#5 Oregon (14-1)

avg: 1860.83  •  sd: 66.82  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
68 Humboldt State Win 15-11 1480.35 Jan 25th Pacific Confrontational Invite 2020
125 Montana** Win 15-3 1334.59 Ignored Jan 25th Pacific Confrontational Invite 2020
105 Washington-B** Win 15-5 1476.53 Ignored Jan 25th Pacific Confrontational Invite 2020
36 Whitman Win 15-5 1997.24 Jan 25th Pacific Confrontational Invite 2020
68 Humboldt State** Win 15-3 1699.19 Ignored Jan 26th Pacific Confrontational Invite 2020
18 Oregon State Win 15-10 2050.46 Jan 26th Pacific Confrontational Invite 2020
40 California-San Diego Win 13-9 1791.3 Feb 15th Presidents Day Invite 2020
35 California-Santa Cruz Win 15-9 1921.54 Feb 15th Presidents Day Invite 2020
92 Southern California** Win 15-5 1577.01 Ignored Feb 15th Presidents Day Invite 2020
4 Cal Poly-SLO Win 11-9 2134.61 Feb 16th Presidents Day Invite 2020
12 UCLA Win 15-11 2037.52 Feb 16th Presidents Day Invite 2020
48 Stanford Win 12-8 1755.66 Feb 16th Presidents Day Invite 2020
3 Colorado Win 12-11 2027.81 Feb 17th Presidents Day Invite 2020
18 Oregon State Win 12-11 1721.85 Feb 17th Presidents Day Invite 2020
1 Washington Loss 7-15 1542.78 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)