11. Hello, computer
How to automate our python scripts to run on a schedule; taking arguments / parameters from a shell input, time zone tricks.
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8. Seeing the trees in the forest
Breaking down the shot profile data for each player and visualising it to quickly identify and compare players' roles within a team context
6. Shot Blots
How to visualise the shot distance profiles for each team. How do we optimise these visualisations to reveal each team's strategic DNA?
Introducing: The Extra Pass
The Extra Pass will share the process and code as I build a data project with Python. It is for those interested in an inside look at how a data analysis or a Python programming project is done. Think of it as virtual at-work shadowing.
Evolution of the NBA offence by geography
Visualising the dramatic changes to the NBA offences over the last 15 years to see how it has changed and where it might be going.
What does data tell us about ageing in the modern NBA?
A data-driven look at how ageing affects the modern NBA. How old are the league's best players? Are modern players getting younger? Who's getting playing time? With visuals.
Interactive basketball data visualizations with Plotly
Analyze sports data with hexbin shot charts and bubble charts with Plotly and Plotly Express (source code & my own data for all 30 teams included in my GitLab repo)
The link between NBA assists and scoring efficiency
Are assists good? In this article, I assess the link between NBA assists and scoring efficiency.
NBA's Catalysts - players powering teammates' offences
A look at the players whose presence most help their teammates' offence (or... not).
Visualising basketball shots in 2020 - the (big) fundamentals.
Where shots come from in modern basketball, and why - in visuals.
NBA shot data analytics & visualization with Python, Pandas and Matplotlib: Part 1 – The basics
Catching up with the advanced analytics revolution
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11. Hello, computer
How to automate our python scripts to run on a schedule; taking arguments / parameters from a shell input, time zone tricks.
10. Game on a viz
We move onto producing a summary chart capable of telling the story of each game for the teams as well as players involved.
9. Game-on, game-off
We produce game-specific and matchup-specific shot blot charts to show how the playoffs and matchup has been affecting each team.
8. Seeing the trees in the forest
Breaking down the shot profile data for each player and visualising it to quickly identify and compare players' roles within a team context
7. Standard standouts
Once we have the game data, we need to identify the "standout" games. How might we do that?
6. Shot Blots
How to visualise the shot distance profiles for each team. How do we optimise these visualisations to reveal each team's strategic DNA?
