#215 Espionage (9-16)

avg: 578.33  •  sd: 60.38  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
102 Tyrannis Loss 2-13 512.17 Jul 13th Battle for the Beltway 2019
128 Legion Loss 5-13 391.65 Jul 13th Battle for the Beltway 2019
233 Stormborn Loss 8-10 187.69 Jul 13th Battle for the Beltway 2019
267 Baltimore BENCH Win 15-4 851.54 Jul 14th Battle for the Beltway 2019
221 District Cocktails Loss 9-13 125.17 Jul 14th Battle for the Beltway 2019
291 Swing Vote Win 15-7 508.67 Jul 14th Battle for the Beltway 2019
201 Nautilus Win 11-10 757.77 Aug 3rd Philly Open 2019
232 Buffalo Brain Freeze Loss 8-11 87.47 Aug 3rd Philly Open 2019
174 Alt Stacks Loss 8-12 300.69 Aug 3rd Philly Open 2019
123 PS Loss 3-13 432.36 Aug 3rd Philly Open 2019
242 Pandatime Win 15-8 976.83 Aug 4th Philly Open 2019
271 Tropics Ultimate Loss 9-11 -6.5 Aug 4th Philly Open 2019
197 HAOS Win 12-11 770.24 Aug 24th The Incident 2019 Age of Ultimatron
89 HVAC Loss 8-13 656.3 Aug 24th The Incident 2019 Age of Ultimatron
142 Superstition Loss 9-11 648.6 Aug 24th The Incident 2019 Age of Ultimatron
180 Varsity Loss 8-9 594.34 Aug 24th The Incident 2019 Age of Ultimatron
201 Nautilus Win 9-3 1232.77 Aug 25th The Incident 2019 Age of Ultimatron
110 Rat City Loss 10-11 949.3 Aug 25th The Incident 2019 Age of Ultimatron
242 Pandatime Win 13-8 908.18 Aug 25th The Incident 2019 Age of Ultimatron
62 8 Bit Heroes** Loss 5-13 688.07 Ignored Sep 7th Capital Mixed Club Sectional Championship 2019
77 Ant Madness** Loss 2-13 621.61 Ignored Sep 7th Capital Mixed Club Sectional Championship 2019
274 WhirlyNegs Win 13-12 338.28 Sep 7th Capital Mixed Club Sectional Championship 2019
190 LORD Win 12-11 790.35 Sep 7th Capital Mixed Club Sectional Championship 2019
106 Fireball Loss 5-15 495.65 Sep 8th Capital Mixed Club Sectional Championship 2019
102 Tyrannis Loss 7-15 512.17 Sep 8th Capital 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)