#11 Oregon (13-8)

avg: 2096.82  •  sd: 37.32  •  top 16/20: 100%

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# Opponent Result Game Rating Status Date Event
8 Stanford Loss 8-9 2108.33 Feb 18th President’s Day Invite
24 Carleton College-Eclipse Win 12-7 2253.34 Feb 18th President’s Day Invite
18 Colorado State Win 10-8 2074.17 Feb 18th President’s Day Invite
29 UCLA Win 14-7 2247.51 Feb 18th President’s Day Invite
24 Carleton College-Eclipse Win 10-5 2306.73 Feb 19th President’s Day Invite
31 California Win 14-5 2257.96 Feb 19th President’s Day Invite
17 California-San Diego Win 13-9 2243.14 Feb 19th President’s Day Invite
29 UCLA Win 10-7 2054.29 Feb 19th President’s Day Invite
3 Colorado Loss 10-12 2229.74 Feb 20th President’s Day Invite
12 California-Santa Barbara Win 12-11 2183.42 Feb 20th President’s Day Invite
39 Santa Clara Win 12-2 2139.36 Mar 11th Stanford Invite Womens
3 Colorado Loss 7-10 2078.2 Mar 11th Stanford Invite Womens
17 California-San Diego Win 13-8 2320.74 Mar 11th Stanford Invite Womens
29 UCLA Win 12-8 2105.78 Mar 11th Stanford Invite Womens
2 British Columbia Loss 3-13 1948.3 Mar 12th Stanford Invite Womens
31 California Win 11-8 2023.57 Mar 12th Stanford Invite Womens
1 North Carolina** Loss 3-13 2337.91 Ignored Mar 12th Stanford Invite Womens
20 Western Washington Win 12-8 2221.59 Mar 25th Northwest Challenge1
5 Vermont Loss 5-13 1773.3 Mar 25th Northwest Challenge1
2 British Columbia Loss 4-13 1948.3 Mar 26th Northwest Challenge1
9 Washington Loss 8-13 1687.36 Mar 26th Northwest Challenge1
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)