Professional eSports Player Demographics and Prize Earnings

56,895 professional and semi-professional eSports players from multiple countries are included in this dataset. It combines demographic information with performance outcomes measured through prize earnings to study the Relative Age Effect. The data were used to estimate the effect of birth timing on financial success using regression and Causal Random Forest methods.

Use Cases

• Analyze the relationship between a player's birth month and their annual prize earnings to test for the Relative Age Effect.
• Model country-level variations in prize earnings across different competitive years.
• Use birth date and year variables to construct relative age measures for cohort analysis within eSports.

Strengths

• Contains data on 56,895 individual players, providing a substantial sample for analysis.
• Includes key variables like country, year, prize earnings, and date of birth for constructing detailed relative age measures.
• Data has been used in published research employing advanced methods like Causal Random Forest.

Limitations

• The dataset's specific column structure and file formats are unknown, which may complicate initial data loading and exploration.
• Without the full column list, the availability of other potentially relevant player attributes (e.g., game genre, team affiliation) is uncertain.
• The temporal coverage (specific years) and exact geographic scope of the countries included are not detailed in the provided input.

Provenance

Source

Harvard Dataverse, authored by Petr Parshakov.

Collection Method

Individual-level information on professional and semi-professional eSports players, compiled for empirical analysis.

Time Range

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Freshness

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Geography

Multiple countries (specific countries not listed).