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Category: Data

Description: Data analysis has become a huge topic, and sports is no exception in order to prevent injuries and optimize performance. However, sports data is often sparse and hard to obtain due to legal restrictions, unwillingness to share, and lack of personnel for the tedious process to collect and prepare the data. These constraints make it difficult to develop systems for analysis, especially automated systems where large datasets are required for learning. We therefore present SoccerMon, the largest dataset available today containing both subjective and objective data collected over two years from two different elite women ́s football teams. In particular, our sports dataset contains 54,485 subjective reports of which there are 529,963 manually reported parameters. Moreover, we have 10,075 objective measurements sessions where there are 6,248,770,794 measured GPS positions on the fields, giving a total of 106,229,103,498 data points. Some initial experiments show how various parameters correlate and demonstrate the potential benefits of artificial intelligence-based prediction systems. Thus, SoccerMon can play a valuable role in developing better analytic models not only for sports, but also for other fields having subjective reports, position information and/or time-series data in general.