#12 Rival (16-10)

avg: 1913.04  •  sd: 40.27  •  top 16/20: 100%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
28 Wicked Win 13-8 1906.17 Jul 13th TCT Pro Elite Challenge 2019
6 6ixers Loss 8-13 1743.2 Jul 13th TCT Pro Elite Challenge 2019
13 Ozone Win 13-10 2232.71 Jul 13th TCT Pro Elite Challenge 2019
21 Grit Win 13-6 2213.28 Jul 14th TCT Pro Elite Challenge 2019
17 Showdown Win 8-7 1852.24 Jul 14th TCT Pro Elite Challenge 2019
7 Phoenix Loss 9-13 1728.63 Jul 14th TCT Pro Elite Challenge 2019
8 Schwa Loss 9-10 1860.49 Jul 14th TCT Pro Elite Challenge 2019
26 Virginia Rebellion Win 13-2 2047.7 Jul 27th TCT Select Flight Invite East 2019
46 Indy Rogue** Win 13-5 1700.16 Ignored Jul 27th TCT Select Flight Invite East 2019
24 Salty Win 13-4 2063.7 Jul 27th TCT Select Flight Invite East 2019
18 Underground Win 12-10 1934.6 Jul 28th TCT Select Flight Invite East 2019
19 BENT Win 13-2 2293.55 Jul 28th TCT Select Flight Invite East 2019
20 Pop Win 13-8 2129.53 Jul 28th TCT Select Flight Invite East 2019
18 Underground Win 13-9 2115.04 Aug 17th TCT Elite Select Challenge 2019
28 Wicked Win 11-4 2010.01 Aug 17th TCT Elite Select Challenge 2019
19 BENT Loss 12-13 1568.55 Aug 17th TCT Elite Select Challenge 2019
15 Nemesis Win 8-7 1888.98 Aug 18th TCT Elite Select Challenge 2019
13 Ozone Win 10-9 2029.57 Aug 18th TCT Elite Select Challenge 2019
17 Showdown Win 8-7 1852.24 Aug 18th TCT Elite Select Challenge 2019
7 Phoenix Loss 4-9 1547.19 Aug 18th TCT Elite Select Challenge 2019
3 Molly Brown Loss 8-15 1784.62 Aug 31st TCT Pro Championships 2019
5 Scandal Loss 11-14 1941.29 Aug 31st TCT Pro Championships 2019
8 Schwa Loss 11-14 1672.15 Aug 31st TCT Pro Championships 2019
9 Nightlock Win 12-11 2073.3 Sep 1st TCT Pro Championships 2019
2 Seattle Riot Loss 7-11 1946.49 Sep 1st TCT Pro Championships 2019
7 Phoenix Loss 6-14 1547.19 Sep 1st TCT Pro Championships 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)