IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v325y2023i1d10.1007_s10479-021-04481-7.html
   My bibliography  Save this article

The analysis of serve decisions in tennis using Bayesian hierarchical models

Author

Listed:
  • Peter Tea

    (Simon Fraser University)

  • Tim B. Swartz

    (Simon Fraser University)

Abstract

Anticipating an opponent’s serve is a salient skill in tennis: a skill that undoubtedly requires hours of deliberate study to properly hone. Awareness of one’s own serve tendencies is equally as important, and helps maintain unpredictable serve patterns that keep the returner unbalanced. This paper investigates intended serve direction with Bayesian hierarchical models applied on an extensive, and now publicly available data source of professional tennis players at Roland Garros. We find discernible differences between men’s and women’s tennis, and between individual players. General serve tendencies such as the preference of serving towards the Body on second serve and on high pressure points are revealed.

Suggested Citation

  • Peter Tea & Tim B. Swartz, 2023. "The analysis of serve decisions in tennis using Bayesian hierarchical models," Annals of Operations Research, Springer, vol. 325(1), pages 633-648, June.
  • Handle: RePEc:spr:annopr:v:325:y:2023:i:1:d:10.1007_s10479-021-04481-7
    DOI: 10.1007/s10479-021-04481-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-021-04481-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10479-021-04481-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Michal Friesl & Jan Libich & Petr Stehlík, 2020. "Fixing ice hockey’s low scoring flip side? Just flip the sides," Annals of Operations Research, Springer, vol. 292(1), pages 27-45, September.
    3. Sebastián Cea & Guillermo Durán & Mario Guajardo & Denis Sauré & Joaquín Siebert & Gonzalo Zamorano, 2020. "An analytics approach to the FIFA ranking procedure and the World Cup final draw," Annals of Operations Research, Springer, vol. 286(1), pages 119-146, March.
    4. Lewandowski, Daniel & Kurowicka, Dorota & Joe, Harry, 2009. "Generating random correlation matrices based on vines and extended onion method," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1989-2001, October.
    5. Mark Walker & John Wooders, 2001. "Minimax Play at Wimbledon," American Economic Review, American Economic Association, vol. 91(5), pages 1521-1538, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Louis Charlot, 2021. "Bayesian hierarchical analysis of a multifaceted program against extreme poverty," Papers 2109.06759, arXiv.org.
    2. Dellaportas, Petros & Titsias, Michalis K. & Petrova, Katerina & Plataniotis, Anastasios, 2023. "Scalable inference for a full multivariate stochastic volatility model," Journal of Econometrics, Elsevier, vol. 232(2), pages 501-520.
    3. Guowen Huang & Patrick E. Brown & Sze Hang Fu & Hwashin Hyun Shin, 2022. "Daily mortality/morbidity and air quality: Using multivariate time series with seasonally varying covariances," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 148-174, January.
    4. Trung Dung Tran & Emmanuel Lesaffre & Geert Verbeke & Joke Duyck, 2021. "Latent Ornstein‐Uhlenbeck models for Bayesian analysis of multivariate longitudinal categorical responses," Biometrics, The International Biometric Society, vol. 77(2), pages 689-701, June.
    5. Lu, Rong, 2020. "Application of machine learning to gas flaring," Thesis Commons g6yvq, Center for Open Science.
    6. Rico Krueger & Taha H. Rashidi & Akshay Vij, 2020. "X vs. Y: an analysis of intergenerational differences in transport mode use among young adults," Transportation, Springer, vol. 47(5), pages 2203-2231, October.
    7. Matthias Breuer & Harm H. Schütt, 2023. "Accounting for uncertainty: an application of Bayesian methods to accruals models," Review of Accounting Studies, Springer, vol. 28(2), pages 726-768, June.
    8. Esther Ulitzsch & Steffi Pohl & Lale Khorramdel & Ulf Kroehne & Matthias Davier, 2022. "A Response-Time-Based Latent Response Mixture Model for Identifying and Modeling Careless and Insufficient Effort Responding in Survey Data," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 593-619, June.
    9. Stephen R. Martin & Philippe Rast, 2022. "The Reliability Factor: Modeling Individual Reliability with Multiple Items from a Single Assessment," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1318-1342, December.
    10. Steffen Jahn & Daniel Guhl & Ainslee Erhard, 2024. "Substitution Patterns and Price Response for Plant-Based Meat Alternatives," Rationality and Competition Discussion Paper Series 509, CRC TRR 190 Rationality and Competition.
    11. Esther Ulitzsch & Steffi Pohl & Lale Khorramdel & Ulf Kroehne & Matthias von Davier, 2024. "Using Response Times for Joint Modeling of Careless Responding and Attentive Response Styles," Journal of Educational and Behavioral Statistics, , vol. 49(2), pages 173-206, April.
    12. Francis,David C. & Kubinec ,Robert, 2022. "Beyond Political Connections : A Measurement Model Approach to Estimating Firm-levelPolitical Influence in 41 Economies," Policy Research Working Paper Series 10119, The World Bank.
    13. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    14. Heinrich, Torsten & Yang, Jangho & Dai, Shuanping, 2020. "Growth, development, and structural change at the firm-level: The example of the PR China," MPRA Paper 105011, University Library of Munich, Germany.
    15. Vincent P. Crawford & Nagore Iriberri, 2004. "Fatal Attraction: Focality, Naivete, and Sophistication in Experimental Hide-and-Seek Games," Levine's Bibliography 122247000000000316, UCLA Department of Economics.
    16. Isabelle Brocas & Juan D. Carrillo, 2004. "Do the “Three-Point Victory†and “Golden Goal†Rules Make Soccer More Exciting?," Journal of Sports Economics, , vol. 5(2), pages 169-185, May.
    17. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    18. Isabelle Brocas & Juan D. Carrillo, 2022. "The development of randomization and deceptive behavior in mixed strategy games," Quantitative Economics, Econometric Society, vol. 13(2), pages 825-862, May.
    19. Subhasish Chowdhury & Dan Kovenock & Roman Sheremeta, 2013. "An experimental investigation of Colonel Blotto games," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 52(3), pages 833-861, April.
    20. Xiaoyue Xi & Simon E. F. Spencer & Matthew Hall & M. Kate Grabowski & Joseph Kagaayi & Oliver Ratmann & Rakai Health Sciences Program and PANGEA‐HIV, 2022. "Inferring the sources of HIV infection in Africa from deep‐sequence data with semi‐parametric Bayesian Poisson flow models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 517-540, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:325:y:2023:i:1:d:10.1007_s10479-021-04481-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.