#165 Unlimited Swipes (11-12)

avg: 788.26  •  sd: 65.22  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
50 Grand Army Loss 8-15 804.87 Jul 7th Philly Invite 2018
113 The Bandits Loss 8-14 507.46 Jul 7th Philly Invite 2018
158 Philly Twist Loss 7-11 348.21 Jul 7th Philly Invite 2018
59 Distelfink Loss 5-15 700.71 Jul 7th Philly Invite 2018
237 Turnstyle Win 13-8 701.44 Jul 8th Philly Invite 2018
163 Stoke Loss 9-11 547.4 Jul 8th Philly Invite 2018
- Philadelphia Forge Win 11-5 667.18 Jul 8th Philly Invite 2018
127 Funk Loss 10-13 652.74 Aug 11th Philly Open 2018
117 The Process Win 11-7 1499.28 Aug 11th Philly Open 2018
99 Legion Loss 7-12 594.75 Aug 11th Philly Open 2018
158 Philly Twist Loss 2-5 215.1 Aug 11th Philly Open 2018
232 Baltimore BENCH Win 11-5 889.24 Aug 12th Philly Open 2018
224 Stormborn Win 9-8 482.45 Aug 12th Philly Open 2018
231 BLT Stacks Win 15-7 898.67 Aug 25th The Incident 2018
158 Philly Twist Win 12-10 1053.23 Aug 25th The Incident 2018
232 Baltimore BENCH Win 15-5 889.24 Aug 25th The Incident 2018
239 Pandatime** Win 15-5 767.17 Ignored Aug 25th The Incident 2018
127 Funk Loss 10-12 742.76 Aug 26th The Incident 2018
132 HVAC Win 14-9 1436.39 Aug 26th The Incident 2018
158 Philly Twist Loss 10-11 690.1 Aug 26th The Incident 2018
127 Funk Loss 9-13 562.31 Sep 8th Metro New York Mixed Sectional Championship 2018
141 Powermove Loss 9-10 768.51 Sep 8th Metro New York Mixed Sectional Championship 2018
- Super Happy Fun Time Win 12-11 1053.19 Sep 8th Metro New York 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)