#16 Chain Lightning (6-7)

avg: 1836.97  •  sd: 92.45  •  top 16/20: 67.1%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
5 Revolver Loss 12-13 1984.63 Jul 13th TCT Pro Elite Challenge 2019
7 Chicago Machine Win 13-7 2566.51 Jul 13th TCT Pro Elite Challenge 2019
19 Voodoo Win 13-7 2293.09 Jul 13th TCT Pro Elite Challenge 2019
8 GOAT Loss 8-13 1501.06 Jul 14th TCT Pro Elite Challenge 2019
9 SoCal Condors Loss 9-13 1551.82 Jul 14th TCT Pro Elite Challenge 2019
18 Patrol Loss 8-13 1242.16 Jul 14th TCT Pro Elite Challenge 2019
57 Red Circus Win 15-7 1882.69 Aug 17th TCT Elite Select Challenge 2019
32 Prairie Fire Win 15-7 2114.11 Aug 17th TCT Elite Select Challenge 2019
9 SoCal Condors Loss 14-16 1762.09 Aug 17th TCT Elite Select Challenge 2019
20 Yogosbo Win 12-11 1858.03 Aug 18th TCT Elite Select Challenge 2019
11 Johnny Bravo Loss 9-10 1773.7 Aug 18th TCT Elite Select Challenge 2019
10 DiG Loss 9-11 1717.84 Aug 18th TCT Elite Select Challenge 2019
30 Black Market I Win 11-10 1660.38 Aug 18th TCT Elite Select Challenge 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)