#104 British Columbia -B (11-11)

avg: 1451.5  •  sd: 41.68  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
340 Stanford-B** Win 13-2 1132.96 Ignored Feb 8th Stanford Open Mens
255 Santa Clara-B Win 12-4 1461.57 Feb 8th Stanford Open Mens
250 Portland Win 12-4 1483.73 Feb 8th Stanford Open Mens
261 Nevada-Reno** Win 13-3 1431.64 Ignored Feb 9th Stanford Open Mens
124 San Jose State Win 7-5 1687.08 Feb 9th Stanford Open Mens
119 Cal Poly-SLO-B Win 11-9 1616.41 Feb 9th Stanford Open Mens
6 Cal Poly-SLO** Loss 5-13 1667.97 Ignored Mar 8th Stanford Invite 2025 Mens
41 California-San Diego Loss 9-13 1373.74 Mar 8th Stanford Invite 2025 Mens
43 Whitman Loss 10-13 1455.46 Mar 8th Stanford Invite 2025 Mens
54 UCLA Loss 7-13 1146.55 Mar 9th Stanford Invite 2025 Mens
143 Wisconsin-Milwaukee Loss 10-13 960.44 Mar 9th Stanford Invite 2025 Mens
250 Portland Win 15-3 1483.73 Mar 29th Northwest Challenge D3
83 Simon Fraser Loss 12-14 1321.38 Mar 29th Northwest Challenge D3
157 Washington-B Win 15-7 1840.2 Mar 30th Northwest Challenge D3
83 Simon Fraser Loss 10-12 1304.21 Mar 30th Northwest Challenge D3
10 British Columbia** Loss 5-13 1551.93 Ignored Apr 19th Cascadia D I Mens Conferences 2025
23 Western Washington Loss 8-13 1492.12 Apr 19th Cascadia D I Mens Conferences 2025
8 Washington** Loss 4-13 1616.59 Ignored Apr 19th Cascadia D I Mens Conferences 2025
246 Oregon State-B Win 13-3 1497.74 Apr 19th Cascadia D I Mens Conferences 2025
224 Oregon-B Win 15-10 1430 Apr 20th Cascadia D I Mens Conferences 2025
157 Washington-B Win 14-10 1638.9 Apr 20th Cascadia D I Mens Conferences 2025
23 Western Washington Loss 9-15 1472.8 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)