#63 Taco Truck (5-14)

avg: 566.83  •  sd: 85.32  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
29 Virginia Rebellion** Loss 3-13 756.45 Ignored Jun 16th Cackalacky Challenge 2018
18 Phoenix** Loss 3-13 1097.67 Ignored Jun 16th Cackalacky Challenge 2018
- Warhawks Win 10-9 719.4 Jun 16th Cackalacky Challenge 2018
29 Virginia Rebellion Loss 8-11 990.84 Jun 17th Cackalacky Challenge 2018
- Ripe** Loss 4-13 990.91 Jun 17th Cackalacky Challenge 2018
- Warhawks Win 11-8 960.01 Jun 17th Cackalacky Challenge 2018
40 Steel Loss 6-12 461.69 Jul 21st Club Terminus 2018
62 Inferno Loss 8-10 310.5 Jul 21st Club Terminus 2018
73 Honey Pot Win 13-6 849.43 Jul 22nd Club Terminus 2018
45 Outbreak Loss 9-11 654.57 Jul 22nd Club Terminus 2018
59 Queen Cake Win 10-8 876.95 Jul 22nd Club Terminus 2018
27 Tabby Rosa** Loss 3-13 781.55 Ignored Jul 22nd Club Terminus 2018
18 Phoenix** Loss 3-13 1097.67 Ignored Sep 8th North Carolina Womens Sectional Championship 2018
37 Fiasco Loss 4-15 496.58 Sep 22nd Southeast Womens Regional Championship 2018
18 Phoenix** Loss 2-15 1097.67 Ignored Sep 22nd Southeast Womens Regional Championship 2018
40 Steel Loss 7-12 520.49 Sep 22nd Southeast Womens Regional Championship 2018
73 Honey Pot Win 11-9 498.64 Sep 23rd Southeast Womens Regional Championship 2018
45 Outbreak Loss 7-15 303.78 Sep 23rd Southeast Womens Regional Championship 2018
59 Queen Cake Loss 4-8 49.48 Sep 23rd Southeast 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)