#6 British Columbia (9-4)

avg: 2560.13  •  sd: 78.27  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
33 UCLA Win 13-3 2662.69 Mar 3rd Stanford Invite 2018
11 Texas Win 12-10 2709.87 Mar 3rd Stanford Invite 2018
26 California Win 12-9 2478.6 Mar 3rd Stanford Invite 2018
2 California-San Diego Loss 7-13 2175.29 Mar 4th Stanford Invite 2018
12 Carleton College Win 13-8 2918.13 Mar 4th Stanford Invite 2018
5 Oregon Win 15-14 2735.52 Mar 4th Stanford Invite 2018
12 Carleton College Win 13-10 2750.11 Mar 23rd NW Challenge 2018
18 Brigham Young Win 10-8 2549.17 Mar 23rd NW Challenge 2018
16 Western Washington Loss 10-12 2106.26 Mar 24th NW Challenge 2018
19 Vermont Win 14-5 2863.48 Mar 24th NW Challenge 2018
1 Dartmouth Loss 7-15 2297.25 Mar 24th NW Challenge 2018
17 California-Santa Barbara Win 15-7 2920.26 Mar 25th NW Challenge 2018
3 North Carolina Loss 8-15 2165.69 Mar 25th NW Challenge 2018
**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)