#4 Chain Lightning (18-7)

avg: 2211.89  •  sd: 48.22  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
10 Rhino Slam! Loss 13-14 1960.8 Jul 8th TCT Pro Elite Challenge West 2023
29 Mallard Win 15-8 2293.07 Jul 8th TCT Pro Elite Challenge West 2023
22 SoCal Condors Win 14-11 2165.74 Jul 8th TCT Pro Elite Challenge West 2023
15 GOAT Loss 11-14 1658.94 Jul 9th TCT Pro Elite Challenge West 2023
14 Sockeye Loss 10-13 1671.73 Jul 9th TCT Pro Elite Challenge West 2023
29 Mallard Win 15-7 2328.26 Jul 9th TCT Pro Elite Challenge West 2023
20 Zyzzyva Win 14-10 2258.91 Aug 19th TCT Elite Select Challenge 2023
25 Mad Men Win 15-6 2357.27 Aug 19th TCT Elite Select Challenge 2023
12 Raleigh-Durham United Win 13-11 2235.46 Aug 19th TCT Elite Select Challenge 2023
7 DiG Loss 12-13 2042.51 Aug 20th TCT Elite Select Challenge 2023
22 SoCal Condors Win 15-10 2306.01 Aug 20th TCT Elite Select Challenge 2023
27 Omen Win 15-7 2339.79 Aug 20th TCT Elite Select Challenge 2023
13 Vault Win 15-12 2304.85 Sep 2nd TCT Pro Championships 2023
2 PoNY Win 13-12 2458.94 Sep 2nd TCT Pro Championships 2023
8 Johnny Bravo Win 14-13 2237.77 Sep 2nd TCT Pro Championships 2023
3 Revolver Loss 10-15 1791.69 Sep 3rd TCT Pro Championships 2023
3 Revolver Win 15-14 2370.3 Sep 3rd TCT Pro Championships 2023
5 Chicago Machine Win 14-12 2410.66 Sep 3rd TCT Pro Championships 2023
1 Truck Stop Loss 14-15 2374.23 Sep 4th TCT Pro Championships 2023
89 Second Nature** Win 13-4 1899.6 Ignored Sep 23rd 2023 Southeast Mens Regional Championship
30 Delirium Win 13-3 2278.68 Sep 23rd 2023 Southeast Mens Regional Championship
12 Raleigh-Durham United Win 13-7 2564.16 Sep 23rd 2023 Southeast Mens Regional Championship
28 Tanasi Win 15-7 2332.45 Sep 23rd 2023 Southeast Mens Regional Championship
6 Ring of Fire Loss 13-15 1966.48 Sep 24th 2023 Southeast Mens Regional Championship
30 Delirium Win 15-12 1979.17 Sep 24th 2023 Southeast Mens Regional Championship
**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)