Comments URL: https://news.ycombinator.com/item?id=45628564

Points: 2

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=31175200

Points: 2

# Comments: 0

I agree 100% here. As a SQL veteran, it would make the transition a lot easier if you used common SQL keywords like group by, order by, limit, etc. e.g.

    from employees
    where country = "USA"
    derive [
      gross_salary: salary + payroll_tax,
      gross_cost:   gross_salary + benefits_cost
    ]           
    where gross_cost > 0
    group by:[title, country] [
        average salary,
        sum     salary,
        average gross_salary,
        sum     gross_salary,
        average gross_cost,
        sum_gross_cost: sum gross_cost,
        count,
    ]
    order by:sum_gross_cost
    where count > 200
    limit 20

“If you look at each one and how they’ve occurred, many have come from “dangerous areas” on the pitch. At AS Monaco over the last season, we looked at where and how goals were most often scored and key areas to shoot/cross from to apply in our game model.”

With teams applying more analytics to soccer there are more cases of high goal probability crosses and defenders’ attempts to clear these are often futile. A great example was England’s equalizer yesterday that technically was an OG but realistically the defender had no chance to clear it and Sterling was right there to tap it in.

I’ve done my share of experiments with ML/AI and where I’ve seen the most interesting value has been NLP applications (such as categorizing customer comments or assigning categories to products based in description) and finding “factors that influence behavior x” which then can be turned into either a model or a few simple rules.

I’d say go with something like Matomo (formerly Piwik) https://matomo.org. If you wanted to build your own, I’d suggest keeping it simple. Look at Matomo’s architecture and replicate https://github.com/matomo-org/matomo.