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Filtering active moments in basketball games using data from players tracking systems

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Abstract

In recent years, sport analytics evolved in the massive collection of data, especially from Global Positioning System (GPS) sensors installed in sport facilities or worn by the athletes. The largest amount of data are used to track locations and trajectories of players during their performance. Data analysis of positioning information during the actions of a game allows a deep characterization of the performance of single players and the whole team. Basketball is one of the team sports where analytics are becoming a fundamental asset. However, during a game, actions are interleaved with inactive periods (e.g., pauses or breaks). For a proper knowledge extraction on the game features, the analysis of players movements must be restricted to active periods only. This paper proposes an algorithm to automatically identify active periods by using players’ tracking data in basketball. The algorithm is based on thresholds that apply to players kinematic parameters. The values of thresholds are identified by setting-up a “ground truth” extracted from the video analysis of the games and by developing a performance evaluation method derived from “Receiver Operating Characteristic” (ROC) curves. When tested on a number of real games, the method shows good performance. This algorithm, along with the identified parameters, could be adopted by practitioners to identify game active periods without the need for video analysis.

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Notes

  1. Aware of possible limitations, we decided to not consider data of both home and away team due to the presence of missing data (i.e. some players had not been tracked for the full game length).

  2. The high variability among single interruptions is increased by the fact that players can attempt either one or two free-throws, depending on the situation. The min values of 3 and 6 seconds are outliers, since, sometimes, it was not possible to correctly track the time due to a replay during the television broadcast.

  3. The aggregation of index t at a frequency of 1 second is necessary, since we match tracking data (expressed in ms) with video-based data (expressed in seconds).

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Acknowledgements

Research carried out in collaboration with the Big&Open Data Innovation Laboratory (BODaI-Lab), University of Brescia (project nr. 03-2016, title Big Data Analytics in Sports, http://bdsports.unibs.it), granted by Fondazione Cariplo and Regione Lombardia.

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Authors and Affiliations

  1. Department of Industrial, Computer and Biomedical Engineering, University of Pavia., Via Ferrata, 1, 27100, Pavia, PV, Italy

    Tullio Facchinetti

  2. Department of Economics and Statistics (DISES), University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy

    Rodolfo Metulini

  3. Department of Economics and Management, University of Brescia, Contrada Santa Chiara, 50, 25122, Brescia, BS, Italy

    Paola Zuccolotto

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  1. Tullio Facchinetti
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Correspondence to Rodolfo Metulini.

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Facchinetti, T., Metulini, R. & Zuccolotto, P. Filtering active moments in basketball games using data from players tracking systems. Ann Oper Res 325, 521–538 (2023). https://doi.org/10.1007/s10479-021-04391-8

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