#4 Ring of Fire (16-5)

avg: 1993.74  •  sd: 70.71  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
7 Chicago Machine Win 14-12 2065.89 Aug 3rd 2018 US Open Club Championships
13 Johnny Bravo Win 14-10 2175.16 Aug 3rd 2018 US Open Club Championships
8 Sub Zero Win 12-9 2175.14 Aug 4th 2018 US Open Club Championships
3 PoNY Loss 13-15 1817.12 Aug 4th 2018 US Open Club Championships
1 Revolver Loss 13-15 1865.47 Aug 5th 2018 US Open Club Championships
20 Patrol Win 15-11 1921.87 Sep 1st TCT Pro Championships 2018
7 Chicago Machine Win 15-6 2444.93 Sep 1st TCT Pro Championships 2018
13 Johnny Bravo Win 15-13 1990.64 Sep 1st TCT Pro Championships 2018
3 PoNY Loss 14-15 1906.3 Sep 2nd TCT Pro Championships 2018
11 DiG Loss 8-15 1232.44 Sep 2nd TCT Pro Championships 2018
13 Johnny Bravo Win 15-9 2291.94 Sep 2nd TCT Pro Championships 2018
55 Ironmen** Win 13-4 1752.22 Ignored Sep 22nd Southeast Mens Regional Championship 2018
97 Rush Hour** Win 13-2 1451.18 Ignored Sep 22nd Southeast Mens Regional Championship 2018
66 Bullet** Win 13-3 1641.59 Ignored Sep 22nd Southeast Mens Regional Championship 2018
37 Brickhouse Win 13-6 1888.21 Sep 22nd Southeast Mens Regional Championship 2018
15 Chain Lightning Win 15-11 2073.9 Sep 23rd Southeast Mens Regional Championship 2018
7 Chicago Machine Win 15-14 1969.93 Oct 18th USA Ultimate National Championships 2018
15 Chain Lightning Win 15-13 1906.92 Oct 18th USA Ultimate National Championships 2018
6 Furious George Win 13-12 1994.11 Oct 18th USA Ultimate National Championships 2018
10 Doublewide Win 14-10 2211.18 Oct 19th USA Ultimate National Championships 2018
1 Revolver Loss 14-15 1954.65 Oct 20th USA Ultimate National Championships 2018
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)