#76 Boomslang (5-11)

avg: 51.28  •  sd: 71  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
51 Vice** Loss 2-15 195.41 Ignored Jun 23rd Boston Invite 2018
42 Pine Baroness** Loss 0-15 422.63 Ignored Jun 23rd Boston Invite 2018
67 Broad City Loss 5-13 -163.08 Jun 23rd Boston Invite 2018
- Tempest** Loss 6-15 77.98 Ignored Jun 24th Boston Invite 2018
- BUDA Win 11-8 398.62 Jun 24th Boston Invite 2018
67 Broad City Loss 7-11 -29.98 Jun 24th Boston Invite 2018
51 Vice** Loss 0-9 195.41 Ignored Aug 11th Philly Open 2018
64 Suffrage Loss 2-13 -92.95 Aug 11th Philly Open 2018
79 DINO Win 13-2 123.43 Aug 11th Philly Open 2018
67 Broad City Loss 5-12 -163.08 Aug 11th Philly Open 2018
- TOX6ix Loss 4-12 -72.46 Sep 8th Upstate New York Womens Sectional Championship 2018
- Roc Paper Scissors Win 11-5 315.81 Sep 8th Upstate New York Womens Sectional Championship 2018
- Salt City Spirit Win 9-8 21.79 Sep 8th Upstate New York Womens Sectional Championship 2018
13 Stella** Loss 4-15 1142.5 Ignored Sep 8th Upstate New York Womens Sectional Championship 2018
- TOX6ix Loss 5-15 -72.46 Sep 9th Upstate New York Womens Sectional Championship 2018
- Roc Paper Scissors Win 13-7 273.35 Sep 9th Upstate New York Womens Sectional 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)