#42 British Columbia (9-11)

avg: 1673.61  •  sd: 83.08  •  top 16/20: 0.5%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
90 Santa Clara Win 13-6 1986.86 Jan 26th Santa Barbara Invite 2019
6 Brigham Young Loss 10-13 1806.59 Jan 26th Santa Barbara Invite 2019
19 Colorado State Win 13-10 2227.69 Jan 26th Santa Barbara Invite 2019
34 UCLA Win 13-12 1853.73 Jan 26th Santa Barbara Invite 2019
16 Southern California Win 13-7 2533.68 Jan 27th Santa Barbara Invite 2019
45 California-Santa Barbara Win 13-9 2081.82 Jan 27th Santa Barbara Invite 2019
5 Cal Poly-SLO Loss 9-13 1725.89 Jan 27th Santa Barbara Invite 2019
90 Santa Clara Win 12-5 1986.86 Feb 16th Presidents Day Invite 2019
37 Illinois Win 8-7 1845.39 Feb 16th Presidents Day Invite 2019
16 Southern California Loss 3-13 1376.15 Feb 17th Presidents Day Invite 2019
21 California Loss 4-7 1347.3 Feb 17th Presidents Day Invite 2019
34 UCLA Loss 5-12 1128.73 Feb 18th Presidents Day Invite 2019
56 California-San Diego Loss 10-11 1467.76 Feb 18th Presidents Day Invite 2019
50 Stanford Loss 7-13 1075.21 Mar 23rd 2019 NW Challenge Mens Tier 1
6 Brigham Young Loss 7-13 1577.2 Mar 23rd 2019 NW Challenge Mens Tier 1
5 Cal Poly-SLO Loss 6-13 1544.46 Mar 23rd 2019 NW Challenge Mens Tier 1
51 Western Washington Loss 9-13 1211.2 Mar 23rd 2019 NW Challenge Mens Tier 1
59 Oregon State Win 12-10 1800.31 Mar 24th 2019 NW Challenge Mens Tier 1
10 Washington Loss 8-13 1548.35 Mar 24th 2019 NW Challenge Mens Tier 1
58 Whitman Win 13-9 1998.22 Mar 24th 2019 NW Challenge Mens Tier 1
**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)