#144 Gridlock (1-18)

avg: 468.25  •  sd: 61.85  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
93 Battery Win 8-7 1008.89 Jul 7th 2018 San Diego Slammer
- Whiskeyjacks Loss 7-11 655.97 Jul 7th 2018 San Diego Slammer
81 Sundowners Loss 5-9 418.66 Jul 7th 2018 San Diego Slammer
86 Green River Swordfish Loss 7-11 455.22 Jul 7th 2018 San Diego Slammer
24 Inception** Loss 1-11 821.34 Ignored Jul 7th 2018 San Diego Slammer
78 Rip City Ultimate Loss 8-15 410.99 Jul 8th 2018 San Diego Slammer
- Carbon Loss 11-15 407.17 Jul 8th 2018 San Diego Slammer
78 Rip City Ultimate Loss 4-13 375.79 Aug 18th Ski Town Classic 2018
92 Choice City Hops Loss 5-13 287.99 Aug 18th Ski Town Classic 2018
88 PowderHogs Loss 6-13 308.48 Aug 18th Ski Town Classic 2018
81 Sundowners Loss 6-13 347.72 Aug 18th Ski Town Classic 2018
78 Rip City Ultimate Loss 9-13 557.23 Aug 19th Ski Town Classic 2018
80 ISO Atmo Loss 2-13 357.83 Aug 19th Ski Town Classic 2018
74 DOGGPOUND Loss 2-11 397.3 Sep 8th So Cal Mens Sectional Championship 2018
91 Sprawl Loss 5-11 297.37 Sep 8th So Cal Mens Sectional Championship 2018
40 Streetgang** Loss 2-11 675.1 Ignored Sep 8th So Cal Mens Sectional Championship 2018
81 Sundowners Loss 9-10 822.72 Sep 8th So Cal Mens Sectional Championship 2018
17 SoCal Condors** Loss 2-11 1081.02 Ignored Sep 9th So Cal Mens Sectional Championship 2018
81 Sundowners Loss 6-9 529.15 Sep 9th So Cal Mens 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)