Sync NBA team data from stats.nba.com

Convert raw NBA JSON to CSV.

This example converts raw NBA JSON game logs into a single CSV file, first showing the transformation of nested statistics into flat, queryable rows. It reads JSON files for each season and measure type, extracting headers and parameters to build team-level records with prefixed column names like `pace_*` or `def_rating_*`. The script uses the CoffeeScript language and the Node.js `fs` and `csv` libraries, with the `csv().to.path` API to write the output. It also merges team abbreviations from a separate CSV, keying each row by team ID and season.

70% match

Generate graphs from NBA data

The visualization generates two bar charts from NBA team statistics, each plotting point differentials against a baseline. The first chart displays each team’s overall point spread, while the second groups the same differentials into “Above 500” and “Below 500” categories for comparison. Using pandas for data manipulation and matplotlib for plotting, the code reads CSV files, sorts teams by differential, and renders bar charts with custom colors and axis labels. The output is saved as PNG images at 150 DPI, with data sourced from stats.nba.com.

63% match

Gist c42ddd2ab41e80bdaa7a1b67f55ac0d4

This visualization uses a timeline of NBA coach tenures, showing each coach’s consecutive seasons as a single continuous stint. The underlying data is parsed from a CSV file, grouping season-by-season records into multi-year stints by merging adjacent years for the same coach. The Ruby script sorts the stints chronologically and prints them as text, relying on the CSV library for input and standard array/hash operations to detect continuity. The output itself is a plain list of stints, with no graphical rendering; the visualization would require a front-end library like D3.js to draw the timeline, but the provided code only performs data preprocessing and formatting.

58% match

Get above/below .500 point differential for NBA teams.

This example visualizes how NBA teams perform against opponents above and below the .500 win-percentage threshold, ranking teams by their point differential against sub-.500 teams across each season. The Ruby script queries a PostgreSQL database of game and team statistics, then computes average scoring margins for games versus winning and losing opponents, sorting teams by the spread between those two margins. It outputs a terminal table via the `terminal-table` gem and writes the same ranked data to CSV files. The code relies on raw SQL through the `pg` API to join game results, demonstrating a straightforward data-processing pipeline rather than interactive visualization.

57% match

Gist 2d7133b7a34afa5c0ddb698237987830

This example visualizes NHL team performance metrics from a JSON dataset of game statistics, plotting each team's goals for and against across multiple games. It uses D3.js to render a scatterplot with team abbreviations as labels, positioning points based on goalsFor and goalsAgainst values while encoding win/loss records through point color. The visualization leverages d3.scalePoint for axes, d3.forceSimulation for label collision avoidance, and Canvas rendering to display all team data points from the standings.json file.

MMbrownshoes

NBA All-Stars from 2000-2016

This example prints a plain-text summary of a dataset listing NBA All-Stars from 2000 to 2016. It displays the row count, column count, and file size in kilobytes, followed by the first ten rows of data as formatted JSON. Built with D3 v5, the visualization uses the `select`, `csv`, and `csvFormat` functions to fetch and process the CSV file from an external URL, then appends the results to the page via `body.append`. The data originates from basketball.realgm.com, as credited in the README.

Ddielaenge

Loading Boston Celtics Shot Log Data from 2014-2015 Season

This example loads Boston Celtics shot log data from the 2014-2015 NBA season and displays the raw parsed dataset as a JSON-formatted text block. It uses `d3.csvParse` to convert a CSV file fetched from a GitHub gist, then coerces quantitative fields like `SHOT_DIST`, `PTS`, and `CLOSE_DEF_DIST` to numbers before rendering them. The visualization demonstrates a simple data-loading pipeline with `fetch`, `select`, and `join`, showing either a loading state or the full array of shot records.

MMatthew Dzwil

Gist fd36310058f0b9965318

The graphic displays the score differential for every NBA game in 2015, charting the home team’s lead (positive) or deficit (negative) at each minute of play. Each team is shown in its own small multiples panel, with colored circles representing each game’s trajectory and a line highlighting a selected match. Built with `d3.js`, `d3-jetpack`, and `d3-starterkit`, the visualization uses a threshold color scale to encode the point difference, and interactive hover effects reveal game details such as date, opponent, and final score. The data originates from a JSON file containing minute-by-minute scores for games on a specific date.

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