Heatmaps in soccer: Event vs tracking datasets

David Garrido, Borja Burriel, Ricardo Resta, Roberto López del Campo and Javier M. Buldú

Chaos, Solitons & Fractals, 2022, vol. 165, issue P2

Abstract: We investigate how similar heatmaps of soccer players are when constructed from (i) event datasets and (ii) tracking datasets. When using event datasets, we show that the scale at which the events are grouped strongly influences the correlation with the tracking heatmaps. Furthermore, there is an optimal scale at which the correlation between event and tracking heatmaps is the highest. However, even at the optimal scale, correlations between both approaches are moderate. Furthermore, there is high heterogeneity in the players’ correlation, ranging from negative values to correlations close to the unity. We show that the number of events performed by a player does not crucially determine the level of correlation between both heatmaps. Finally, we analysed the influence of the player position, showing that defenders are the players with the highest correlations while forwards have the lowest.

Keywords: Soccer analytics; Player movement; Heatmaps; Event datasets; Tracking datasets (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:165:y:2022:i:p2:s0960077922010062

DOI: 10.1016/j.chaos.2022.112827

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