#7 Mischief (9-9)

avg: 1927.51  •  sd: 59.08  •  top 16/20: 98.7%

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# Opponent Result Game Rating Status Date Event
11 Lochsa Win 12-6 2463.06 Jul 13th TCT Pro Elite Challenge 2019
1 Drag'n Thrust Loss 11-13 1967.52 Jul 13th TCT Pro Elite Challenge 2019
30 No Touching! Win 14-13 1765.73 Jul 13th TCT Pro Elite Challenge 2019
43 Birdfruit Win 10-7 1907.46 Jul 14th TCT Pro Elite Challenge 2019
18 Columbus Cocktails Win 12-11 1887.6 Jul 14th TCT Pro Elite Challenge 2019
1 Drag'n Thrust Loss 10-12 1958.24 Jul 14th TCT Pro Elite Challenge 2019
8 shame. Loss 12-13 1801.6 Jul 14th TCT Pro Elite Challenge 2019
3 Seattle Mixtape Loss 12-13 1958.72 Aug 2nd 2019 US Open Club Championship
2 AMP Win 15-10 2560.97 Aug 3rd 2019 US Open Club Championship
3 Seattle Mixtape Loss 10-14 1685.01 Aug 3rd 2019 US Open Club Championship
9 Snake Country Loss 10-13 1597.62 Aug 4th 2019 US Open Club Championship
125 Nothing's Great Again** Win 15-5 1652.35 Ignored Aug 17th TCT Elite Select Challenge 2019
18 Columbus Cocktails Win 13-9 2181.17 Aug 17th TCT Elite Select Challenge 2019
15 Loco Loss 10-13 1474.19 Aug 17th TCT Elite Select Challenge 2019
18 Columbus Cocktails Win 9-8 1887.6 Aug 18th TCT Elite Select Challenge 2019
15 Loco Loss 10-11 1677.34 Aug 18th TCT Elite Select Challenge 2019
32 NOISE Win 11-5 2211.8 Aug 18th TCT Elite Select Challenge 2019
5 Wild Card Loss 7-8 1877.74 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)