#27 Turbine (13-7)

avg: 1392.19  •  sd: 127.68  •  top 16/20: 4.7%

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# Opponent Result Game Rating Status Date Event
169 Bearfest** Win 11-1 634.92 Ignored Jul 28th 2018 Richmond Stonewalled
135 Helots** Win 11-3 1186.33 Ignored Jul 28th 2018 Richmond Stonewalled
52 Oakgrove Boys Win 11-3 1786.93 Jul 28th 2018 Richmond Stonewalled
136 Pipeline** Win 11-3 1180.82 Ignored Jul 28th 2018 Richmond Stonewalled
33 Richmond Floodwall Win 13-6 1943.56 Jul 29th 2018 Richmond Stonewalled
76 Slag Dump Win 13-3 1590.89 Jul 29th 2018 Richmond Stonewalled
20 Patrol Loss 12-13 1415.7 Aug 18th TCT Elite Select Challenge 2018
18 Pittsburgh Temper Loss 6-13 1071.4 Aug 18th TCT Elite Select Challenge 2018
17 SoCal Condors Loss 12-14 1460.06 Aug 18th TCT Elite Select Challenge 2018
26 Brickyard Win 10-8 1661.5 Aug 19th TCT Elite Select Challenge 2018
22 Voodoo Loss 8-12 1063.32 Aug 19th TCT Elite Select Challenge 2018
107 BaNC Win 13-8 1271.8 Sep 8th North Carolina Mens Sectional Championship 2018
37 Brickhouse Win 13-8 1784.37 Sep 8th North Carolina Mens Sectional Championship 2018
- Ra** Win 13-5 796.87 Ignored Sep 8th North Carolina Mens Sectional Championship 2018
- The Semple Temple** Win 13-3 568.03 Ignored Sep 8th North Carolina Mens Sectional Championship 2018
37 Brickhouse Win 12-7 1808.73 Sep 9th North Carolina Mens Sectional Championship 2018
71 UpRoar Loss 11-13 794.37 Sep 22nd Southeast Mens Regional Championship 2018
75 Omen Win 12-9 1338.11 Sep 22nd Southeast Mens Regional Championship 2018
41 Coastal Empire Loss 11-12 1145.34 Sep 22nd Southeast Mens Regional Championship 2018
23 Freaks Loss 10-13 1172.97 Sep 22nd Southeast 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)