If u are a day trader, irrespective of the TF u trade, u should align urself with the trend in 15min TF. Avoiding whats happening in 15min TF, is highly dangerous and will end up as a very costly mistake.

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#piind

3147 to 3270
minor resustnce breakout now https://t.co/mcLJB7yEhO

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We’ve been getting calls and outreach from Queens residents all day about this.

The community’s response? Outrage.


Amazon is a billion-dollar company. The idea that it will receive hundreds of millions of dollars in tax breaks at a time when our subway is crumbling and our communities need MORE investment, not less, is extremely concerning to residents here.

When we talk about bringing jobs to the community, we need to dig deep:
- Has the company promised to hire in the existing community?
- What’s the quality of jobs + how many are promised? Are these jobs low-wage or high wage? Are there benefits? Can people collectively bargain?

Displacement is not community development. Investing in luxury condos is not the same thing as investing in people and families.

Shuffling working class people out of a community does not improve their quality of life.

We need to focus on good healthcare, living wages, affordable rent. Corporations that offer none of those things should be met w/ skepticism.

It’s possible to establish economic partnerships w/ real opportunities for working families, instead of a race-to-the-bottom competition.
Really enjoyed digging into recent innovations in the football analytics industry.

>10 hours of interviews for this w/ a dozen or so of top firms in the game. Really grateful to everyone who gave up time & insights, even those that didnt make final cut 🙇‍♂️ https://t.co/9YOSrl8TdN


For avoidance of doubt, leading tracking analytics firms are now well beyond voronoi diagrams, using more granular measures to assess control and value of space.

This @JaviOnData & @LukeBornn paper from 2018 referenced in the piece demonstrates one method
https://t.co/Hx8XTUMpJ5


Bit of this that I nerded out on the most is "ghosting" — technique used by @counterattack9 & co @stats_insights, among others.

Deep learning models predict how specific players — operating w/in specific setups — will move & execute actions. A paper here: https://t.co/9qrKvJ70EN


So many use-cases:
1/ Quickly & automatically spot situations where opponent's defence is abnormally vulnerable. Drill those to death in training.
2/ Swap target player B in for current player A, and simulate. How does target player strengthen/weaken team? In specific situations?