#6 Stanford (11-7)

avg: 2373.21  •  sd: 62.09  •  top 16/20: 100%

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# Opponent Result Game Rating Status Date Event
8 California-San Diego Loss 10-11 2231.58 Jan 25th Santa Barbara Invite 2025
12 Utah Loss 5-13 1730.78 Jan 25th Santa Barbara Invite 2025
19 Victoria Win 11-9 2327.62 Jan 25th Santa Barbara Invite 2025
9 California-Santa Cruz Win 8-7 2470.27 Jan 26th Santa Barbara Invite 2025
14 Brigham Young Win 12-10 2448.76 Feb 15th Presidents Day Invite 2025
15 California-Santa Barbara Loss 6-7 2036.32 Feb 15th Presidents Day Invite 2025
5 Colorado Loss 7-8 2357.06 Feb 15th Presidents Day Invite 2025
8 California-San Diego Loss 11-12 2231.58 Feb 16th Presidents Day Invite 2025
5 Colorado Loss 6-9 2063.5 Feb 16th Presidents Day Invite 2025
35 Colorado State** Win 13-2 2359.29 Ignored Feb 16th Presidents Day Invite 2025
23 UCLA Win 13-4 2559.3 Feb 17th Presidents Day Invite 2025
20 Western Washington Win 12-8 2476.32 Feb 17th Presidents Day Invite 2025
74 Brown** Win 13-4 1938.48 Ignored Mar 1st Stanford Invite 2025 Womens
13 Cal Poly-SLO Win 12-7 2791.31 Mar 1st Stanford Invite 2025 Womens
34 Wisconsin** Win 13-5 2366.57 Ignored Mar 1st Stanford Invite 2025 Womens
15 California-Santa Barbara Win 11-5 2761.32 Mar 2nd Stanford Invite 2025 Womens
2 Tufts Loss 8-11 2372.23 Mar 2nd Stanford Invite 2025 Womens
10 Washington Win 9-8 2469.56 Mar 2nd Stanford Invite 2025 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)