#120 baNC (8-17)

avg: 936.22  •  sd: 69.61  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
108 H.O.G. Ultimate Loss 11-12 887.99 Jun 15th ATL Classic 2019
203 War Machine Win 12-7 959.54 Jun 15th ATL Classic 2019
52 El Niño Loss 6-13 756.84 Jun 15th ATL Classic 2019
67 Ironmen Loss 6-13 638.16 Jun 15th ATL Classic 2019
127 Rougaroux Loss 6-10 409.81 Jun 16th ATL Classic 2019
203 War Machine Win 13-4 1039.03 Jun 16th ATL Classic 2019
37 Tanasi Loss 10-12 1237.13 Jul 20th 2019 Club Terminus
221 Traffic Win 13-6 862.31 Jul 20th 2019 Club Terminus
117 Rush Hour ATL Loss 7-13 410.65 Jul 20th 2019 Club Terminus
127 Rougaroux Win 13-6 1505.97 Jul 20th 2019 Club Terminus
86 Bullet Loss 8-13 610.54 Jul 21st 2019 Club Terminus
67 Ironmen Win 13-7 1795.69 Jul 21st 2019 Club Terminus
85 ATLiens Loss 6-13 509.18 Jul 21st 2019 Club Terminus
107 Fathom Loss 10-12 781.41 Aug 24th FCS Invite 2019
38 Lost Boys Loss 8-13 946.84 Aug 24th FCS Invite 2019
23 Vault** Loss 2-13 1023.65 Ignored Aug 24th FCS Invite 2019
99 Bash Bros Loss 9-13 624.15 Aug 24th FCS Invite 2019
84 Black Lung Loss 11-12 1000.55 Aug 25th FCS Invite 2019
143 Space Cowboys Win 15-14 952.93 Aug 25th FCS Invite 2019
143 Space Cowboys Win 11-7 1294.83 Aug 25th FCS Invite 2019
29 Brickhouse Loss 10-11 1383.25 Sep 7th North Carolina Mens Club Sectional Championship 2019
63 Turbine Win 11-10 1377.54 Sep 7th North Carolina Mens Club Sectional Championship 2019
99 Bash Bros Loss 13-14 917.71 Sep 7th North Carolina Mens Club Sectional Championship 2019
63 Turbine Loss 8-15 687.73 Sep 8th North Carolina Mens Club Sectional Championship 2019
99 Bash Bros Loss 9-15 527.23 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)