#218 Megalodon (6-14)

avg: 559.61  •  sd: 68.96  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
195 Birds of Paradise Win 12-8 1089.92 Jul 20th Revolution 2019
184 DR Loss 9-12 352.08 Jul 20th Revolution 2019
260 Party Cats-D Win 11-6 847.84 Jul 20th Revolution 2019
142 Superstition Loss 11-13 668.96 Jul 20th Revolution 2019
237 Feral Cows Loss 7-12 -89.28 Jul 21st Revolution 2019
163 VU Loss 6-13 214.21 Jul 21st Revolution 2019
261 Happy Cows Win 11-6 843.58 Jul 21st Revolution 2019
161 Breakers Mark Loss 10-14 428.83 Aug 3rd Kleinman Eruption 2019
119 Bulleit Train Loss 6-15 453.77 Aug 3rd Kleinman Eruption 2019
67 The Administrators Loss 9-15 739.7 Aug 3rd Kleinman Eruption 2019
127 Platypi Loss 5-15 404.27 Aug 3rd Kleinman Eruption 2019
253 Friendzone Win 13-7 899.16 Aug 4th Kleinman Eruption 2019
276 SkyLab Win 15-9 703.94 Aug 4th Kleinman Eruption 2019
239 Midnight Whiskey Loss 12-13 304.78 Aug 4th Kleinman Eruption 2019
9 Blackbird** Loss 2-13 1225.7 Ignored Sep 7th Nor Cal Mixed Club Sectional Championship 2019
88 Alchemy Loss 7-12 632.64 Sep 7th Nor Cal Mixed Club Sectional Championship 2019
66 Firefly** Loss 4-13 657.29 Ignored Sep 7th Nor Cal Mixed Club Sectional Championship 2019
184 DR Win 10-9 822.45 Sep 7th Nor Cal Mixed Club Sectional Championship 2019
118 Absolute Zero Loss 3-13 454.19 Sep 8th Nor Cal Mixed Club Sectional Championship 2019
145 The Los Gatos Gatos-Senior Loss 7-13 330.29 Sep 8th Nor Cal 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)