#16 Grit (5-11)

avg: 1690.14  •  sd: 59.61  •  top 16/20: 41%

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# Opponent Result Game Rating Status Date Event
26 Vengeance Win 15-8 1998.76 Jul 15th TCT Pro Elite Challenge East 2023
7 BENT Loss 11-12 2006.43 Jul 15th TCT Pro Elite Challenge East 2023
30 Tabby Rosa Win 11-9 1633.44 Jul 15th TCT Pro Elite Challenge East 2023
14 Parcha Loss 11-12 1700.67 Jul 16th TCT Pro Elite Challenge East 2023
27 Underground Win 11-9 1675.13 Aug 19th TCT Elite Select Challenge 2023
34 Indy Rogue Win 15-6 1786.34 Aug 19th TCT Elite Select Challenge 2023
13 Nightlock Loss 9-15 1316.04 Aug 19th TCT Elite Select Challenge 2023
23 Flight Win 14-6 2139.4 Aug 20th TCT Elite Select Challenge 2023
12 Nemesis Loss 11-14 1618.13 Aug 20th TCT Elite Select Challenge 2023
14 Parcha Loss 6-11 1278.97 Aug 20th TCT Elite Select Challenge 2023
8 6ixers Loss 10-15 1652.33 Sep 2nd TCT Pro Championships 2023
11 Seattle Riot Loss 8-15 1419.81 Sep 2nd TCT Pro Championships 2023
4 Molly Brown Loss 9-14 1860.16 Sep 2nd TCT Pro Championships 2023
3 Fury** Loss 5-15 1854.65 Ignored Sep 3rd TCT Pro Championships 2023
10 Traffic Loss 10-12 1862.93 Sep 3rd TCT Pro Championships 2023
11 Seattle Riot Loss 10-15 1531.02 Sep 4th TCT Pro Championships 2023
**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)