#31 Heist (7-17)

avg: 1307.56  •  sd: 62.54  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
14 Nemesis Loss 8-13 1278.36 Jul 13th TCT Pro Elite Challenge 2019
4 Molly Brown** Loss 2-13 1680.55 Ignored Jul 13th TCT Pro Elite Challenge 2019
17 Showdown Loss 8-11 1314.6 Jul 13th TCT Pro Elite Challenge 2019
11 Wildfire Loss 9-10 1742 Jul 14th TCT Pro Elite Challenge 2019
16 Pop Loss 8-9 1555.7 Jul 14th TCT Pro Elite Challenge 2019
10 Nightlock Loss 6-13 1357.64 Jul 14th TCT Pro Elite Challenge 2019
24 Elevate Loss 14-15 1339.37 Jul 27th TCT Select Flight Invite East 2019
32 FAB Win 12-8 1733.05 Jul 27th TCT Select Flight Invite East 2019
12 Siege Loss 6-13 1250.11 Jul 27th TCT Select Flight Invite East 2019
21 BENT Loss 4-13 985.97 Jul 27th TCT Select Flight Invite East 2019
37 Stella Win 13-7 1761.89 Jul 28th TCT Select Flight Invite East 2019
28 Colorado Small Batch Win 12-10 1633.69 Jul 28th TCT Select Flight Invite East 2019
20 Grit Loss 11-13 1379.21 Aug 17th TCT Elite Select Challenge 2019
7 Phoenix** Loss 4-15 1451.86 Ignored Aug 17th TCT Elite Select Challenge 2019
22 LOL Loss 9-13 1110.59 Aug 17th TCT Elite Select Challenge 2019
27 Wicked Loss 4-9 801.32 Aug 18th TCT Elite Select Challenge 2019
16 Pop Loss 7-9 1401.37 Aug 18th TCT Elite Select Challenge 2019
16 Pop Loss 8-12 1239.55 Sep 21st North Central Club Womens Regional Championship 2019
84 Cold Cuts** Win 12-3 900.76 Ignored Sep 21st North Central Club Womens Regional Championship 2019
81 Lady Forward Win 13-6 933.07 Sep 21st North Central Club Womens Regional Championship 2019
55 Crackle Win 9-5 1397.1 Sep 21st North Central Club Womens Regional Championship 2019
27 Wicked Loss 13-14 1276.32 Sep 22nd North Central Club Womens Regional Championship 2019
33 Fusion Loss 9-13 863.44 Sep 22nd North Central Club Womens Regional Championship 2019
52 Stellar Win 15-6 1559.5 Sep 22nd North Central Club Womens Regional Championship 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)