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Text Mining and Performance Analysis

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Proceedings of the 9th International Performance Analysis Workshop and Conference & 5th IACSS Conference (PACSS 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1426))

Abstract

As a discipline originating from notational analysis and biomechanics, performance analysis is dominated by research based on metrical data like match-statistics or physiological measures. However, a lot of evaluations of performance are provided in the form of written text, for example, scouting reports or (pre-) match analysis. Innovative text mining techniques enable researchers to derive knowledge from such written texts in an effective manner but have hardly been applied in performance analysis so far. In this long abstract, I present some promising applications of text mining in three areas of interest for performance analysis: the evaluation of technological officiating aids, player scouting and predicting match outcome. For each, I intend to demonstrate how deriving information from text sources can contribute to the knowledge base in the respective area. In my concluding remarks, I further appeal to define standards for the use of text mining in performance analysis.

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References

  1. Mayring, P.: Qualitative Inhaltsanalyse: Grundlagen und Techniken. Beltz, Weinheim, Germany (2008)

    Google Scholar 

  2. Cai, Y., Sun, J.T.: Text mining. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems, p. 131. Springer, Boston, MA (2009)

    Google Scholar 

  3. Bhatia, S., Mellers, B., Walasek, L.: Affective responses to uncertain real-world outcomes: sentiment change on Twitter. PLoS One 14(2), e0212489 (2019)

    Article  Google Scholar 

  4. Culotta, A.: Lightweight methods to estimate influenza rates and alcohol sales volume from Twitter messages. Lang. Resour. Eval. 47, 217–238 (2013)

    Article  Google Scholar 

  5. Schmidt, S.H., Frederick, L.E., Pegoraro, A., Spencer, T.C.: An analysis of Colin Kapernick, Megan Rapinoe, and the National Anthem Protests. Commun. Sport 7(5), 653–677 (2019)

    Article  Google Scholar 

  6. Frederick, L.E., Pegoraro, A., Schmidt, S.H.: “I’m Not going to the f***ing White House”: Twitter Users React to Donald Trump and Megan Rapinoe. Commun. Sport OnlineFirst, 1–19 (2020)

    Google Scholar 

  7. Fan, M., Billings, A., Zhu, X., Yu, P.: Twitter-based BIRGing: big data analysis of English national team fans during the 2018 FIFA World Cup. Commun. Sport 8(3), 317–345 (2020)

    Article  Google Scholar 

  8. Kolbinger, O.: VAR experiments in the Bundesliga. In: Armenteros, M., Benítez, A.J., Betancor, M.A. (eds.) The Use of Video Technologies in Refereeing Football and Other Sports, pp. 228–245. Routledge, New York, NY (2020)

    Google Scholar 

  9. Cunningham, N.R., Eastin, M.S.: Second screen and sports: a structural investigation into team identification and efficacy. Commun. Sport 5(3), 288–310 (2017)

    Article  Google Scholar 

  10. Kolbinger, O., Knopp, M.: Video kills the sentiment – exploring fans’ reception of the video assistant referee in the English premier league using Twitter data. PLoS One 15(12), e0242728 (2020)

    Article  Google Scholar 

  11. Sumsky, A.: Kobe Bryant’s Scouting Report is Worth the Read. https://basketballfor-ever.com/2020/07/02/kobe-bryants-scouting-report-worth-read. Accessed 20 Sep 2021

  12. Seppa, T., Schuckers, M.E., Rovito, M.: Text mining of scouting reports as a novel data source for improving NHL draft analytics. In: Ottawa Hockey Analytics Conference, pp. 1–11. Ottawa, ON

    Google Scholar 

  13. Maymin, P.: Using scouting report text to predict NCAA→NBA performance. J. Bus. Analyt. 4(1), 40–54 (2021)

    Article  Google Scholar 

  14. Beal, R., Middleton, S.E., Norman, T.J., Ramchurn, S.D.: Combining Machine Learning and Human Experts to Predict Match Outcomes in Football: A Baseline Model. Preprint at https://arxiv.org/abs/2012.04380 (2020)

  15. Dixon, M.J., Coles, S.G.: Modelling association football scores and inefficiencies in the football betting market. J. R. Stat. Soc. Ser. C: Appl. Stat. 46(2), 265–280 (1997)

    Article  Google Scholar 

  16. Surowiecki, J.: The Wisdom of Crowds. Doubleday, New York, NY (2004)

    Google Scholar 

  17. Schumaker, R.P., Labedz, C.S., Jr., Jarmoszko, A.T., Brown, L.L.: Prediction from regional angst – a study of NFL sentiment in Twitter using technical stock market charting. Decis. Support Syst. 98, 80–88 (2017)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Technical University of Munich, Munich, Germany

    Otto Kolbinger

Authors
  1. Otto Kolbinger
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Correspondence to Otto Kolbinger .

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Editors and Affiliations

  1. Institut für Sportwissenschaft Universität Wien, Wien, Austria

    Arnold Baca

  2. University of Vienna, Wien, Austria

    Juliana Exel

  3. Faculty of Sports and Health Sciences, Technical University Munich, München, Bayern, Germany

    Martin Lames

  4. Middlesex University, Hendon, UK

    Nic James

  5. The Burroughs, Middlesex University, Hendon, UK

    Nimai Parmar

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Kolbinger, O. (2022). Text Mining and Performance Analysis. In: Baca, A., Exel, J., Lames, M., James, N., Parmar, N. (eds) Proceedings of the 9th International Performance Analysis Workshop and Conference & 5th IACSS Conference. PACSS 2021. Advances in Intelligent Systems and Computing, vol 1426. Springer, Cham. https://doi.org/10.1007/978-3-030-99333-7_1

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