#41 Coastal Empire (14-6)

avg: 1270.34  •  sd: 64.54  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
98 Southern Hospitality Win 13-10 1177.19 Jul 21st Club Terminus 2018
23 Freaks Loss 11-12 1376.12 Jul 21st Club Terminus 2018
107 BaNC Win 12-8 1216.79 Jul 22nd Club Terminus 2018
15 Chain Lightning Loss 7-13 1135.21 Jul 22nd Club Terminus 2018
61 Tanasi Win 11-8 1461.53 Jul 22nd Club Terminus 2018
160 Duel** Win 13-3 825.03 Ignored Aug 18th Trestlemania III
71 UpRoar Win 13-9 1441.78 Aug 18th Trestlemania III
145 Rampage** Win 13-2 1035.85 Ignored Aug 18th Trestlemania III
102 H.O.G. Ultimate Win 13-4 1427.51 Aug 18th Trestlemania III
71 UpRoar Win 13-12 1148.21 Aug 19th Trestlemania III
102 H.O.G. Ultimate Win 13-9 1246.08 Aug 19th Trestlemania III
97 Rush Hour Win 12-7 1371.69 Sep 8th East Coast Mens Sectional Championship 2018
66 Bullet Win 13-7 1599.12 Sep 8th East Coast Mens Sectional Championship 2018
15 Chain Lightning Loss 8-12 1251.59 Sep 8th East Coast Mens Sectional Championship 2018
160 Duel** Win 13-2 825.03 Ignored Sep 8th East Coast Mens Sectional Championship 2018
23 Freaks Loss 7-13 943.58 Sep 9th East Coast Mens Sectional Championship 2018
44 El Niño Loss 10-11 1120 Sep 22nd Southeast Mens Regional Championship 2018
27 Turbine Win 12-11 1517.19 Sep 22nd Southeast Mens Regional Championship 2018
71 UpRoar Loss 13-14 898.21 Sep 22nd Southeast Mens Regional Championship 2018
75 Omen Win 13-10 1320.88 Sep 22nd 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)