#9 Stanford (15-6)

avg: 2105.14  •  sd: 67.84  •  top 16/20: 99.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
14 UCLA Loss 11-13 1759.44 Jan 25th Santa Barbara Invite 2020
8 Northeastern Loss 11-12 1981.64 Jan 25th Santa Barbara Invite 2020
59 Washington University Win 10-6 1857.85 Jan 25th Santa Barbara Invite 2020
5 Washington Loss 9-13 1726.95 Jan 25th Santa Barbara Invite 2020
35 Utah Win 11-10 1699.56 Jan 26th Santa Barbara Invite 2020
27 California-Davis Win 8-7 1870.47 Jan 26th Santa Barbara Invite 2020
35 Utah Win 10-6 2070.72 Feb 15th Presidents Day Invite 2020
90 Southern California** Win 13-4 1724.67 Ignored Feb 15th Presidents Day Invite 2020
26 Texas Loss 7-9 1484.08 Feb 15th Presidents Day Invite 2020
10 California-Santa Barbara Loss 8-9 1916.89 Feb 15th Presidents Day Invite 2020
14 UCLA Win 12-7 2508.8 Feb 16th Presidents Day Invite 2020
29 California Win 12-7 2201.04 Feb 16th Presidents Day Invite 2020
45 Chicago Win 12-8 1958.68 Feb 17th Presidents Day Invite 2020
16 Western Washington Win 11-6 2508.86 Feb 17th Presidents Day Invite 2020
10 California-Santa Barbara Win 11-10 2166.89 Feb 17th Presidents Day Invite 2020
23 Minnesota Win 11-6 2330.35 Mar 7th Stanford Invite 2020
26 Texas Win 9-6 2181.98 Mar 7th Stanford Invite 2020
4 California-San Diego Win 8-7 2362.65 Mar 7th Stanford Invite 2020
10 California-Santa Barbara Win 13-9 2460.45 Mar 8th Stanford Invite 2020
1 Carleton College Loss 7-13 1957.37 Mar 8th Stanford Invite 2020
4 California-San Diego Win 10-7 2627.32 Mar 8th Stanford Invite 2020
**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)