#225 Big Bend (4-11)

avg: 351.82  •  sd: 69.1  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
19 Bucket** Loss 3-13 1105.23 Ignored Jun 16th ATL Classic 2018
82 Method** Loss 4-13 632.62 Ignored Jun 16th ATL Classic 2018
244 Mixchief Win 13-5 577.14 Jun 16th ATL Classic 2018
151 LoveShack Loss 7-13 298.9 Jun 16th ATL Classic 2018
244 Mixchief Win 13-5 577.14 Jun 17th ATL Classic 2018
177 OutKast Loss 8-13 200.98 Jun 17th ATL Classic 2018
90 Mutiny Loss 6-13 565.68 Jul 7th Swan Boat 2018
244 Mixchief Win 13-8 473.3 Jul 7th Swan Boat 2018
- Bold City Loss 7-8 450.94 Jul 7th Swan Boat 2018
105 Jackpot** Loss 4-13 485.69 Ignored Jul 7th Swan Boat 2018
244 Mixchief Win 15-10 430.75 Jul 8th Swan Boat 2018
- Bold City Loss 11-15 194.78 Jul 8th Swan Boat 2018
27 Weird** Loss 2-13 977.85 Ignored Sep 8th Florida Mixed Sectional Championship 2018
105 Jackpot** Loss 2-13 485.69 Ignored Sep 8th Florida Mixed Sectional Championship 2018
244 Mixchief Loss 4-5 -147.86 Sep 9th Florida Mixed Sectional 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)