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Understanding the effect of contextual factors and decision making on team performance in Twenty20 cricket: an interpretable machine learning approach

Author

Listed:
  • Praveen Puram

    (Indian Institute of Management Kozhikode (IIMK))

  • Soumya Roy

    (Indian Institute of Management Kozhikode (IIMK))

  • Deepak Srivastav

    (Indian Institute of Management Kozhikode (IIMK))

  • Anand Gurumurthy

    (Indian Institute of Management Kozhikode (IIMK))

Abstract

For better performance in any team sport, team managers assess the match conditions, and opponents’ strengths and weaknesses to select the best team possible. In cricket, existing studies focus on the effect of contextual factors such as home advantage, toss win, and toss decision, among others, on team performance. However, very few studies discuss the factors’ relative importance or the extent of their impact on performance. There is also a lack of studies addressing the best situational decisions to be taken by teams, given certain opponents and match conditions. This study aims to determine the effect of contextual factors and subsequent decisions taken on team performance in Twenty20 (T20) cricket. Match-wise data for nine seasons of the Indian premier league consisting of 563 matches were considered, and tree-based machine learning (ML) models such as gradient boosting, regression tree, bagging, random forest, and bayesian additive regression tree (BART) were employed for data analysis. BART produced the most efficient results, which were further interpreted using Interpretable ML methods such as partial dependence plots and accumulated local effects to determine the most critical factors affecting team performance. Additionally, these findings were used to obtain optimal pre-match decisions and pre-season strategies to achieve higher performance, which could serve as a decision support system for teams in T20 cricket.

Suggested Citation

  • Praveen Puram & Soumya Roy & Deepak Srivastav & Anand Gurumurthy, 2023. "Understanding the effect of contextual factors and decision making on team performance in Twenty20 cricket: an interpretable machine learning approach," Annals of Operations Research, Springer, vol. 325(1), pages 261-288, June.
  • Handle: RePEc:spr:annopr:v:325:y:2023:i:1:d:10.1007_s10479-022-05027-1
    DOI: 10.1007/s10479-022-05027-1
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    as
    1. Majd Kharfan & Vicky Wing Kei Chan & Tugba Firdolas Efendigil, 2021. "A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches," Annals of Operations Research, Springer, vol. 303(1), pages 159-174, August.
    2. Steven Salaga & Katie M Brown, 2018. "Momentum and betting market perceptions of momentum in college football," Applied Economics Letters, Taylor & Francis Journals, vol. 25(19), pages 1383-1388, November.
    3. V. Bhaskar, 2009. "Rational Adversaries? Evidence from Randomised Trials in One Day Cricket," Economic Journal, Royal Economic Society, vol. 119(534), pages 1-23, January.
    4. P Scarf & S Akhtar, 2011. "An analysis of strategy in the first three innings in test cricket: declaration and the follow-on," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(11), pages 1931-1940, November.
    5. Dieudonné Tchuente & Serge Nyawa, 2022. "Real estate price estimation in French cities using geocoding and machine learning," Annals of Operations Research, Springer, vol. 308(1), pages 571-608, January.
    6. Zied Ftiti & Kais Tissaoui & Sahbi Boubaker, 2022. "On the relationship between oil and gas markets: a new forecasting framework based on a machine learning approach," Annals of Operations Research, Springer, vol. 313(2), pages 915-943, June.
    7. Abhinav Sacheti & Ian Gregory-Smith & David Paton, 2016. "Managerial Decision Making Under Uncertainty," Journal of Sports Economics, , vol. 17(1), pages 44-63, January.
    8. P Dawson & B Morley & D Paton & D Thomas, 2009. "To bat or not to bat: An examination of match outcomes in day-night limited overs cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1786-1793, December.
    9. Asif, Muhammad & McHale, Ian G., 2016. "In-play forecasting of win probability in One-Day International cricket: A dynamic logistic regression model," International Journal of Forecasting, Elsevier, vol. 32(1), pages 34-43.
    10. J M Norman & S R Clarke, 2007. "Dynamic programming in cricket: optimizing batting order for a sticky wicket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(12), pages 1678-1682, December.
    11. Dibyojyoti Bhattacharjee & Hemanta Saikia, 2016. "An objective approach of balanced cricket team selection using binary integer programming method," OPSEARCH, Springer;Operational Research Society of India, vol. 53(2), pages 225-247, June.
    12. R Bhattacharya & P S Gill & T B Swartz, 2011. "Duckworth–Lewis and Twenty20 cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(11), pages 1951-1957, November.
    13. Philip Scarf & Xin Shi & Sohail Akhtar, 2011. "On the distribution of runs scored and batting strategy in test cricket," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 471-497, April.
    14. Erdinc Akyildirim & Ahmet Goncu & Ahmet Sensoy, 2021. "Prediction of cryptocurrency returns using machine learning," Annals of Operations Research, Springer, vol. 297(1), pages 3-36, February.
    15. P Scarf & S Akhtar, 2011. "An analysis of strategy in the first three innings in test cricket: declaration and the follow-on," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(11), pages 1931-1940, November.
    16. Hemanta Saikia, 2020. "Quantifying the Current Form of Cricket Teams and Predicting the Match Winner," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 45(2), pages 151-158, May.
    17. Luigi Bianchi & Chiara Liti & Giampaolo Liuzzi & Veronica Piccialli & Cecilia Salvatore, 2022. "Improving P300 Speller performance by means of optimization and machine learning," Annals of Operations Research, Springer, vol. 312(2), pages 1221-1259, May.
    18. Gaurav Deval & Faiz Hamid & Mayank Goel, 2021. "When to declare the third innings of a test cricket match?," Annals of Operations Research, Springer, vol. 303(1), pages 81-99, August.
    19. Tao, Yu-Li & Chuang, Hwei-Lin & Lin, Eric S., 2016. "Compensation and performance in Major League Baseball: Evidence from salary dispersion and team performance," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 151-159.
    20. Harsha Perera & Jack Davis & Tim B. Swartz, 2018. "Assessing the impact of fielding in Twenty20 cricket," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(8), pages 1335-1343, August.
    21. Sabahi, Sima & Parast, Mahour Mellat, 2020. "The impact of entrepreneurship orientation on project performance: A machine learning approach," International Journal of Production Economics, Elsevier, vol. 226(C).
    22. Arnab Adhikari & Adrija Majumdar & Gaurav Gupta & Arnab Bisi, 2020. "An innovative super-efficiency data envelopment analysis, semi-variance, and Shannon-entropy-based methodology for player selection: evidence from cricket," Annals of Operations Research, Springer, vol. 284(1), pages 1-32, January.
    23. Sohail Akhtar & Philip Scarf & Zahid Rasool, 2015. "Rating players in test match cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(4), pages 684-695, April.
    24. Akhtar, Sohail & Scarf, Philip, 2012. "Forecasting test cricket match outcomes in play," International Journal of Forecasting, Elsevier, vol. 28(3), pages 632-643.
    25. G D Sharp & W J Brettenny & J W Gonsalves & M Lourens & R A Stretch, 2011. "Integer optimisation for the selection of a Twenty20 cricket team," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(9), pages 1688-1694, September.
    26. R Bhattacharya & P S Gill & T B Swartz, 2011. "Duckworth–Lewis and Twenty20 cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(11), pages 1951-1957, November.
    27. V. Bhaskar, 2009. "Rational Adversaries? Evidence from Randomised Trials in One Day Cricket," Economic Journal, Royal Economic Society, vol. 119(534), pages 1-23, January.
    28. Marina Johnson & Abdullah Albizri & Serhat Simsek, 2022. "Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis," Annals of Operations Research, Springer, vol. 308(1), pages 275-305, January.
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