#53 Venus (3-8)

avg: 925.73  •  sd: 105.65  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
2 Brute Squad** Loss 2-15 1830.69 Ignored Jun 1st New York Warmup Womens Sanctioned Games 2019
37 Stella Loss 6-8 903.87 Jun 1st New York Warmup Womens Sanctioned Games 2019
38 Rebel Rebel Win 11-7 1664.36 Jun 2nd New York Warmup Womens Sanctioned Games 2019
43 Tempest Loss 9-13 665.61 Jun 22nd Boston Invite 2019
63 Hot Metal Win 12-8 1154.34 Jun 22nd Boston Invite 2019
21 BENT** Loss 4-15 985.97 Ignored Jun 22nd Boston Invite 2019
45 Vice Loss 5-7 744.96 Jun 22nd Boston Invite 2019
51 TOX6ix Loss 10-11 847.13 Jun 23rd Boston Invite 2019
18 Iris** Loss 4-15 1076.17 Ignored Jun 23rd Boston Invite 2019
79 Versa Win 15-7 939.97 Jun 23rd Boston Invite 2019
48 Brooklyn Book Club Loss 7-11 566.95 Jun 23rd Boston Invite 2019
**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)