#62 Inferno (8-11)

avg: 573.17  •  sd: 106.78  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
- Temptress Win 11-4 632.09 Jun 30th Texas Two Finger 2018
14 Showdown** Loss 0-11 1131.13 Ignored Jun 30th Texas Two Finger 2018
- Cazadora** Win 11-3 461.76 Ignored Jun 30th Texas Two Finger 2018
- Texas Tango Win 9-4 877.48 Jun 30th Texas Two Finger 2018
54 Maeve Win 6-4 1082.8 Jun 30th Texas Two Finger 2018
59 Queen Cake Loss 0-13 14.29 Jul 21st Club Terminus 2018
63 Taco Truck Win 10-8 829.5 Jul 21st Club Terminus 2018
29 Virginia Rebellion** Loss 2-13 756.45 Ignored Jul 22nd Club Terminus 2018
73 Honey Pot Win 10-3 849.43 Jul 22nd Club Terminus 2018
45 Outbreak Loss 5-13 303.78 Jul 22nd Club Terminus 2018
40 Steel Loss 6-13 441 Jul 22nd Club Terminus 2018
- Cazadora Win 11-5 461.76 Sep 8th Texas Womens Sectional Championship 2018
54 Maeve Loss 4-8 152.38 Sep 8th Texas Womens Sectional Championship 2018
47 Trainwreck Loss 8-12 397.03 Sep 22nd South Central Womens Regional Championship 2018
14 Showdown** Loss 0-15 1131.13 Ignored Sep 22nd South Central Womens Regional Championship 2018
25 Colorado Small Batch** Loss 3-15 847.1 Ignored Sep 22nd South Central Womens Regional Championship 2018
3 Molly Brown** Loss 0-15 1673.57 Ignored Sep 22nd South Central Womens Regional Championship 2018
54 Maeve Win 11-7 1184.08 Sep 23rd South Central Womens Regional Championship 2018
38 Jackwagon Loss 5-15 481.53 Sep 23rd South Central Womens Regional Championship 2018
**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)