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netball-numbers-challenge's Introduction

Netball Numbers Challenge

Partly inspired by the R Community's Tidy Tuesday, #NetballNumbers provides a forum for netball and data fans an opportunity to flex their analytical muscles, and practice their statistical analysis and data visualisation skills with a range of netball-related datasets.


A new dataset will be regularly posted here and on Twitter - with the goal to explore the dataset, learn something new and/or produce and communicable output (e.g. a data visualisation or table). Importantly, anyone can have a go - data science gurus vs. those just starting out, netball lovers vs. people who have no idea what netball is, Melbourne Vixens fans vs. those who support the wrong team - everyone is welcome here. The goal here is for everyone to apply their skills, get feedback and learn from each others work. In particular, I'm hopeful this challenge encourages those who may have never tried (but have always been interested) to dip their toes into the world of data analysis via a relatable topic.


Here are the basic "rules":

  1. Take the data as it is! You are welcome to explore the dataset however you like, the data is provided as a "toy" dataset to practice various techniques on. Focus on the provided dataset, learning, and improving your analytical skills.
  2. R, Python, Excel, Stata - any approach or methodology is welcome! If you use a code-based approach, try and share this so others can learn from what you've done.
  3. Post your findings or data visualisation on Twitter, by either replying or retweeting the relevant thread associated with the present challenge. Use the #NetballNumbers hashtag so people can find what you've done.
  4. If posting a visual, try and remember to add alt text (alternative text) to the graphic. Twitter provides guidelines for how to add alt text to your images. The DataViz Society/Nightingale has an article on writing good alt text for plots/graphs.

I will also try and post an example with each challenge to hopefully get everyone's brains ticking over, provide some sample code that's been used to analyse the dataset, and keep my own data skills up-to-scratch.

Datasets

Volume Challenge Data Date Year Source Example
1 Results from the Time Machine Sep. 1st-15th 2021 ANZC & SSN Seasonal Goal Difference
2 Ask and You Shall Receive Sep. 20th-Oct. 4th 2021 Deakin CSR Second Phase by Team
3 Something About Riddles and Fruit TBC 2021 Twitter

Upcoming Challenges

  • Have You Seen Colin?
  • C'mon Jump in my Car
  • In the Zone
  • Super Powers
  • The People's Diamonds

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