#6 British Columbia (8-5)

avg: 2231.77  •  sd: 92.96  •  top 16/20: 99.9%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
38 Florida** Win 13-0 2211.11 Ignored Mar 2nd Stanford Invite 2019
23 California Loss 9-10 1792.92 Mar 2nd Stanford Invite 2019
21 Cal Poly-SLO Win 10-8 2206.26 Mar 2nd Stanford Invite 2019
13 Stanford Loss 9-10 1930.63 Mar 2nd Stanford Invite 2019
16 Oregon Win 11-9 2266.94 Mar 3rd Stanford Invite 2019
24 Washington Loss 9-11 1623.38 Mar 3rd Stanford Invite 2019
32 Brigham Young Win 15-8 2275.29 Mar 29th NW Challenge Tier 1 Womens
2 California-San Diego Win 15-11 2800.23 Mar 29th NW Challenge Tier 1 Womens
3 Ohio State Loss 11-15 1991.83 Mar 30th NW Challenge Tier 1 Womens
8 Dartmouth Win 14-12 2379.81 Mar 30th NW Challenge Tier 1 Womens
5 Carleton College-Syzygy Win 14-12 2486.46 Mar 30th NW Challenge Tier 1 Womens
3 Ohio State Loss 11-15 1991.83 Mar 31st NW Challenge Tier 1 Womens
1 North Carolina Win 13-11 2758.91 Mar 31st NW Challenge Tier 1 Womens
**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)