open-data VS sourcegraph

You can check statsbomb open data but you will to preprocess it from json to sql. They have great course and articles about analyzing the data. Another good reading is awasome-football . They provide list of resources to get data. But the most comprehensive and recommended resources eddwebster's guide. He worked for city football group and his repository updated frequently.

I tried visualising Enzo's progressive passes in each of his world cup matches. I used the data available on StatsBomb for this.

https://www.football-data.org/coverage https://datahub.io/collections/football https://github.com/statsbomb/open-data https://www.kaggle.com/datasets/hugomathien/soccer https://www.kaggle.com/datasets/martj42/international-football-results-from-1872-to-2017 https://www.kaggle.com/datasets/secareanualin/football-events https://www.kaggle.com/datasets/adityadesai13/european-football-database-20192020 https://www.kaggle.com/datasets/vivovinco/20212022-football-player-stats https://www.kaggle.com/datasets/antoinekrajnc/soccer-players-statistics

StatsBomb | Multiple roles | REMOTE, or Bath (UK), or Cairo (Egypt)

StatsBomb is a sports analytics startup, covering football (both the soccer and American varieties) and soon basketball. We sell data products as well as analysis tools to sports, media and gambling organisations, with a tech pipeline that includes computer vision, machine learning, stream processing, and web-based dataviz. We count many of the biggest names in football as customers, and your work will have a direct impact on our ability to deliver insights to those customers, driving success on the field.

We're hiring software engineers of various stripes (data pipeline roles with Python and Clojure, full-stack web dev roles with JavaScript) and more besides. We're fully remote, but have offices in Bath, UK and Cairo, Egypt for those that want them. We organise regular team days and also run our own industry-leading conference each year.

- Apply at: https://statsbomb.com/careers

If you'd like to find out more about football analytics:

- Play with our open data: https://github.com/statsbomb/open-data

- Read our articles: https://statsbomb.com/articles/

- Browse our conference videos: https://www.youtube.com/channel/UCmZ2ArreL9muPvH49Gaw0Bw

The data source providing all the passes is from StatBomb

If you're familiar with GitHub and JSON then https://github.com/statsbomb/open-data looks decent.

The company StatsBomb, which specializes in football analytics, has made a lot of their data available for public use here: https://github.com/statsbomb/open-data I’ve been playing with it recently and I’ve found it to be pretty great.

Messi has routinely been one of the best performers in European soccer, including his record-breaking 2011-2012 season in the Spanish league (“La Liga”) with Barcelona, where he set the record for most goals in a season. Unfortunately, success with the Argentina national team has frequently eluded him, finishing as a “runner-up” in the World Cup once and in the Copa America 3 times, before finally winning the Copa America in 2021. Critics often point to his difficulties with his national team as a fatal flaw. I was interested in how his scoring opportunities during arguably his best performance at Barcelona compared to his chances made with Argentina. The data suggests that he is regularly shooting from further away from goal when playing with Argentina when compared to his best performance with Barcelona, which could be a result of a number of factors (different team tactics, difficulty getting up the field, increasing age, less familiarity with teammates, etc.). Data: 2011/2012 La Liga season and World Cup 2018 data were collected from the very nice, public datasets provided by StatsBomb at https://github.com/statsbomb/open-data. The World Cup 2014 data was a bit more difficult to find, but was scraped from the Huffington Post . The StatsBomb data has a ton of great stats to dig into, but because the Huffington Post data had less detail, I wasn't able to go into all of it with just this plot.

I think Statsbomb has a free API you can use on Github if you request access. https://github.com/statsbomb/open-data

Sourcegraph | REMOTE | Full-Time | Machine Learning Engineer, Developer Advocate, Enterprise Product Manager, Technical Advisor | https://sourcegraph.com

Sourcegraph is a code AI platform that makes it easy to read, write, and fix code–even in big, complex codebases.

We are building Cody, an AI coding assistant that uses code search and code intelligence to help devs quickly understand what's happening in code and generate new code that matches the best practices in your codebase. Cody supports AI-enabled autocompletion, fixing bugs, refactoring, test generation, code explanation, and answering high-level questions. You can read Steve Yegge's post on why Cody's code context engine differentiates it from the fast-moving field of AI dev tools: https://about.sourcegraph.com/blog/cheating-is-all-you-need.

Apply here: https://grnh.se/0572f98b4us

That's pretty much what https://sourcegraph.com/ are selling, is it not?

Despite their shitty rug-pull <https://github.com/sourcegraph/sourcegraph/pull/53345>, I do really like Sourcegraph and one doesn't (currently?!) need to be logged in to use it: https://sourcegraph.com/search and they have a handy rewrite pattern such that one can just plug the repo path into the URL for quick searching e.g. https://sourcegraph.com/github.com/JetBrains/intellij-commun...

- https://sourcegraph.com is pivoting and building a copilot application (named Cody). This is pretty good, since sourcegraph is great at understanding your code

While a readable Dockerfile can work as documentation, there are a few caveats:

* the application needs to be designed to work outside containers (so, no hardcoded URLs, ports, or paths). Also, not directly related to containers, but it's nice if it can be easily compiled in most environments and not just on the base image.

* I still need a way to notify me of updates; if the Dockerfile just wgets a binary, this doesn't help me.

* The Dockerfiles need to be easy to find. Sourcegraph's don't seem to be referenced from the documentation, I had to look through their Github repos to find https://github.com/sourcegraph/sourcegraph/tree/main/docker-... (though most are bazel scripts instead of Dockerfiles, but serve the same purpose)

We use Sourcegraph, which is a tool that searches through code in repositories. We leverage this tool in order to understand the adoption curve of our components across all of Reddit. We have a dashboard for each of the platforms to compare the inclusion of RPL components over legacy components. These insights are helpful for us to make informed decisions on how we continue to drive RPL adoption. We love seeing the green line go up and the red line go down!

SourceGraph: https://github.com/sourcegraph/sourcegraph/pulls?q=is%3Apr+a...