#93 Battery (10-12)

avg: 883.89  •  sd: 80.11  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
81 Sundowners Loss 8-9 822.72 Jul 7th 2018 San Diego Slammer
144 Gridlock Loss 7-8 343.25 Jul 7th 2018 San Diego Slammer
86 Green River Swordfish Loss 6-11 375.42 Jul 7th 2018 San Diego Slammer
- Whiskeyjacks Loss 8-11 757.26 Jul 7th 2018 San Diego Slammer
78 Rip City Ultimate Win 12-11 1100.79 Jul 8th 2018 San Diego Slammer
- Carbon Win 15-11 1169.5 Jul 8th 2018 San Diego Slammer
80 ISO Atmo Win 12-11 1082.83 Aug 18th Ski Town Classic 2018
89 The Killjoys Loss 10-13 574.94 Aug 18th Ski Town Classic 2018
- NCFO Win 12-11 1052.42 Aug 18th Ski Town Classic 2018
43 Clutch Loss 7-12 734.22 Aug 18th Ski Town Classic 2018
92 Choice City Hops Win 12-10 1126.11 Aug 19th Ski Town Classic 2018
43 Clutch Loss 5-13 654.73 Aug 19th Ski Town Classic 2018
81 Sundowners Win 12-10 1185.84 Aug 19th Ski Town Classic 2018
- Anchor** Win 12-5 849.25 Ignored Sep 8th Nor Cal Mens Sectional Championship 2018
- Journeymen Win 11-4 1064.22 Sep 8th Nor Cal Mens Sectional Championship 2018
86 Green River Swordfish Loss 7-11 455.22 Sep 8th Nor Cal Mens Sectional Championship 2018
19 Guerrilla Loss 9-13 1129.33 Sep 9th Nor Cal Mens Sectional Championship 2018
99 Red Dawn Win 10-5 1417.61 Sep 9th Nor Cal Mens Sectional Championship 2018
- Journeymen Win 12-5 1064.22 Sep 9th Nor Cal Mens Sectional Championship 2018
1 Revolver** Loss 4-15 1479.65 Ignored Sep 22nd Southwest Mens Regional Championship 2018
19 Guerrilla** Loss 6-15 947.89 Ignored Sep 22nd Southwest Mens Regional Championship 2018
81 Sundowners Loss 10-15 494.12 Sep 22nd Southwest Mens Regional 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)