#72 Santa Clara (10-9)

avg: 1396.79  •  sd: 69.75  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
135 Claremont Loss 5-6 853.82 Feb 3rd Stanford Open 2024
186 UCLA-B** Win 13-2 1185.49 Ignored Feb 3rd Stanford Open 2024
148 Cal Poly-SLO-B Win 8-5 1334.66 Feb 3rd Stanford Open 2024
79 San Diego State Win 6-5 1485.25 Feb 17th Santa Clara University Presidents Day
184 California-Davis-B** Win 13-2 1196.28 Ignored Feb 17th Santa Clara University Presidents Day
137 California-B Win 7-3 1547.01 Feb 17th Santa Clara University Presidents Day
79 San Diego State Loss 8-9 1235.25 Feb 18th Santa Clara University Presidents Day
131 Occidental Win 8-6 1315.35 Feb 18th Santa Clara University Presidents Day
88 California-Irvine Win 7-6 1434.28 Feb 18th Santa Clara University Presidents Day
20 California-Davis Loss 2-15 1381.44 Apr 13th NorCal D I Womens Conferences 2024
248 Cal Poly-Humboldt** Win 15-2 600 Ignored Apr 13th NorCal D I Womens Conferences 2024
17 California-Santa Cruz** Loss 3-14 1478.47 Ignored Apr 13th NorCal D I Womens Conferences 2024
28 California Loss 1-15 1256.51 Apr 14th NorCal D I Womens Conferences 2024
67 Nevada-Reno Loss 10-11 1314.92 Apr 14th NorCal D I Womens Conferences 2024
17 California-Santa Cruz** Loss 1-15 1478.47 Ignored Apr 27th Southwest D I College Womens Regionals 2024
88 California-Irvine Win 8-5 1762.89 Apr 27th Southwest D I College Womens Regionals 2024
47 Southern California Loss 3-11 1036.66 Apr 27th Southwest D I College Womens Regionals 2024
79 San Diego State Loss 6-7 1235.25 Apr 28th Southwest D I College Womens Regionals 2024
69 Grand Canyon Win 12-5 2036.37 Apr 28th Southwest D I College Womens Regionals 2024
**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)