#235 Skyhawks (5-15)

avg: 244.51  •  sd: 79.19  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
129 Moonshine** Loss 4-11 371.57 Ignored Jul 21st Bourbon Bash 2018
- Pocket City Approach Loss 3-9 -334.06 Jul 21st Bourbon Bash 2018
223 Petey's Scallywags Win 9-6 779.26 Jul 21st Bourbon Bash 2018
238 Strictly Bidness Win 9-6 605.98 Jul 21st Bourbon Bash 2018
198 Second Wind Loss 4-11 -18.75 Jul 21st Bourbon Bash 2018
122 Huntsville Outlaws Loss 7-12 496.21 Jul 22nd Bourbon Bash 2018
- Pocket City Approach Win 8-6 566.43 Jul 22nd Bourbon Bash 2018
198 Second Wind Loss 9-10 456.25 Jul 22nd Bourbon Bash 2018
184 Mousetrap Loss 7-13 100.54 Aug 4th Heavyweights 2018
160 Mad Udderburn Loss 3-13 202.17 Aug 4th Heavyweights 2018
140 Rocket LawnChair** Loss 2-13 295.66 Ignored Aug 4th Heavyweights 2018
185 Boomtown Pandas Loss 3-12 50.51 Aug 5th Heavyweights 2018
246 Taco Cat Win 9-7 183.17 Aug 5th Heavyweights 2018
211 Stackcats Loss 2-13 -81.02 Aug 5th Heavyweights 2018
186 Jabba Loss 4-11 50.2 Sep 8th Central Plains Mixed Sectional Championship 2018
155 Liquid Hustle Loss 6-11 295.13 Sep 8th Central Plains Mixed Sectional Championship 2018
172 Los Heros Loss 5-11 144.78 Sep 8th Central Plains Mixed Sectional Championship 2018
246 Taco Cat Win 11-0 503.83 Sep 8th Central Plains Mixed Sectional Championship 2018
104 Shakedown** Loss 3-11 500.57 Ignored Sep 8th Central Plains Mixed Sectional Championship 2018
137 ELevate** Loss 5-15 341.87 Ignored Sep 9th Central Plains Mixed 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)