Does winning breed winning? Winner–loser effects in professional doubles tennis

Overview

https://research.qut.edu.au/best/https://research.qut.edu.au/best/

Athletes, fans and commentators often believe that success generates momentum: winning a crucial point, game or set may increase confidence and make subsequent success more likely. This idea is commonly described as the “hot-hand” effect. However, decades of research have produced mixed findings. Some studies suggest that streaks are largely perceived rather than real, while others find evidence that previous success can influence later performance through confidence, strategic adjustment, physiological responses or changes in risk-taking.

This project investigates winner–loser effects in professional doubles tennis. It builds on research suggesting that competitive outcomes may influence later performance, potentially through psychological and biological pathways such as confidence, stress or testosterone-related responses. Doubles tennis provides a particularly interesting setting because success is experienced collectively: each player competes alongside a teammate, and team dynamics may amplify or weaken the effect of a prior win or loss.

The project focuses on matches in which the first set ended in a tiebreak. These closely contested sets provide a useful setting for examining whether narrowly winning or losing a high-pressure first set predicts the probability of winning the second set. The analysis will compare men’s, women’s and mixed doubles competitions, while accounting for factors such as team rankings, differences in player quality, tournament context and possible home advantage.

The student will work with an existing professional tennis dataset and help develop a clean, reproducible empirical design. The broader research question is whether winner and loser effects are stronger in team-based sporting contexts than in individual competitions, and whether patterns differ by gender composition. The project offers practical experience in sports analytics, behavioural economics, causal reasoning and applied statistical modelling.

Research activities

The student will:

• Conduct a review existing literature to identify gaps and contextualize the current study;
• Work on real-world data using quantitative data analysis techniques;
• Document the findings of the study, interpreting the data and drawing conclusions based on the analysis.

The student will work with Dr Ho Fai Chan and members of the behavioural economics/AI research team at the ARC BITA Centre. They will gain practical experience in Python or R, reproducible data cleaning, regression modelling, visualisation, research documentation and academic writing.

Outcomes

• The project aims to produce a draft manuscript that includes the following:
  • Introduction: Overview of the study’s background and significance.
  • Literature Review: Review of the existing research literature and identification of gaps.
  • Research Aims/Objectives and Questions/Hypotheses: Clear articulation of the study’s aims/objectives and research questions/hypotheses.
  • Methodology: Description of the data collection process and analysis
  • methods.
  • Results and Discussion: Presentation and interpretation of findings.
  • Conclusions and Future Work: Summary of key insights and
  • recommendations for future research.
  • Reference List/Bibliography
  • Appendices (optional)
• Generate a brief, 2-3 slide presentation to present your research at the Faculty of Business and Law VRES Showcase to conclude the program.
• (Optional) The student will be eligible to present the findings of their research to an audience of 100+ academics and industry partners in the annual BITA conference in February/March 2027, provided the student is keen and interested to.
• (Optional) The ultimate goal is to co-author and submit the manuscript with the student to an academic journal, provided the student is keen and interested to. However, it should be noted that the primary deliverable is a final draft manuscript. As it is not certain that the student will be paid beyond the completion of the VRES program in early 2025, any further work on the paper would be entirely voluntary and optional for the student.

Skills and experience

• Suitable for students in economics, statistics, mathematics, sport science, psychology, data science or IT;
• Some proficiency in data entry, data analysis, and statistical techniques preferred; and
• Experience using software such as Stata, R and Python is desirable.

Scholarships

You may be eligible to apply for a research scholarship.

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Keywords

• Behavioural economics
• Tennis
• Sport economics

Contact

Contact the supervisor for more information.