#51 Turbine (11-8)

avg: 1332.69  •  sd: 66.47  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
23 CLE Smokestack Loss 5-15 1035.66 Jul 13th TCT Select Flight Invite West 2019
72 Sawtooth Win 15-7 1788.67 Jul 13th TCT Select Flight Invite West 2019
67 UpRoar Win 15-8 1777.13 Jul 13th TCT Select Flight Invite West 2019
23 CLE Smokestack Loss 7-13 1078.13 Jul 14th TCT Select Flight Invite West 2019
55 Streetgang Win 13-12 1414.61 Jul 14th TCT Select Flight Invite West 2019
49 El Niño Win 11-9 1597.19 Jul 14th TCT Select Flight Invite West 2019
104 Charleston Heat Stroke Win 13-7 1582.75 Aug 24th FCS Invite 2019
35 Tanasi Loss 9-11 1224.42 Aug 24th FCS Invite 2019
75 Richmond Floodwall Win 11-7 1632.06 Aug 24th FCS Invite 2019
82 Black Lung Win 12-9 1471.69 Aug 24th FCS Invite 2019
122 Cockfight Win 11-9 1172.66 Aug 25th FCS Invite 2019
37 Lost Boys Loss 10-15 1007.48 Aug 25th FCS Invite 2019
35 Tanasi Loss 12-15 1173.13 Aug 25th FCS Invite 2019
115 baNC Loss 10-11 837.39 Sep 7th North Carolina Mens Club Sectional Championship 2019
79 Bash Bros Win 10-8 1412.38 Sep 7th North Carolina Mens Club Sectional Championship 2019
24 Brickhouse Loss 7-10 1178.54 Sep 7th North Carolina Mens Club Sectional Championship 2019
115 baNC Win 15-8 1527.2 Sep 8th North Carolina Mens Club Sectional Championship 2019
79 Bash Bros Win 9-1 1749.72 Sep 8th North Carolina Mens Club Sectional Championship 2019
24 Brickhouse Loss 7-15 968.2 Sep 8th North Carolina 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)