ArticleScientific data2025
An integrated dataset of spatiotemporal and event data in elite soccer.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in football talent identification: a systematic review.Frontiers in sports and active living · 2026Pooled it
- A graph-integrated reinforcement learning framework with graph neural networks for tactical decision modeling in professional football.Scientific reports · 2026Article
- An integrated dataset of spatiotemporal and event data in elite soccer.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Data-driven match analysis in soccer is a growing discipline in both research and practice. However, public data is scarce, which raises the barrier for entering this field and decreases reproducibility of methods and results. To bridge this gap, this paper presents a dataset of official match information, event, and position data from seven matches of the German Bundesliga's first and second division. The match information contains meta data about the matches and their participants. The event data contain timestamps along with descriptions of discrete events, like passes, shots, or fouls. The position data contain the x/y-coordinates of every player and the ball. By integrating multiple data modalities - i.e., event logs with timestamps, and x-y coordinates of player and ball positions - the dataset offers a multidimensional view of match dynamics. This dataset supports the validation of existing analytical techniques and facilitates the development of new methodologies in sports analytics. With availability under CC-BY 4.0, it promotes transparency, reproducibility, and the idea of open science in match analysis research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.