#17 Lawless (14-8)

avg: 1763.19  •  sd: 63.23  •  top 16/20: 35%

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# Opponent Result Game Rating Status Date Event
77 Bullet Train** Win 15-6 1762.69 Ignored Jul 8th TCT Pro Elite Challenge West 2023
15 Mischief Loss 12-15 1530.39 Jul 8th TCT Pro Elite Challenge West 2023
35 Impact Win 15-13 1731.27 Jul 8th TCT Pro Elite Challenge West 2023
23 Oregon Scorch Win 15-9 2192.24 Jul 9th TCT Pro Elite Challenge West 2023
18 Polar Bears Win 12-10 1999.33 Jul 9th TCT Pro Elite Challenge West 2023
4 BFG Loss 12-13 1834.6 Jul 9th TCT Pro Elite Challenge West 2023
43 Dirty Bird Loss 10-11 1353.32 Aug 19th TCT Elite Select Challenge 2023
1 shame. Loss 11-15 1786.04 Aug 19th TCT Elite Select Challenge 2023
6 Sprocket Loss 13-15 1710.78 Aug 19th TCT Elite Select Challenge 2023
43 Dirty Bird Loss 9-10 1353.32 Aug 20th TCT Elite Select Challenge 2023
13 Slow Loss 10-14 1450.88 Aug 20th TCT Elite Select Challenge 2023
20 Toro Win 15-12 2037.98 Aug 20th TCT Elite Select Challenge 2023
113 Shipwreck Win 12-6 1581.54 Sep 9th 2023 Mixed So Cal Sectional Championship
169 Octonauts** Win 13-3 1331.99 Ignored Sep 9th 2023 Mixed So Cal Sectional Championship
160 Spoiler Alert** Win 13-5 1385.69 Ignored Sep 9th 2023 Mixed So Cal Sectional Championship
152 Family Style** Win 13-5 1395.65 Ignored Sep 9th 2023 Mixed So Cal Sectional Championship
39 Lotus Win 13-10 1823.18 Sep 10th 2023 Mixed So Cal Sectional Championship
113 Shipwreck** Win 15-5 1602.23 Ignored Sep 23rd 2023 Southwest Mixed Regional Championship
26 Sunshine Win 13-8 2143.58 Sep 23rd 2023 Southwest Mixed Regional Championship
33 Tower Win 15-9 2063.85 Sep 23rd 2023 Southwest Mixed Regional Championship
26 Sunshine Win 15-10 2101.02 Sep 24th 2023 Southwest Mixed Regional Championship
15 Mischief Loss 8-15 1266.07 Sep 24th 2023 Southwest Mixed Regional Championship
**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)