#144 Rat City (8-10)

avg: 890.57  •  sd: 67.19  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
207 District Cocktails Win 13-3 1135.79 Jul 14th Battle for the Beltway 2018
173 Fake Newport News Win 13-6 1341.57 Jul 14th Battle for the Beltway 2018
87 Sparkle Ponies Loss 7-10 787.76 Jul 14th Battle for the Beltway 2018
- Swing Vote** Win 13-1 794.12 Ignored Jul 14th Battle for the Beltway 2018
179 LORD Win 13-6 1281.22 Jul 15th Battle for the Beltway 2018
87 Sparkle Ponies Loss 11-12 1052.43 Jul 15th Battle for the Beltway 2018
101 Tyrannis Loss 6-11 565.7 Aug 11th Chesapeake Open 2018
149 Crucible Win 12-11 1004.71 Aug 11th Chesapeake Open 2018
163 Stoke Win 11-8 1162.22 Aug 11th Chesapeake Open 2018
95 Ant Madness Loss 3-12 547.28 Aug 11th Chesapeake Open 2018
126 American Hyperbole Loss 9-12 636.89 Aug 12th Chesapeake Open 2018
163 Stoke Loss 10-11 671.61 Aug 12th Chesapeake Open 2018
126 American Hyperbole Loss 9-10 857.26 Sep 8th Capital Mixed Sectional Championship 2018
207 District Cocktails Win 13-5 1135.79 Sep 8th Capital Mixed Sectional Championship 2018
173 Fake Newport News Win 13-7 1299.1 Sep 8th Capital Mixed Sectional Championship 2018
5 Space Heater** Loss 4-13 1362.94 Ignored Sep 8th Capital Mixed Sectional Championship 2018
132 HVAC Loss 6-10 466.37 Sep 9th Capital Mixed Sectional Championship 2018
173 Fake Newport News Loss 9-11 492.36 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)