#2 Washington (16-0)

avg: 2331.44  •  sd: 66.15  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
41 Santa Clara** Win 13-1 2154.48 Ignored Jan 25th Santa Barbara Invite 2020
7 Colorado State University Win 13-7 2514.9 Jan 25th Santa Barbara Invite 2020
24 California-Santa Barbara** Win 13-5 2266.89 Ignored Jan 25th Santa Barbara Invite 2020
22 Western Washington** Win 13-3 2306.65 Ignored Jan 25th Santa Barbara Invite 2020
4 Cal Poly-SLO Win 13-11 2307.45 Jan 26th Santa Barbara Invite 2020
31 Utah** Win 13-5 2205.06 Ignored Jan 26th Santa Barbara Invite 2020
12 UCLA Win 13-5 2456.88 Jan 26th Santa Barbara Invite 2020
138 San Diego State** Win 15-3 1520.15 Ignored Feb 15th Presidents Day Invite 2020
43 Stanford Win 15-8 2082.11 Feb 15th Presidents Day Invite 2020
31 Utah Win 13-7 2162.59 Feb 15th Presidents Day Invite 2020
35 Oklahoma State Win 15-8 2148.1 Feb 16th Presidents Day Invite 2020
3 Colorado Win 11-9 2341.1 Feb 16th Presidents Day Invite 2020
30 California-Santa Cruz Win 14-8 2142.87 Feb 16th Presidents Day Invite 2020
6 Oregon Win 15-7 2660.76 Feb 17th Presidents Day Invite 2020
4 Cal Poly-SLO Win 11-7 2545.5 Feb 17th Presidents Day Invite 2020
43 Stanford** Win 15-2 2117.3 Ignored 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)