Where to go? – Resource List For Sport Science Data

So I decided to take a break from our journey building our data analysis system to provide some resources or methods I have found really helpful with my own learning along the way.

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First up and this is a pretty big one: Google. Although it took me longer than it should have to realise this, more often than not a solution to your data problem is a Google search away, the alternative can be many, many failed frustrating efforts. Have faith you are not the first person to run into this problem, someone has asked the question previously and a kind soul has answered it.

Secondly, for those using Excel, the internet has lots of Excel based forums you can ask specific questions based around your own data where people jump at the chance to answer them. MrExcel being a frequent haunt of mine back in the early days

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For more in-depth questions, possibly a DAX based one in PowerPivot, a VBA based one or maybe you’ve gone down the R/Python route then Stack Overflow can be very helpful. However be warned, if you don’t meet the criteria they have set for asking a question or searched  thoroughly to see if it has been asked before, it won’t be long before someone ignores your question and chastises you for not asking it correctly 😉

R has a very active community on twitter, where the hashtag #Rstats is popular and questions around how to use it can often be answered quickly.

There are some general sources of information, here are some specific sources (in no particular order):

  • Adam Sullivan
    • Very useful blog on monitoring and planning training load in Excel plus has some awesome free Excel templates
  • Mladen Jovanovic (Complementary Training)
    • Really useful blog covering Excel, DAX, R and many other aspects of data analytics in sport
  • Mathieu Lacome N
    • New blog however Mathieu’s work is of high quality and looks like he will cover a lot around analytics in sport. R based.
  • Github
    • For the heavier analytics, Github contains lots of useful examples to draw
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      inspiration from.
      • Kaggle
        • Provides similar examples to Github but specifically based around machine learning
        • Also nice setup to practise and coding/scripting on
          Kaggle_logo
  • Excel Tricks For Sports
    • Vast catalogue of YouTube videos covering almost every aspect of what you would want to do through Excel for Sport
  • SuperDataScience
    • Paywalled Site (Option for 1st month for $1 however)
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    • Catalogue of courses to learn data analytics incl: R, Python, SQL, Machine Learning, Data Science, Excel (Query, PowerPivot, DAX). (NOT SPORT BASED)
  • Datacamp
    • Useful site to learn about almost any data-based language including R, Python and analytics within those platforms (Paywalled but does offer free samples & some free courses)
      datacamp-sq

From my own perspective, learning any new type of analysis or system (Excel, R, Python, Tableau etc.) I have found it much better to have an idea of what you want to be able to do before beginning the journey than trying to learn it without an end goal in mind. Know what you want to paint before buying your paint!

Hopefully the above provides both insight into where you can go with analytics in sport as well and help kickstart your journey into analytics in sport! I will continue to update this blog as more resources come to mind!

PS – I get asked about books, I have books on some of the areas covered on this blog but 99% of issues I run into are solved by looking online or speaking to someone about it. It’s rare I look in a book for a solution these days so I have purposely left them out here.

Edit: 30/01/2019

Credit to @allison_horst who made the above image

I wanted to add some resources I have come across lately that are R specific but definitely of high value!

  • Rstudio::conf
    • Rstudio runs an annual conference and puts the videos from it online. These cover a wide range of topics so there is something for beginners upto advanced users
  • community.rstudio.com
    • As mentioned above, stackoverflow can be intimidating at times. RStudio tries to provide it’s own alternative
  • rfordatasci
    • The background behind this site was covered by a recent talk. It started as someone’s own way to learn R and grew into a platform for anyone looking to learn R
  • DataVizBook
    • I know I said I don’t use books but this has a linked repo on GitHub which lets you look at all the code involved
  • Hadley Wickham
    • Hadley is one of the many online R gurus but also is the Chief Scientist with RStudio.
    • His site has many links across all sorts of topics
  • data.table workshop materials
    • The data.table package in R is really useful if dealing with large datasets
    • Linked is a nice workshop on introducing the package
  • University course on Data Science for Economists
    • Although advanced, covers wide range of topics
  • Integrating GitHub into your workflow for version control
    • Admittedly a new concept to me, version control lets you alter your work while retaining a log of these changes to allow you to revert back if needed

I will look to add to the above over time as I come across new resources.

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