#59 California-Santa Cruz (15-7)

avg: 1273.6  •  sd: 78.96  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
30 California Loss 8-13 1001.66 Jan 28th Santa Barbara Invitational 2023
4 Carleton College** Loss 2-14 1638.71 Ignored Jan 28th Santa Barbara Invitational 2023
71 Northwestern Win 11-6 1663.75 Jan 28th Santa Barbara Invitational 2023
23 California-Davis Loss 6-11 1014.02 Jan 28th Santa Barbara Invitational 2023
71 Northwestern Win 11-10 1242.05 Jan 29th Santa Barbara Invitational 2023
35 UCLA Win 9-5 1985.55 Jan 29th Santa Barbara Invitational 2023
47 Santa Clara Loss 5-11 768.16 Feb 4th Stanford Open
155 Stanford-B** Win 9-2 957.72 Ignored Feb 4th Stanford Open
34 Portland Loss 5-10 889.16 Feb 4th Stanford Open
102 Claremont Win 9-3 1491.16 Feb 4th Stanford Open
125 San Diego State Win 12-1 1279.71 Feb 5th Stanford Open
95 Southern California Win 9-3 1529.15 Feb 5th Stanford Open
95 Southern California Win 7-2 1529.15 Feb 5th Stanford Open
118 California-San Diego-B Win 8-5 1229.17 Feb 5th Stanford Open
83 Nevada-Reno Win 9-6 1421.91 Feb 18th Santa Clara Rage Tournament
149 Arizona State** Win 13-0 1020.66 Ignored Feb 18th Santa Clara Rage Tournament
118 California-San Diego-B Win 10-4 1375.56 Feb 18th Santa Clara Rage Tournament
172 Chico State** Win 13-1 790.14 Ignored Feb 18th Santa Clara Rage Tournament
179 California-Davis-B** Win 13-0 644.21 Ignored Feb 18th Santa Clara Rage Tournament
47 Santa Clara Loss 6-7 1243.16 Feb 19th Santa Clara Rage Tournament
83 Nevada-Reno Loss 6-7 878.35 Feb 19th Santa Clara Rage Tournament
184 California-B** Win 13-2 554.97 Ignored Feb 19th Santa Clara Rage Tournament
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)