#21 Public Enemy (16-7)

avg: 1675.65  •  sd: 69.86  •  top 16/20: 62.4%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
29 7 Figures Loss 9-11 1311.17 Jul 7th TCT Pro Elite Challenge 2018
8 Love Tractor Loss 7-13 1321.63 Jul 7th TCT Pro Elite Challenge 2018
7 Blackbird Win 11-10 2016.68 Jul 7th TCT Pro Elite Challenge 2018
36 Steamboat Win 11-7 1972.29 Jul 8th TCT Pro Elite Challenge 2018
31 Metro North Loss 9-10 1431.32 Jul 8th TCT Pro Elite Challenge 2018
13 Birdfruit Win 8-7 1904.29 Jul 8th TCT Pro Elite Challenge 2018
4 BFG Loss 11-12 1854.89 Aug 3rd 2018 US Open Club Championships
2 Seattle Mixtape Win 11-10 2146.63 Aug 3rd 2018 US Open Club Championships
41 Storm Win 15-8 2043.97 Aug 3rd 2018 US Open Club Championships
3 Drag'n Thrust Loss 8-12 1577.07 Aug 4th 2018 US Open Club Championships
36 Steamboat Win 15-14 1630.39 Aug 5th 2018 US Open Club Championships
167 Wildstyle** Win 11-4 1383.29 Ignored Sep 8th Texas Mixed Sectional Championship 2018
- Tlacuaches** Win 11-2 1490.9 Ignored Sep 8th Texas Mixed Sectional Championship 2018
- boom shaka laka** Win 11-1 333.44 Ignored Sep 8th Texas Mixed Sectional Championship 2018
118 Risky Business Win 11-6 1578.1 Sep 8th Texas Mixed Sectional Championship 2018
40 Five One Two Win 10-8 1747.96 Sep 9th Texas Mixed Sectional Championship 2018
64 Sellout Win 11-2 1888.24 Sep 9th Texas Mixed Sectional Championship 2018
49 Cosa Nostra Win 13-7 1929.32 Sep 22nd South Central Mixed Regional Championship 2018
128 Boomtown** Win 13-3 1577.74 Ignored Sep 22nd South Central Mixed Regional Championship 2018
89 Sweet Action Win 13-5 1769.03 Sep 22nd South Central Mixed Regional Championship 2018
43 Flight Club Win 12-11 1598.13 Sep 23rd South Central Mixed Regional Championship 2018
10 shame. Loss 10-15 1391.12 Sep 23rd South Central Mixed Regional Championship 2018
28 Mesteño Loss 7-13 1005 Sep 23rd South Central Mixed Regional Championship 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)