How to automate our python scripts to run on a schedule; taking arguments / parameters from a shell input, time zone tricks.
Do you want to see more from your data?
Breaking down the shot profile data for each player and visualising it to quickly identify and compare players' roles within a team context
How to visualise the shot distance profiles for each team. How do we optimise these visualisations to reveal each team's strategic DNA?
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.
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.
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.
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)
Are assists good? In this article, I assess the link between NBA assists and scoring efficiency.
A look at the players whose presence most help their teammates' offence (or... not).
Where shots come from in modern basketball, and why - in visuals.
Catching up with the advanced analytics revolution
Starting to analyse NBA play-by-play shot location data by distances to reveal insights into teams' offensive tendencies and edges.
A little bit about my Python setup, including hardware, IDE, package and python version management, as well as preferred deployment destinations.
How to get and parse the NBA API play by play data, and add player on/off data
Producing visualisations of NBA team performances. How to grab box score data from the NBA API, analysing & visualising them to capture their overall performances & recent form.
A look at teams' (in)consistency over this season. Which teams have been hot/cold recently? Which teams have been the most/least consistent?
A look at a distributions of players' minutes, and thinking about metrics for consistency. We find some interesting outliers in that data too!