#55 Streetgang (12-9)

avg: 1289.61  •  sd: 61.68  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
119 DOGGPOUND Win 13-8 1446 Jun 15th San Diego Slammer 2019
62 OAT Loss 6-13 657.83 Jun 15th San Diego Slammer 2019
46 The Killjoys Loss 11-13 1138.68 Jun 15th San Diego Slammer 2019
119 DOGGPOUND Win 13-10 1277.99 Jun 16th San Diego Slammer 2019
62 OAT Loss 7-13 700.3 Jun 16th San Diego Slammer 2019
46 The Killjoys Loss 12-13 1242.52 Jun 16th San Diego Slammer 2019
176 Daybreak Win 15-10 1090.2 Jul 13th TCT Select Flight Invite West 2019
47 Haymaker Loss 12-14 1140.47 Jul 13th TCT Select Flight Invite West 2019
73 ISO Atmo Win 12-9 1533.07 Jul 13th TCT Select Flight Invite West 2019
29 Clutch Win 13-11 1767.28 Jul 14th TCT Select Flight Invite West 2019
47 Haymaker Loss 8-10 1098.76 Jul 14th TCT Select Flight Invite West 2019
51 Turbine Loss 12-13 1207.69 Jul 14th TCT Select Flight Invite West 2019
165 OC Crows Win 14-8 1231.44 Jul 27th Angel City Shootout 2019
119 DOGGPOUND Win 15-4 1549.84 Jul 27th Angel City Shootout 2019
61 Sundowners Win 15-14 1393.07 Jul 27th Angel City Shootout 2019
194 Goaltimate All Stars** Win 13-5 1103.16 Ignored Sep 7th So Cal Mens Club Sectional Championship 2019
9 SoCal Condors Loss 9-13 1551.82 Sep 7th So Cal Mens Club Sectional Championship 2019
61 Sundowners Loss 10-11 1143.07 Sep 7th So Cal Mens Club Sectional Championship 2019
228 desert penguins** Win 13-4 826.12 Ignored Sep 7th So Cal Mens Club Sectional Championship 2019
165 OC Crows Win 13-4 1295.41 Sep 8th So Cal Mens Club Sectional Championship 2019
119 DOGGPOUND Win 13-6 1549.84 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)