#81 Sundowners (11-15)

avg: 947.72  •  sd: 78.95  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
93 Battery Win 9-8 1008.89 Jul 7th 2018 San Diego Slammer
- Whiskeyjacks Loss 9-11 873.66 Jul 7th 2018 San Diego Slammer
24 Inception Loss 2-11 821.34 Jul 7th 2018 San Diego Slammer
144 Gridlock Win 9-5 997.31 Jul 7th 2018 San Diego Slammer
86 Green River Swordfish Win 11-7 1389.01 Jul 7th 2018 San Diego Slammer
91 Sprawl Loss 13-14 772.37 Jul 8th 2018 San Diego Slammer
88 PowderHogs Win 13-9 1327.04 Jul 8th 2018 San Diego Slammer
78 Rip City Ultimate Loss 11-12 850.79 Aug 18th Ski Town Classic 2018
92 Choice City Hops Win 12-11 1012.99 Aug 18th Ski Town Classic 2018
88 PowderHogs Loss 6-13 308.48 Aug 18th Ski Town Classic 2018
144 Gridlock Win 13-6 1068.25 Aug 18th Ski Town Classic 2018
93 Battery Loss 10-12 645.77 Aug 19th Ski Town Classic 2018
88 PowderHogs Loss 10-11 783.48 Aug 19th Ski Town Classic 2018
89 The Killjoys Win 13-5 1503.08 Aug 19th Ski Town Classic 2018
74 DOGGPOUND Loss 9-10 872.3 Sep 8th So Cal Mens Sectional Championship 2018
17 SoCal Condors Loss 5-11 1081.02 Sep 8th So Cal Mens Sectional Championship 2018
91 Sprawl Loss 9-11 648.16 Sep 8th So Cal Mens Sectional Championship 2018
144 Gridlock Win 10-9 593.25 Sep 8th So Cal Mens Sectional Championship 2018
91 Sprawl Win 13-4 1497.37 Sep 9th So Cal Mens Sectional Championship 2018
40 Streetgang Loss 8-11 909.49 Sep 9th So Cal Mens Sectional Championship 2018
144 Gridlock Win 9-6 886.82 Sep 9th So Cal Mens Sectional Championship 2018
1 Revolver** Loss 5-15 1479.65 Ignored Sep 22nd Southwest Mens Regional Championship 2018
93 Battery Win 15-10 1337.49 Sep 22nd Southwest Mens Regional Championship 2018
19 Guerrilla** Loss 6-15 947.89 Ignored Sep 22nd Southwest Mens Regional Championship 2018
40 Streetgang Loss 11-15 893.93 Sep 23rd Southwest Mens Regional Championship 2018
86 Green River Swordfish Loss 12-13 797.11 Sep 23rd 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)