#160 Duel (4-15)

avg: 225.03  •  sd: 84.27  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
115 Rougaroux Loss 3-8 154.42 Jul 7th Huckfest 2018
165 War Machine Loss 11-12 -21.5 Jul 7th Huckfest 2018
- Austin Amigos Win 12-11 467.14 Jul 7th Huckfest 2018
- Nashvillians Win 11-10 496.51 Jul 7th Huckfest 2018
101 Memphis Belle** Loss 3-13 229.87 Ignored Jul 8th Huckfest 2018
165 War Machine Win 13-10 431.64 Jul 8th Huckfest 2018
- Nashvillians Loss 10-13 43.37 Jul 8th Huckfest 2018
41 Coastal Empire** Loss 3-13 670.34 Ignored Aug 18th Trestlemania III
145 Rampage Win 13-9 854.41 Aug 18th Trestlemania III
102 H.O.G. Ultimate** Loss 3-13 227.51 Ignored Aug 18th Trestlemania III
71 UpRoar** Loss 2-13 423.21 Ignored Aug 18th Trestlemania III
128 Vicious Cycle Loss 6-13 48.1 Aug 19th Trestlemania III
145 Rampage Loss 6-13 -164.15 Aug 19th Trestlemania III
97 Rush Hour** Loss 3-13 251.18 Ignored Sep 8th East Coast Mens Sectional Championship 2018
66 Bullet** Loss 3-13 441.59 Ignored Sep 8th East Coast Mens Sectional Championship 2018
15 Chain Lightning** Loss 2-13 1092.74 Ignored Sep 8th East Coast Mens Sectional Championship 2018
41 Coastal Empire** Loss 2-13 670.34 Ignored Sep 8th East Coast Mens Sectional Championship 2018
61 Tanasi** Loss 2-13 495.92 Ignored Sep 9th East Coast Mens Sectional Championship 2018
133 Holy City Heathens Loss 2-11 6.4 Sep 9th East Coast Mens Sectional 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)