day Invite 2019
93 California-Davis Win 11-6 1924.24 Feb 16th Presidents Day Invite 2019
42 British Columbia Win 7-4 2169.77 Feb 17th Presidents Day Invite 2019
8 Colorado Win 7-6 2220.44 Feb 17th Presidents Day Invite 2019
16 Southern California Loss 4-10 1376.15 Feb 18th Presidents Day Invite 2019
5 Cal Poly-SLO Loss 4-10 1544.46 Feb 18th Presidents Day Invite 2019
6 Brigham Young Win 11-10 2259.73 Mar 2nd Stanford Invite 2019
14 Ohio State Loss 7-8 1867.06 Mar 2nd Stanford Invite 2019
3 Oregon Loss 7-13 1631.45 Mar 2nd Stanford Invite 2019
49 Northwestern Win 13-6 2237.69 Mar 2nd Stanford Invite 2019
7 Carleton College-CUT Loss 6-12 1539.33 Mar 3rd Stanford Invite 2019
13 Wisconsin Win 11-10 2125.97 Mar 3rd Stanford Invite 2019
19 Colorado State Loss 3-7 1299.55 Mar 3rd Stanford Invite 2019
**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)