#10 British Columbia (16-7)

avg: 2151.93  •  sd: 56.41  •  top 16/20: 99.4%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
119 Cal Poly-SLO-B** Win 13-2 1967.2 Ignored Jan 25th Santa Barbara Invite 2025
25 Victoria Win 11-7 2425.74 Jan 25th Santa Barbara Invite 2025
9 California-Santa Cruz Win 11-10 2310.14 Jan 26th Santa Barbara Invite 2025
13 California Loss 4-12 1503.63 Jan 26th Santa Barbara Invite 2025
8 Washington Loss 5-8 1762.98 Jan 26th Santa Barbara Invite 2025
121 Northwestern** Win 13-3 1963.35 Ignored Mar 8th Stanford Invite 2025 Mens
127 Santa Clara** Win 13-2 1941.61 Ignored Mar 8th Stanford Invite 2025 Mens
46 Stanford Win 12-9 2110.73 Mar 8th Stanford Invite 2025 Mens
45 Virginia Tech Win 13-8 2271.76 Mar 9th Stanford Invite 2025 Mens
23 Western Washington Loss 10-11 1863.28 Mar 9th Stanford Invite 2025 Mens
6 Cal Poly-SLO Loss 11-12 2142.97 Mar 9th Stanford Invite 2025 Mens
32 Utah State Win 16-9 2398.85 Mar 22nd Northwest Challenge 2025 mens
9 California-Santa Cruz Loss 14-17 1905.8 Mar 22nd Northwest Challenge 2025 mens
138 Gonzaga** Win 15-5 1913.2 Ignored Mar 22nd Northwest Challenge 2025 mens
8 Washington Loss 14-15 2091.59 Mar 23rd Northwest Challenge 2025 mens
32 Utah State Win 15-10 2305.76 Mar 23rd Northwest Challenge 2025 mens
9 California-Santa Cruz Win 13-12 2310.14 Mar 23rd Northwest Challenge 2025 mens
8 Washington Win 12-11 2341.59 Apr 19th Cascadia D I Mens Conferences 2025
246 Oregon State-B** Win 13-1 1497.74 Ignored Apr 19th Cascadia D I Mens Conferences 2025
104 British Columbia -B** Win 13-5 2051.5 Ignored Apr 19th Cascadia D I Mens Conferences 2025
23 Western Washington Win 13-11 2217.12 Apr 19th Cascadia D I Mens Conferences 2025
11 Oregon State Loss 13-15 1921.55 Apr 20th Cascadia D I Mens Conferences 2025
8 Washington Win 14-13 2341.59 Apr 20th Cascadia D I Mens Conferences 2025
**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)