#18 Oregon State (10-5)

avg: 1596.85  •  sd: 74.99  •  top 16/20: 72.6%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
125 Montana** Win 15-1 1334.59 Ignored Jan 25th Pacific Confrontational Invite 2020
67 Nevada-Reno Win 15-11 1482.02 Jan 25th Pacific Confrontational Invite 2020
120 Lewis & Clark** Win 15-4 1350.19 Ignored Jan 25th Pacific Confrontational Invite 2020
105 Washington-B Win 15-8 1441.34 Jan 25th Pacific Confrontational Invite 2020
5 Oregon Loss 10-15 1407.23 Jan 26th Pacific Confrontational Invite 2020
36 Whitman Win 15-6 1997.24 Jan 26th Pacific Confrontational Invite 2020
27 California-Santa Barbara Loss 6-13 870.18 Feb 15th Presidents Day Invite 2020
3 Colorado Loss 10-12 1664.69 Feb 15th Presidents Day Invite 2020
12 UCLA Loss 10-12 1418.23 Feb 15th Presidents Day Invite 2020
60 Illinois Win 11-8 1532.75 Feb 16th Presidents Day Invite 2020
40 California-San Diego Win 13-9 1791.3 Feb 16th Presidents Day Invite 2020
35 California-Santa Cruz Win 13-8 1902.22 Feb 16th Presidents Day Invite 2020
127 San Diego State** Win 12-5 1315.59 Ignored Feb 16th Presidents Day Invite 2020
5 Oregon Loss 11-12 1735.83 Feb 17th Presidents Day Invite 2020
12 UCLA Win 10-8 1919.02 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)