#69 Viva (2-16)

avg: 385.9  •  sd: 102.29  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
56 Brooklyn Book Club Loss 7-13 136.07 Jul 28th TCT Select Flight Invite 2018
57 Helix Loss 4-13 72.41 Jul 28th TCT Select Flight Invite 2018
24 Wicked** Loss 1-13 892.17 Ignored Jul 28th TCT Select Flight Invite 2018
43 Green Means Go Loss 8-11 630.99 Jul 29th TCT Select Flight Invite 2018
54 Maeve Loss 7-11 250.29 Jul 29th TCT Select Flight Invite 2018
47 Trainwreck Loss 3-13 238.18 Aug 18th Ski Town Classic 2018
26 Elevate Loss 8-13 904.95 Aug 18th Ski Town Classic 2018
68 Seven Devils Win 11-10 541.4 Aug 18th Ski Town Classic 2018
38 Jackwagon Loss 7-13 524 Aug 19th Ski Town Classic 2018
72 Seattle END Win 11-9 557.8 Aug 19th Ski Town Classic 2018
52 Deadly Viper Assassination Squad Loss 6-13 193.57 Aug 19th Ski Town Classic 2018
46 Venom Loss 5-12 273.67 Sep 8th So Cal Womens Sectional Championship 2018
33 Rampage** Loss 1-15 563.46 Ignored Sep 8th So Cal Womens Sectional Championship 2018
15 Wildfire** Loss 1-15 1123.35 Ignored Sep 8th So Cal Womens Sectional Championship 2018
46 Venom Loss 2-11 273.67 Sep 22nd Southwest Womens Regional Championship 2018
15 Wildfire** Loss 1-11 1123.35 Ignored Sep 22nd Southwest Womens Regional Championship 2018
32 FAB** Loss 2-11 590.7 Ignored Sep 22nd Southwest Womens Regional Championship 2018
2 Fury** Loss 1-11 1760.71 Ignored Sep 22nd Southwest Womens Regional Championship 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)