#268 Orbit (2-17)

avg: 298.04  •  sd: 83.7  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
271 Bold City Loss 5-9 -241.75 Jun 15th ATL Classic 2019
47 Huntsville Outlaws** Loss 1-13 873.42 Ignored Jun 15th ATL Classic 2019
164 BATL Cows Loss 3-12 256.2 Jun 15th ATL Classic 2019
278 Baywatch Loss 8-9 90.25 Jun 15th ATL Classic 2019
235 Mississippi Blues Loss 8-13 -10.25 Jun 16th ATL Classic 2019
252 Big Bend Loss 5-10 -161.79 Jun 16th ATL Classic 2019
255 Mixchief Win 10-4 1000.84 Jun 16th ATL Classic 2019
235 Mississippi Blues Loss 9-10 360.91 Jul 6th Huntsville Huckfest 2019
74 Trash Pandas** Loss 5-13 673.88 Ignored Jul 6th Huntsville Huckfest 2019
83 Old #7** Loss 4-13 637.85 Ignored Jul 6th Huntsville Huckfest 2019
174 Magic City Mayhem Loss 11-13 557.33 Jul 6th Huntsville Huckfest 2019
230 The Umbrella Loss 10-11 388.47 Jul 7th Huntsville Huckfest 2019
284 Mixed on the Rock Win 13-8 632.51 Jul 7th Huntsville Huckfest 2019
167 Possum Loss 6-13 236.1 Jul 7th Huntsville Huckfest 2019
164 BATL Cows Loss 8-10 593.54 Sep 7th East Coast Mixed Club Sectional Championship 2019
34 'Shine** Loss 0-13 988.45 Ignored Sep 7th East Coast Mixed Club Sectional Championship 2019
162 OutKast Loss 4-11 272.91 Sep 7th East Coast Mixed Club Sectional Championship 2019
167 Possum Loss 4-13 236.1 Sep 7th East Coast Mixed Club Sectional Championship 2019
230 The Umbrella Loss 9-13 94.91 Sep 8th East Coast Mixed Club Sectional Championship 2019
**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)