#207 District Cocktails (6-13)

avg: 535.79  •  sd: 79.74  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
87 Sparkle Ponies Loss 6-12 598.12 Jul 14th Battle for the Beltway 2018
179 LORD Loss 9-10 556.22 Jul 14th Battle for the Beltway 2018
173 Fake Newport News Loss 7-11 274.67 Jul 14th Battle for the Beltway 2018
144 Rat City Loss 3-13 290.57 Jul 14th Battle for the Beltway 2018
- Swing Vote Win 13-7 751.65 Jul 15th Battle for the Beltway 2018
173 Fake Newport News Win 12-5 1341.57 Jul 15th Battle for the Beltway 2018
179 LORD Loss 5-9 152.16 Jul 15th Battle for the Beltway 2018
95 Ant Madness Loss 5-10 573.38 Jul 21st SunRise Open 2018
153 APEX Loss 9-10 728.35 Jul 21st SunRise Open 2018
243 SPACE INVADERS Win 13-4 620.63 Jul 21st SunRise Open 2018
224 Stormborn Win 11-9 606.66 Jul 22nd SunRise Open 2018
173 Fake Newport News Loss 9-12 396.2 Jul 22nd SunRise Open 2018
126 American Hyperbole Loss 7-12 461.75 Sep 8th Capital Mixed Sectional Championship 2018
5 Space Heater** Loss 1-13 1362.94 Ignored Sep 8th Capital Mixed Sectional Championship 2018
173 Fake Newport News Loss 9-12 396.2 Sep 8th Capital Mixed Sectional Championship 2018
144 Rat City Loss 5-13 290.57 Sep 8th Capital Mixed Sectional Championship 2018
- Vacation Win 9-2 579.6 Sep 9th Capital Mixed Sectional Championship 2018
- Hott Olson Win 15-3 967.38 Sep 9th Capital Mixed Sectional Championship 2018
179 LORD Loss 4-8 116.41 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)