#24 British Columbia (14-8)

avg: 1800.53  •  sd: 41.37  •  top 16/20: 17.8%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
79 Grand Canyon Win 13-10 1668.84 Jan 27th Santa Barbara Invite 2024
67 Chicago Win 12-6 1966.33 Jan 27th Santa Barbara Invite 2024
5 Cal Poly-SLO Loss 7-13 1617.18 Jan 27th Santa Barbara Invite 2024
47 Oklahoma Christian Win 13-12 1645.17 Jan 27th Santa Barbara Invite 2024
53 Colorado State Win 13-6 2070.56 Jan 28th Santa Barbara Invite 2024
6 Oregon Loss 9-15 1593.78 Jan 28th Santa Barbara Invite 2024
22 Washington Win 13-9 2237.56 Jan 28th Santa Barbara Invite 2024
30 Utah Win 15-10 2130.6 Jan 28th Santa Barbara Invite 2024
15 California Loss 12-13 1799.22 Feb 17th Presidents Day Invite 2024
35 California-Santa Cruz Win 10-8 1899.87 Feb 17th Presidents Day Invite 2024
18 Oregon State Loss 11-12 1744.3 Feb 17th Presidents Day Invite 2024
134 California-Irvine Win 15-8 1673.9 Feb 18th Presidents Day Invite 2024
43 California-San Diego Win 10-9 1687.26 Feb 18th Presidents Day Invite 2024
54 California-Santa Barbara Win 9-7 1748.98 Feb 18th Presidents Day Invite 2024
43 California-San Diego Win 9-8 1687.26 Feb 19th Presidents Day Invite 2024
26 Utah State Loss 9-11 1520.2 Feb 19th Presidents Day Invite 2024
5 Cal Poly-SLO Loss 8-12 1733.55 Mar 23rd Northwest Challenge Mens 2024
30 Utah Win 14-11 1990.33 Mar 23rd Northwest Challenge Mens 2024
63 Western Washington Win 15-10 1875.84 Mar 23rd Northwest Challenge Mens 2024
10 Carleton College Loss 13-15 1796.16 Mar 24th Northwest Challenge Mens 2024
23 UCLA Win 14-12 2029.4 Mar 24th Northwest Challenge Mens 2024
15 California Loss 10-14 1525.52 Mar 24th Northwest Challenge Mens 2024
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)