#33 UCLA (9-16)

avg: 2062.69  •  sd: 56.01  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
79 Chicago Win 13-8 2149.54 Jan 27th Santa Barbara Invitational 2018
19 Vermont Loss 11-12 2138.48 Jan 27th Santa Barbara Invitational 2018
63 Arizona Loss 9-12 1440.86 Jan 27th Santa Barbara Invitational 2018
38 Victoria Loss 9-10 1895.2 Jan 27th Santa Barbara Invitational 2018
63 Arizona Loss 9-12 1440.86 Jan 28th Santa Barbara Invitational 2018
79 Chicago Win 13-8 2149.54 Jan 28th Santa Barbara Invitational 2018
37 Northwestern Win 11-8 2393.78 Feb 17th Presidents Day Invitational Tournament 2018
2 California-San Diego** Loss 5-12 2132.82 Ignored Feb 17th Presidents Day Invitational Tournament 2018
5 Oregon Loss 7-13 2052.99 Feb 17th Presidents Day Invitational Tournament 2018
36 Colorado College Win 13-11 2262.02 Feb 18th Presidents Day Invitational Tournament 2018
9 Colorado Loss 4-11 1899.69 Feb 18th Presidents Day Invitational Tournament 2018
2 California-San Diego Loss 6-11 2186.12 Feb 18th Presidents Day Invitational Tournament 2018
17 California-Santa Barbara Loss 4-11 1720.26 Feb 19th Presidents Day Invitational Tournament 2018
26 California Loss 9-10 2008.23 Feb 19th Presidents Day Invitational Tournament 2018
6 British Columbia Loss 3-13 1960.13 Mar 3rd Stanford Invite 2018
11 Texas Loss 7-11 2004.85 Mar 3rd Stanford Invite 2018
26 California Win 8-5 2586.84 Mar 3rd Stanford Invite 2018
13 Ohio State Loss 8-9 2279.92 Mar 4th Stanford Invite 2018
20 Washington Loss 10-13 1912.09 Mar 4th Stanford Invite 2018
37 Northwestern Loss 10-11 1903.18 Mar 24th Womens Centex 2018
123 MIT Win 13-8 1855.39 Mar 24th Womens Centex 2018
41 Georgia Tech Win 13-7 2566.95 Mar 24th Womens Centex 2018
9 Colorado Loss 5-12 1899.69 Mar 24th Womens Centex 2018
42 Wisconsin Win 10-7 2393.36 Mar 25th Womens Centex 2018
41 Georgia Tech Win 13-12 2134.42 Mar 25th Womens Centex 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)