#20 Pop (10-7)

avg: 1633.37  •  sd: 83.01  •  top 16/20: 7%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
30 Colorado Small Batch Loss 9-13 979.14 Jul 13th TCT Pro Elite Challenge 2019
9 Nightlock Loss 9-13 1529.74 Jul 13th TCT Pro Elite Challenge 2019
8 Schwa Loss 10-12 1747.37 Jul 13th TCT Pro Elite Challenge 2019
28 Wicked Win 12-9 1755.38 Jul 14th TCT Pro Elite Challenge 2019
21 Grit Loss 4-9 1013.28 Jul 14th TCT Pro Elite Challenge 2019
33 Heist Win 9-8 1496.72 Jul 14th TCT Pro Elite Challenge 2019
30 Colorado Small Batch Win 13-7 1955.24 Jul 27th TCT Select Flight Invite East 2019
66 Hot Metal** Win 13-5 1319.64 Ignored Jul 27th TCT Select Flight Invite East 2019
39 Stella Win 13-7 1769.44 Jul 27th TCT Select Flight Invite East 2019
26 Virginia Rebellion Win 13-7 2005.23 Jul 28th TCT Select Flight Invite East 2019
22 Tabby Rosa Win 12-11 1682.69 Jul 28th TCT Select Flight Invite East 2019
12 Rival Loss 8-13 1416.88 Jul 28th TCT Select Flight Invite East 2019
27 Elevate Loss 11-12 1308.54 Aug 17th TCT Elite Select Challenge 2019
15 Nemesis Loss 9-11 1514.78 Aug 17th TCT Elite Select Challenge 2019
11 Siege Win 14-9 2387.56 Aug 17th TCT Elite Select Challenge 2019
55 Dish** Win 12-3 1475.68 Ignored Aug 18th TCT Elite Select Challenge 2019
33 Heist Win 9-7 1651.06 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)