#7 Western Washington (8-6)

avg: 2199.67  •  sd: 77.05  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
29 Northwestern Win 9-6 2186.18 Feb 16th Presidents Day Invite 2019
2 California-San Diego Loss 5-9 1890.01 Feb 16th Presidents Day Invite 2019
16 Oregon Win 8-7 2142.73 Feb 17th Presidents Day Invite 2019
12 Minnesota Win 8-4 2634.52 Feb 17th Presidents Day Invite 2019
17 Vermont Loss 6-7 1888.2 Feb 17th Presidents Day Invite 2019
14 Colorado Loss 6-7 1921.85 Feb 18th Presidents Day Invite 2019
13 Stanford Win 8-2 2655.63 Feb 18th Presidents Day Invite 2019
32 Brigham Young Win 15-6 2310.48 Mar 29th NW Challenge Tier 1 Womens
5 Carleton College-Syzygy Loss 11-15 1884.33 Mar 29th NW Challenge Tier 1 Womens
1 North Carolina Loss 10-12 2291.95 Mar 30th NW Challenge Tier 1 Womens
50 Whitman Win 15-11 1890.07 Mar 30th NW Challenge Tier 1 Womens
11 Pittsburgh Win 15-11 2464.43 Mar 30th NW Challenge Tier 1 Womens
16 Oregon Win 14-5 2617.73 Mar 31st NW Challenge Tier 1 Womens
2 California-San Diego Loss 10-14 2020.36 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)