Descriptor: Soccer Player Transfer Dataset (SPTD)

Soccer is the most played and studied sport globally. Its enormous popularity attracts billions of spectators for major events like the FIFA World Cup and continental championships, generating a significant financial flow from ticket sales, TV contracts, marketing, and merchandising. Soccer player transfers between soccer clubs are a major financial aspect of soccer. This process allows clubs to sell and buy players, allowing them to acquire a significant amount of money quickly. These transactions are documented and classified on the Transfermarkt website, which contains a substantial volume of transfer data, encompassing buy and sell transactions and loan transfers between teams. This work builds a comprehensive database of male soccer player transfers. We collected the data from the Transfermarkt website using information retrieval algorithms. The collected data was organized, structured, and made available in four formats: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i>) a relational database easily accessible through Structured Query Language (SQL); <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i>) in Comma-Separated Values (CSV) files; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iii</i>) in JavaScript Object Notation (JSON) files; and iv) using the Neo4j, a high-speed graph database. The complete database has more than 3 million transfer records, encompassing around 12 thousand soccer clubs from 108 different countries, and ranges from 1870 to 2024. This paper aims to describe the data collection method and the database. Additionally, it summarises some aspects of the constructed database, such as the number of player transfers and the value of these transfers, to provide initial insights on the collected data. We hope that this comprehensive and structured data can be an important source for researchers worldwide. Furthermore, we also want practitioners and soccer club administrators to use our database to improve their decision-making related to soccer player transfers.

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Publication Details

Journal
IEEE data descriptions.
Published
2025-01-01
DOI
https://doi.org/10.1109/ieeedata.2025.3569490
Citations
1
Primary Topic
Sports Analytics and Performance
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article
Field-Weighted Citation Impact
3.13
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article

Descriptor: Soccer Player Transfer Dataset (SPTD)

Iago A. Carvalho, Flavio B. Gonzaga, A. Franco, OTAVIO F. B. SILVEIRA
1 citations
IEEE data descriptions.
Sports Analytics and Performance
3.13
article

Descriptor: Soccer Player Transfer Dataset (SPTD)

Iago A. Carvalho, Flavio B. Gonzaga, A. Franco, OTAVIO F. B. SILVEIRA
article en
1 citations

Abstract

Soccer is the most played and studied sport globally. Its enormous popularity attracts billions of spectators for major events like the FIFA World Cup and continental championships, generating a significant financial flow from ticket sales, TV contracts, marketing, and merchandising. Soccer player transfers between soccer clubs are a major financial aspect of soccer. This process allows clubs to sell and buy players, allowing them to acquire a significant amount of money quickly. These transactions are documented and classified on the Transfermarkt website, which contains a substantial volume of transfer data, encompassing buy and sell transactions and loan transfers between teams. This work builds a comprehensive database of male soccer player transfers. We collected the data from the Transfermarkt website using information retrieval algorithms. The collected data was organized, structured, and made available in four formats: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i>) a relational database easily accessible through Structured Query Language (SQL); <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i>) in Comma-Separated Values (CSV) files; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iii</i>) in JavaScript Object Notation (JSON) files; and iv) using the Neo4j, a high-speed graph database. The complete database has more than 3 million transfer records, encompassing around 12 thousand soccer clubs from 108 different countries, and ranges from 1870 to 2024. This paper aims to describe the data collection method and the database. Additionally, it summarises some aspects of the constructed database, such as the number of player transfers and the value of these transfers, to provide initial insights on the collected data. We hope that this comprehensive and structured data can be an important source for researchers worldwide. Furthermore, we also want practitioners and soccer club administrators to use our database to improve their decision-making related to soccer player transfers.

IEEE data descriptions.Vol. 2
Universidade Federal de Alfenas (BR)
Openalex Percentile: Top 9%
Sports Analytics and Performance
3.13
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