#54 Battery (15-5)

avg: 1307.49  •  sd: 64.36  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
191 Midnight Meat Train** Win 13-1 1126.4 Ignored Aug 3rd Heavyweights 2019
101 Imperial Win 13-6 1639.84 Aug 3rd Heavyweights 2019
237 Kettering** Win 13-1 558.38 Ignored Aug 3rd Heavyweights 2019
48 Cryptic Loss 12-13 1231.2 Aug 4th Heavyweights 2019
47 Haymaker Loss 8-13 865.26 Aug 4th Heavyweights 2019
99 Black Market II Win 13-11 1281.24 Aug 4th Heavyweights 2019
82 Black Lung Win 13-11 1355.17 Aug 4th Heavyweights 2019
176 Daybreak** Win 13-1 1236.59 Ignored Aug 24th Ski Town Classic 2019
170 Sandbaggers Win 13-10 989.88 Aug 24th Ski Town Classic 2019
73 ISO Atmo Win 12-10 1425.82 Aug 24th Ski Town Classic 2019
62 OAT Win 13-5 1857.83 Aug 24th Ski Town Classic 2019
72 Sawtooth Loss 8-10 926 Aug 25th Ski Town Classic 2019
97 PowderHogs Win 13-11 1292.95 Aug 25th Ski Town Classic 2019
46 The Killjoys Loss 10-11 1242.52 Aug 25th Ski Town Classic 2019
156 Cojones Win 11-4 1342.9 Sep 7th Nor Cal Mens Club Sectional Championship 2019
127 Journeymen Win 11-4 1500.7 Sep 7th Nor Cal Mens Club Sectional Championship 2019
184 Arithmetic Ultimate Win 11-6 1106.57 Sep 7th Nor Cal Mens Club Sectional Championship 2019
62 OAT Loss 8-9 1132.83 Sep 7th Nor Cal Mens Club Sectional Championship 2019
121 Green River Swordfish Win 15-8 1505 Sep 8th Nor Cal Mens Club Sectional Championship 2019
63 Guerrilla Win 15-13 1471.28 Sep 8th Nor Cal Mens 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)