Shooting Data Joins a Centralized Platform for Basketball Analytics in Spain

Basketball has become one of the most data-hungry sports on the planet, and a new study published in Multimedia Tools and Applications shows how the Spanish professional league is catching up with the analytics revolution. Guillermo Vinué, an independent researcher based in Valencia, has completed the final piece of a three-part research project devoted to improving basketball data management in Spain. The work focuses on the ACB, the country’s top male professional competition, and tackles a source of information that had remained untouched in his earlier papers: shooting data. By capturing exactly where every shot is taken, what type of shot it is, and whether it goes in, the research opens the door to a far richer picture of player performance than traditional box score statistics can provide.

The new paper is the third and last part of what the author describes as a research triptych. The first two installments dealt with box score data and play-by-play data respectively, each feeding into a growing analytical infrastructure for Spanish basketball. Box scores capture cumulative statistics such as points, rebounds, and assists, while play-by-play records describe the sequence of events during a game. Shooting data, however, adds a crucial spatial dimension. For each attempt, the dataset records the location of the shot on the court, the shot type, and the outcome. This is precisely the kind of information that has transformed how analysts, coaches, and fans understand the modern game, from the rise of the three-point shot to the strategic value of shots near the rim.

To build the dataset, Vinué developed code in the R programming language that automatically collects shooting information from the official ACB website using a web scraping procedure. Web scraping involves programmatically extracting data from web pages, and the author designed the procedure to be friendly and reproducible. The approach draws on established R tools for harvesting web content, including packages such as rvest and RSelenium, and respects the target website’s robots.txt file, which specifies rules for automated access. Once collected, the raw data required ordinary cleaning to be structured into a standard format suitable for analysis. The author then validated the accuracy of the scraped data by plotting the shots and comparing their coordinates and outcomes against the shot charts displayed on the source website, ensuring that the automated pipeline faithfully reproduces the original information.

The scale of the resulting dataset is substantial. It covers the 2024-2025 ACB season and includes 39,550 shots drawn from 306 games and 293 players. Each record contains, among other details, the location of the attempted shot, the shot type, and whether the attempt was successful. A dataset of this size allows for detailed shooting profiles of individual players, showing where on the floor they prefer to shoot and how effective they are from each area. Such profiles go far beyond a simple field goal percentage, which collapses all attempts into a single number regardless of where they were taken. Spatially aware statistics have become central to basketball analytics worldwide, and this work brings that capability to Spanish league data in a systematic, reproducible way.

A key contribution of the study is the integration of shooting data into a previously developed data platform, so that all analyses are centralized in a single place. The platform, described in an earlier paper, already handled box score and play-by-play information; adding shooting data completes the triptych and turns the platform into a comprehensive resource for Spanish professional basketball. Alongside the platform, the author updated BAwiR, an accompanying R package that is freely available from CRAN, the comprehensive archive network for R software. The package includes documentation for all its files as well as three vignettes that guide users through its functionality. Making both the data and the code openly available means that other researchers, analysts, and enthusiasts can reproduce the results and extend the analysis to their own questions.

To make the data accessible to non-programmers, the study also provides a web application for easy interaction. Built with Shiny, a popular framework for turning R code into interactive web tools, the application lets users explore shooting data without writing any code themselves. Interactive visualization has become an essential bridge between raw data and decision-makers in sport, allowing coaches and analysts to query specific players, games, or shot types on demand. The author also gathered feedback from users through a questionnaire, reflecting a growing recognition in the visualization research community that sports tools should be designed in consultation with the people who will actually use them.

One of the more technically interesting parts of the work concerns how to divide the basketball court into meaningful zones. Classical zones such as the paint, the mid-range areas, and the corners are usually defined manually, but the paper explores whether automatic clustering algorithms can do the job instead. Three methods were tested on the shooting dataset: DBSCAN, HDBSCAN, and k-means. DBSCAN, which stands for Density-Based Spatial Clustering of Applications with Noise, groups points that lie close together in space and marks isolated points as noise, but it requires the user to specify a neighborhood radius and a minimum number of points in advance. HDBSCAN extends this idea by building a hierarchy of clusters of varying density, reducing the need to tune the radius parameter. K-means, the simplest of the three, iteratively assigns each shot to its nearest cluster center and then recalculates those centers as the average of the assigned points, with the number of clusters chosen beforehand, often through silhouette analysis.

The results of the clustering comparison were instructive. K-means produced the closest representation of the classical basketball regions, identifying six zones, although it did not distinguish between two-point and three-point shots, and some regions contained both. HDBSCAN roughly identified some areas, such as the paint and the left and right three-point zones, but its other regions were messy. DBSCAN fared worst, returning just two large regions that carry little basketball meaning. When the analysis was refined by filtering first by shot type, k-means divided the two-point area into three regions approximating the paint and the left and right mid-ranges, while the three-point area split into six clearly identifiable regions: left and right corners, left and right elbows, center three-point shots, and long-distance three-point attempts. These regions largely coincide with the manual partition proposed by the author, and the clustering even suggested numerical limits for the court zones, such as paint boundaries at y-coordinates of around plus or minus 2000. The conclusion is that k-means can serve as a useful starting point for determining the coordinates needed to build court regions manually.

The study is candid about the limitations of automated data collection. The scraping code is reproducible with similar shot data, but if the target website changes its structure, manual updates and further cleaning may be required. This is a familiar trade-off in sports analytics, where researchers often depend on websites they do not control. The author acknowledges the official ACB website for making the data accessible, and the paper’s appendices provide a step-by-step guide to the data collection strategy, along with a glossary of basketball terms to help readers less familiar with the sport’s vocabulary. Such transparency supports reproducibility, a value increasingly emphasized in the R sports analytics community, where packages for basketball, football, and other sports are systematically reviewed and shared.

The broader significance of the work lies in democratizing access to professional basketball data outside the NBA. While the NBA offers well-documented application programming interfaces that have spawned a rich ecosystem of analytical tools, European leagues have historically lagged in data availability. Research has shown that analytics investment can influence team performance in professional basketball, and systematic reviews have highlighted both the potential and the uneven adoption of analytical techniques in the sport. By centralizing box score, play-by-play, and shooting data for the ACB in one platform, backed by a free R package and an interactive web application, this research gives Spanish basketball a data infrastructure comparable in spirit to what NBA analysts enjoy. From shot charts and spatial clustering to player shooting profiles, the tools are now in place for coaches, journalists, and fans to interrogate the game at a level of detail that was previously out of reach for Spain’s top league.

Subject of Research: Web scraping and visualization of shooting data from the Spanish ACB basketball league

Article Title: Adding shooting data to a centralized platform for basketball data visualization

Article References: Adding shooting data to a centralized platform for basketball data visualization. (n.d.). https://doi.org/10.1007/s11042-026-21930-2

Image Credits: AI Generated

DOI: 10.1007/s11042-026-21930-2

Keywords: basketball analytics, ACB, shooting data, web scraping, R package, data visualization, Shiny web application, k-means clustering, DBSCAN, HDBSCAN, shot charts, sport analytics