#55 OAT (16-8)

avg: 1321.73  •  sd: 93.18  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
49 The Killjoys Win 13-10 1712.78 Jun 15th San Diego Slammer 2019
69 Streetgang Win 13-6 1834.48 Jun 15th San Diego Slammer 2019
167 OC Crows** Win 13-2 1274.71 Ignored Jun 15th San Diego Slammer 2019
70 Sundowners Loss 8-13 731.94 Jun 16th San Diego Slammer 2019
69 Streetgang Win 13-7 1792.01 Jun 16th San Diego Slammer 2019
61 Battery Loss 5-13 679.3 Aug 24th Ski Town Classic 2019
80 ISO Atmo Loss 10-13 818.77 Aug 24th Ski Town Classic 2019
90 PowderHogs Win 13-10 1420.94 Aug 24th Ski Town Classic 2019
168 Sandbaggers Win 13-9 1092.78 Aug 24th Ski Town Classic 2019
70 Sundowners Loss 9-13 809.54 Aug 25th Ski Town Classic 2019
174 Daybreak Win 13-7 1193.02 Aug 25th Ski Town Classic 2019
90 PowderHogs Loss 10-12 854.67 Aug 25th Ski Town Classic 2019
158 Cojones Win 11-2 1342.54 Sep 7th Nor Cal Mens Club Sectional Championship 2019
182 Arithmetic Ultimate** Win 11-0 1165.67 Ignored Sep 7th Nor Cal Mens Club Sectional Championship 2019
61 Battery Win 9-8 1404.3 Sep 7th Nor Cal Mens Club Sectional Championship 2019
124 Journeymen Win 11-4 1519.27 Sep 7th Nor Cal Mens Club Sectional Championship 2019
60 Guerrilla Win 15-6 1882.03 Sep 8th Nor Cal Mens Club Sectional Championship 2019
60 Guerrilla Win 14-13 1407.03 Sep 21st Southwest Club Mens Regional Championship 2019
9 SoCal Condors Loss 9-15 1378.46 Sep 21st Southwest Club Mens Regional Championship 2019
69 Streetgang Win 14-13 1359.48 Sep 21st Southwest Club Mens Regional Championship 2019
60 Guerrilla Win 14-13 1407.03 Sep 22nd Southwest Club Mens Regional Championship 2019
61 Battery Win 13-6 1879.3 Sep 22nd Southwest Club Mens Regional Championship 2019
9 SoCal Condors Loss 6-13 1293.94 Sep 22nd Southwest Club Mens Regional Championship 2019
6 Revolver** Loss 5-13 1438.57 Ignored Sep 22nd Southwest Club Mens Regional 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)