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The game beyond the field: on football players’ performance through social media, sentiment and topic analysis

Author

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  • Marco Ortu

    (University of Cagliari)

  • Francesco Mola

    (University of Cagliari)

Abstract

This study investigates the complex relationship between social media sentiment and football players’ performance in the English Premier League (EPL). We adapt the TOpic modeling Based Index Assessment through Sentiment (TOBIAS) framework, originally developed for educational settings, to the domain of sports analytics. This novel application faces difficulties in handling the volume and variability of social media data, as well as in accurately linking pre-match sentiments to post-match performance metrics. Our methodology integrates advanced Natural Language Processing (NLP) techniques, including sentiment analysis and topic modeling, with Partial Least Squares Path Modeling (PLS-PM). We analyze a dataset of 167,841 tweets related to 512 English Premier League (EPL) players, collected from May 2022 to May 2023. The study is conducted in two phases: pre-match analysis to assess public expectations, and post-match analysis to evaluate reactions to player performances. Experimental analysis reveals significant correlations between pre-match sentiments and subsequent player performance, with negative sentiments showing a stronger predictive power than positive ones. Post-match, we observe a shift in the relationship between sentiments and performance metrics, indicating the public’s responsiveness to match outcomes. Our findings contribute to the broader understanding of social media’s role in sports performance and offer insights for potential applications in regulating online behaviors in sports contexts.

Suggested Citation

  • Marco Ortu & Francesco Mola, 2025. "The game beyond the field: on football players’ performance through social media, sentiment and topic analysis," Computational Statistics, Springer, vol. 40(4), pages 2085-2108, April.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:4:d:10.1007_s00180-024-01584-0
    DOI: 10.1007/s00180-024-01584-0
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    References listed on IDEAS

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    1. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
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