#39 Santa Clara (12-9)

avg: 1539.36  •  sd: 77.54  •  top 16/20: 0.3%

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# Opponent Result Game Rating Status Date Event
119 San Diego State** Win 11-1 1444.36 Ignored Feb 4th Stanford Open
50 California-Santa Cruz Win 11-5 2037.65 Feb 4th Stanford Open
90 Claremont Win 6-4 1429.67 Feb 4th Stanford Open
87 Southern California Loss 6-7 961.09 Feb 4th Stanford Open
34 Portland Loss 7-8 1507.44 Feb 5th Stanford Open
158 Stanford-B** Win 12-1 1121.55 Ignored Feb 5th Stanford Open
24 Carleton College-Eclipse Loss 6-8 1432.34 Feb 5th Stanford Open
34 Portland Loss 5-9 1103.38 Feb 5th Stanford Open
90 Claremont Win 9-4 1664.06 Feb 18th Santa Clara Rage Tournament
101 Occidental Win 11-5 1575.82 Feb 18th Santa Clara Rage Tournament
192 Pacific Lutheran** Win 13-3 824.56 Ignored Feb 18th Santa Clara Rage Tournament
84 California-Irvine Win 10-7 1503.54 Feb 18th Santa Clara Rage Tournament
158 Stanford-B** Win 11-2 1121.55 Ignored Feb 18th Santa Clara Rage Tournament
162 UCLA-B** Win 12-0 1100.64 Ignored Feb 19th Santa Clara Rage Tournament
50 California-Santa Cruz Win 7-6 1562.65 Feb 19th Santa Clara Rage Tournament
11 Oregon Loss 2-12 1496.82 Mar 11th Stanford Invite Womens
3 Colorado** Loss 3-13 1867.86 Ignored Mar 11th Stanford Invite Womens
17 California-San Diego Loss 5-9 1295.52 Mar 11th Stanford Invite Womens
25 California-Davis Win 8-4 2284.75 Mar 12th Stanford Invite Womens
17 California-San Diego Loss 7-8 1699.58 Mar 12th Stanford Invite Womens
29 UCLA Loss 7-9 1385.28 Mar 12th Stanford Invite Womens
**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)