Elite Soccer Player Running Performance Across Match Contexts

583 observations of running match performance for elite senior male soccer players across a full season. The data includes running variables like total distance, high-intensity distance, and accelerations, coded by contextual factors such as player position, match location, competition level, and result.

Use Cases

• Model the relationship between contextual factors like field positioning and competition level on running outputs such as total covered distance and high metabolic load distance.
• Analyze the distribution of high-intensity running metrics like distance in sprint (>25.2 km/h) and combined HSR and SPR (>19.8 km/h) across match results (win, draw, loss).
• Compare player workload metrics such as relative distance (m/min) and number of high-magnitude accelerations/decelerations between home and away match locations.
• Investigate correlations between maximal running speed and other performance variables like entries in the HSR and sprint zones across different player positions.

Strengths

• Contains 583 observations of match performance data from elite athletes.
• Includes multiple specific running performance metrics and contextual match factors for analysis.
• Data is explicitly coded by contextual factors like field positioning, location, competition level, and match result.

Limitations

• Sample size of 583 observations may be limited for training complex machine learning models without risk of overfitting.
• Dataset is restricted to elite senior male soccer players, limiting generalizability to other populations like female or youth players.
• Lacks explicit column definitions and sample data, which may increase the time required for data understanding and validation.

Provenance

Source

Tomáš Malý Dataverse.

Collection Method

Running match performance data collected from elite senior male soccer players.

Time Range

Covers a full season; specific years not provided.

Freshness

Last updated on 2026-01-20.