#4 Ring of Fire (6-4)

avg: 2165.85  •  sd: 99.13  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
3 PoNY Win 14-10 2566.72 Aug 2nd 2019 US Open Club Championship
2 Truck Stop Loss 7-15 1569.57 Aug 2nd 2019 US Open Club Championship
1 Sockeye Loss 11-14 1979.51 Aug 3rd 2019 US Open Club Championship
5 Revolver Win 15-11 2490.8 Aug 3rd 2019 US Open Club Championship
2 Truck Stop Loss 12-15 1869.08 Aug 4th 2019 US Open Club Championship
6 Sub Zero Win 15-7 2663.25 Aug 31st TCT Pro Championships 2019
14 Doublewide Win 14-11 2185.63 Aug 31st TCT Pro Championships 2019
12 Pittsburgh Temper Win 13-8 2386.01 Aug 31st TCT Pro Championships 2019
7 Chicago Machine Loss 12-14 1788.02 Sep 1st TCT Pro Championships 2019
10 DiG Win 15-14 2092.04 Sep 1st TCT Pro Championships 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)