GithubHelp home page GithubHelp logo

nflfastr-python-tutorial's Introduction

nflfastR-Python-Tutorial

nflfastR Python

click any icon to visit its respective page!

CLICK HERE to visit the tutorial!

About this tutorial

This tutorial goes over how to work with nflfastR data in Python and concludes four parts:

  • Part 1 - Importing Packages and Scraping the Data

    • Overview of the core python data science packages
    • How to scrape nflfastR data
  • Part 2 - Getting to Know the Data

    • Basic pandas methods
    • Data cleaning
  • Part 3 - Basic Data Manipulation

    • Subsetting/querying overview
    • Using the data to compute important fantasy metrics
      • Air yards data
      • Redzone targets
      • Carries inside the 5
    • Calculating fantasy points for every play along with player totals
    • One-Hot Encoding
  • Part 4 - Data Viz! ๐Ÿ“Š

    • Basic viz with fantasy relevant data
    • Advanced viz using a function to show air yards distributions for any player

Resources used

Python pandas matplotlib Seaborn numpy Colab nflfastR

Special thank yous

nflfastR OSF benbaldwin mrcaseb deryck


Click here to visit the nflfastR documentation

Thanks for checking out the tutorial, I hope you enjoy!

By: Max Bolger @mnpykings, 2020

pykings

nflfastr-python-tutorial's People

Contributors

maxbolger avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.