#16 Oregon (9-11)

avg: 2017.73  •  sd: 74.74  •  top 16/20: 85.1%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
23 California Win 10-9 2042.92 Feb 16th Presidents Day Invite 2019
17 Vermont Win 10-8 2275.86 Feb 16th Presidents Day Invite 2019
12 Minnesota Win 8-7 2194.71 Feb 17th Presidents Day Invite 2019
7 Western Washington Loss 7-8 2074.67 Feb 17th Presidents Day Invite 2019
2 California-San Diego Loss 7-12 1898.55 Feb 17th Presidents Day Invite 2019
13 Stanford Win 9-5 2584.69 Feb 18th Presidents Day Invite 2019
14 Colorado Win 11-6 2593.55 Feb 18th Presidents Day Invite 2019
19 UCLA Win 8-7 2090.86 Mar 2nd Stanford Invite 2019
50 Whitman Loss 6-7 1383.9 Mar 2nd Stanford Invite 2019
5 Carleton College-Syzygy Loss 5-12 1665.5 Mar 2nd Stanford Invite 2019
14 Colorado Loss 8-12 1605.7 Mar 2nd Stanford Invite 2019
9 Texas Loss 8-9 2019 Mar 3rd Stanford Invite 2019
6 British Columbia Loss 9-11 1982.56 Mar 3rd Stanford Invite 2019
32 Brigham Young Win 15-11 2091.65 Mar 29th NW Challenge Tier 1 Womens
24 Washington Win 15-10 2326.19 Mar 29th NW Challenge Tier 1 Womens
11 Pittsburgh Win 15-12 2383.76 Mar 30th NW Challenge Tier 1 Womens
1 North Carolina Loss 7-15 1930.07 Mar 30th NW Challenge Tier 1 Womens
2 California-San Diego Loss 10-11 2294.06 Mar 30th NW Challenge Tier 1 Womens
7 Western Washington Loss 5-14 1599.67 Mar 31st NW Challenge Tier 1 Womens
13 Stanford Loss 7-13 1498.1 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)