#44 El Niño (21-5)

avg: 1245  •  sd: 73.91  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
66 Bullet Loss 10-13 713.44 Jun 16th ATL Classic 2018
71 UpRoar Win 13-8 1519.37 Jun 16th ATL Classic 2018
145 Rampage Win 13-8 932.01 Jun 16th ATL Classic 2018
165 War Machine** Win 13-4 703.5 Ignored Jun 16th ATL Classic 2018
102 H.O.G. Ultimate Win 12-9 1172.88 Jun 17th ATL Classic 2018
55 Ironmen Loss 10-12 914.1 Jun 17th ATL Classic 2018
75 Omen Win 11-9 1241.95 Jun 17th ATL Classic 2018
140 ScooberDivers** Win 11-4 1123.04 Ignored Jul 7th Swan Boat 2018
133 Holy City Heathens Win 8-7 731.4 Jul 7th Swan Boat 2018
128 Vicious Cycle Win 12-9 993.46 Jul 7th Swan Boat 2018
- Shore Break** Win 13-4 736.57 Ignored Jul 7th Swan Boat 2018
168 Tyranny** Win 15-1 660.32 Ignored Jul 8th Swan Boat 2018
128 Vicious Cycle Win 15-7 1248.1 Jul 8th Swan Boat 2018
75 Omen Win 15-10 1446.35 Jul 8th Swan Boat 2018
71 UpRoar Win 11-6 1569.91 Sep 8th Florida Mens Sectional Championship 2018
- GrandMaster Flash** Win 11-3 723.1 Ignored Sep 8th Florida Mens Sectional Championship 2018
128 Vicious Cycle Win 11-5 1248.1 Sep 8th Florida Mens Sectional Championship 2018
168 Tyranny** Win 11-0 660.32 Ignored Sep 8th Florida Mens Sectional Championship 2018
108 Swamp Horse Win 15-9 1286.94 Sep 9th Florida Mens Sectional Championship 2018
71 UpRoar Win 13-10 1351.36 Sep 9th Florida Mens Sectional Championship 2018
23 Freaks Loss 10-13 1172.97 Sep 22nd Southeast Mens Regional Championship 2018
37 Brickhouse Loss 14-15 1163.21 Sep 22nd Southeast Mens Regional Championship 2018
102 H.O.G. Ultimate Win 12-8 1268.67 Sep 22nd Southeast Mens Regional Championship 2018
41 Coastal Empire Win 11-10 1395.34 Sep 22nd Southeast Mens Regional Championship 2018
34 Lost Boys Win 13-10 1637.33 Sep 23rd Southeast Mens Regional Championship 2018
37 Brickhouse Loss 11-12 1163.21 Sep 23rd 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)