#167 OC Crows (4-18)

avg: 674.71  •  sd: 50.24  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
70 Sundowners Loss 5-13 628.1 Jun 15th San Diego Slammer 2019
55 OAT** Loss 2-13 721.73 Ignored Jun 15th San Diego Slammer 2019
119 DOGGPOUND Loss 6-13 337.89 Jun 15th San Diego Slammer 2019
49 The Killjoys Loss 7-13 827.11 Jun 16th San Diego Slammer 2019
119 DOGGPOUND Loss 12-13 812.89 Jun 16th San Diego Slammer 2019
70 Sundowners Loss 3-15 628.1 Jul 27th Angel City Shootout 2019
69 Streetgang Loss 8-14 698.44 Jul 27th Angel City Shootout 2019
119 DOGGPOUND Loss 10-15 484.28 Jul 27th Angel City Shootout 2019
49 The Killjoys** Loss 3-13 784.64 Ignored Aug 24th Ski Town Classic 2019
113 Choice City Hops Loss 6-13 391.9 Aug 24th Ski Town Classic 2019
158 Cojones Loss 10-12 504.42 Aug 24th Ski Town Classic 2019
68 Sawtooth Loss 2-13 636.41 Aug 24th Ski Town Classic 2019
70 Sundowners Loss 3-13 628.1 Aug 25th Ski Town Classic 2019
174 Daybreak Win 9-6 1054.05 Aug 25th Ski Town Classic 2019
168 Sandbaggers Win 11-7 1141.11 Aug 25th Ski Town Classic 2019
195 Goaltimate All Stars Win 12-9 825.77 Sep 7th So Cal Mens Club Sectional Championship 2019
70 Sundowners Loss 7-12 707.59 Sep 7th So Cal Mens Club Sectional Championship 2019
119 DOGGPOUND Loss 12-13 812.89 Sep 7th So Cal Mens Club Sectional Championship 2019
229 desert penguins Win 10-6 703.87 Sep 7th So Cal Mens Club Sectional Championship 2019
9 SoCal Condors** Loss 2-13 1293.94 Ignored Sep 8th So Cal Mens Club Sectional Championship 2019
69 Streetgang Loss 4-13 634.48 Sep 8th So Cal Mens Club Sectional Championship 2019
119 DOGGPOUND Loss 6-15 337.89 Sep 8th So 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)