#224 Stormborn (4-21)

avg: 357.45  •  sd: 89  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
212 Mixed Results Loss 6-13 -85.95 Jun 23rd Summer Glazed Daze 2018
42 Mixfits** Loss 2-13 874.53 Ignored Jun 23rd Summer Glazed Daze 2018
101 Tyrannis** Loss 3-13 512.39 Ignored Jun 23rd Summer Glazed Daze 2018
63 Rowdy** Loss 4-12 688.72 Ignored Jun 23rd Summer Glazed Daze 2018
192 RnB Loss 11-13 389.93 Jun 24th Summer Glazed Daze 2018
153 APEX Loss 9-13 434.79 Jun 24th Summer Glazed Daze 2018
41 Storm** Loss 2-13 879.16 Ignored Jun 24th Summer Glazed Daze 2018
156 Heavy Flow Loss 7-9 560.22 Jul 21st SunRise Open 2018
132 HVAC Loss 7-9 683.19 Jul 21st SunRise Open 2018
173 Fake Newport News Loss 6-11 194.87 Jul 21st SunRise Open 2018
207 District Cocktails Loss 9-11 286.59 Jul 22nd SunRise Open 2018
243 SPACE INVADERS Win 12-9 366 Jul 22nd SunRise Open 2018
206 Varsity Loss 3-7 -57.3 Aug 11th Philly Open 2018
156 Heavy Flow Loss 6-13 239.56 Aug 11th Philly Open 2018
232 Baltimore BENCH Win 5-4 414.24 Aug 11th Philly Open 2018
83 Birds Loss 6-13 625.54 Aug 11th Philly Open 2018
165 Unlimited Swipes Loss 8-9 663.26 Aug 12th Philly Open 2018
232 Baltimore BENCH Loss 5-9 -239.82 Aug 12th Philly Open 2018
- Hott Olson Loss 9-10 242.38 Sep 8th Capital Mixed Sectional Championship 2018
- Huck Norris Win 9-5 700.03 Sep 8th Capital Mixed Sectional Championship 2018
24 Rally** Loss 2-11 1011.11 Ignored Sep 8th Capital Mixed Sectional Championship 2018
99 Legion Loss 5-11 515.26 Sep 8th Capital Mixed Sectional Championship 2018
- Fireball Loss 5-11 626.5 Sep 8th Capital Mixed Sectional Championship 2018
- Vacation Win 9-5 508.66 Sep 9th Capital Mixed Sectional Championship 2018
232 Baltimore BENCH Loss 4-7 -206.92 Sep 9th Capital 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)