#43 Stanford (6-11)

avg: 1517.3  •  sd: 72.7  •  top 16/20: 0.1%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
42 Northwestern Win 13-10 1857.52 Jan 25th Santa Barbara Invite 2020
108 Connecticut Win 13-9 1487.33 Jan 25th Santa Barbara Invite 2020
14 California Loss 8-13 1343.12 Jan 25th Santa Barbara Invite 2020
69 Victoria Win 13-10 1646.95 Jan 25th Santa Barbara Invite 2020
42 Northwestern Loss 8-12 1088.23 Jan 26th Santa Barbara Invite 2020
37 California-San Diego Loss 8-9 1445.96 Jan 26th Santa Barbara Invite 2020
31 Utah Loss 9-12 1259.69 Jan 26th Santa Barbara Invite 2020
138 San Diego State Win 10-9 1045.15 Feb 15th Presidents Day Invite 2020
2 Washington Loss 8-15 1766.63 Feb 15th Presidents Day Invite 2020
31 Utah Win 11-8 1970.66 Feb 15th Presidents Day Invite 2020
6 Oregon Loss 8-12 1619.6 Feb 16th Presidents Day Invite 2020
4 Cal Poly-SLO Loss 6-12 1499.3 Feb 16th Presidents Day Invite 2020
24 California-Santa Barbara Win 11-7 2133.78 Feb 16th Presidents Day Invite 2020
12 UCLA Loss 7-9 1577.54 Feb 16th Presidents Day Invite 2020
2 Washington** Loss 2-15 1731.44 Ignored Feb 17th Presidents Day Invite 2020
31 Utah Loss 6-9 1186.49 Feb 17th Presidents Day Invite 2020
12 UCLA Loss 6-14 1256.88 Feb 17th Presidents Day 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)