2D AMIS: Spatial Normalization Data for Soccer Matches

32,000 observations from a 25×40 grid reference dataset of the Russian Championship 2012-13 season. This dataset accompanies the article '2D ADAPTIVE MULTI-INTERVAL SCALE (AMIS): METHOD FOR NORMALIZATION AND VISUALIZATION OF SPATIAL DATA' and includes example match data from the 2018 and 1966 World Cup finals, software, and resulting normalized heatmaps. It was authored by Gennady Kravtsov and last updated on 2026-06-09.

Use Cases

• Benchmarking the 2D AMIS normalization method against min-max or z-score scaling using the provided example data.
• Visualizing spatial profiles and heatmaps for soccer match events based on the included grid data.
• Developing custom spatial analysis tools by adapting the provided Python source code.
• Studying the spatial dynamics of specific soccer matches like the 2018 World Cup final.

Strengths

• Includes a reference dataset with 32,000 observations on a 25×40 grid.
• Provides both an executable tool and Python source code for the 2D AMIS method.
• Contains specific, real-world example data from notable soccer matches (e.g., 2018 World Cup final).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the primary dataset is unknown, which may limit suitability assessment.
• The executable is packaged for Windows only, which may limit accessibility for other operating systems.

Provenance

Source

Harvard Dataverse, accompanying a research article.

Collection Method

Likely collected from public sports statistics for the example matches.

Time Range

Reference data from 2012-13; example matches from 1966 and 2018.

Freshness

Last updated 2026-06-09 15:25:06; freshness should be verified.

Geography

Data relates to the Russian Championship and international World Cup finals.