#16 British Columbia (14-6)

avg: 1992.55  •  sd: 58.78  •  top 16/20: 81.7%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
42 Grand Canyon Win 15-4 2305.28 Jan 28th Santa Barbara Invitational 2023
53 Utah Win 11-6 2166.68 Jan 28th Santa Barbara Invitational 2023
73 California-Santa Barbara Win 14-2 2091.64 Jan 28th Santa Barbara Invitational 2023
7 Cal Poly-SLO Loss 10-11 2050.35 Jan 28th Santa Barbara Invitational 2023
54 Northwestern Win 11-5 2216.19 Jan 29th Santa Barbara Invitational 2023
15 UCLA Loss 7-8 1903.29 Jan 29th Santa Barbara Invitational 2023
44 Victoria Loss 7-10 1307.06 Jan 29th Santa Barbara Invitational 2023
17 Washington Win 11-9 2239.35 Jan 29th Santa Barbara Invitational 2023
57 Stanford Win 13-6 2182.25 Mar 4th Stanford Invite Mens
47 Colorado State Win 13-9 2065.79 Mar 4th Stanford Invite Mens
17 Washington Loss 10-13 1662 Mar 4th Stanford Invite Mens
32 Oregon State Loss 10-11 1680.73 Mar 5th Stanford Invite Mens
29 Utah State Win 12-11 1963.27 Mar 5th Stanford Invite Mens
23 Wisconsin Win 11-10 2019.52 Mar 5th Stanford Invite Mens
86 Dartmouth Win 15-5 2036.97 Apr 1st Northwest Challenge Mens
81 Whitman Win 15-7 2064.12 Apr 1st Northwest Challenge Mens
29 Utah State Loss 8-11 1472.66 Apr 1st Northwest Challenge Mens
32 Oregon State Win 15-9 2321.21 Apr 2nd Northwest Challenge Mens
46 Western Washington Win 12-9 2033.9 Apr 2nd Northwest Challenge Mens
53 Utah Win 13-8 2116.15 Apr 2nd Northwest Challenge Mens
**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)