Ecn 496 economic honor thesis
Title: The Impact of Prize Pool Distribution on Player Effort and Team Performance in CS Esports Tournaments Introduction
The growth of esports has significantly reshaped the landscape of competitive gaming, turning it into a billion-dollar industry. One of the core components that motivate participants in esports tournaments is the structure of the prize pool. Understanding how different prize pool distributions affect player effort and team performance is crucial for stakeholders like tournament organizers and sponsors. The design of the prize pool could maximize not only player engagement but also viewer satisfaction, thereby enhancing the economic sustainability of esports.
My research focuses on the question: How does prize pool distribution (X) affect player effort and team performance (Y) in CS esports tournaments? This question aims to uncover the influence of financial incentives on performance within an industry where prize pool structures can vary substantially, impacting individual and team motivations. This study could provide valuable insights into designing optimal incentive mechanisms that encourage higher levels of effort and engagement in a dynamic, high-frequency competition like esports.
Literature Review
This research builds upon several foundational papers in tournament theory and incentive structures. Lazear and Rosen (1981) provided one of the earliest and most influential examinations of tournament theory, demonstrating that larger prize differences can drive increased participant effort. This finding is highly relevant to esports, where prize pool distributions vary significantly between tournaments. Applying their theory to the esports context allows us to examine whether larger financial incentives translate into greater player effort and improved team performance.
Ehrenberg and Bognanno (1990) examined the role of financial incentives in golf tournaments, finding that larger prizes led to better performance. This paper provides a useful comparison foresports, as both golf and esports involve individual and team dynamics influenced by financial rewards. By extending Ehrenberg and Bognanno's analysis to the context of esports, this study will explore whether similar incentive effects are present in high-frequency, short-duration esports tournaments.
The study by "AI-enabled prediction of video game player performance using the data from sensors" (2022), presents an artificial intelligence solution for predicting esports player performance using sensor data. By collecting physiological, environmental, and smart chair data from both professional and amateur players, the study assesses in-game performance through a recurrent neural network, offering insights into factors influencing player outcomes. This research provides a technological angle on how various factors impact player performance, complementing the analysis of prize pool incentives.
Another recent study, "Fighting fair: community perspectives on the fairness of performance enhancers in esports" (2024) explores the competitive gaming community's opinions on various performance enhancers and their potential impact on esports. Through qualitative and quantitative surveys, the research identifies key themes in how players rationalize their views on fairness and performance enhancement. This perspective helps to understand how fairness considerations can influence player behavior. and engagement in esports tournaments.
Finally, The research titled "Performance analysis in esports: part 1" (2024) focuses on the validity and reliability of match statistics and notational analysis in "League of Legends." It aims to objectively capture aspects of athlete performances to inform. coaching, highlighting the emerging expertise domain within esports and the limited performance analysis currently available. This study underscores the importance of accurate performance metrics in understanding player effort and outcomes, which directly ties into how prize pool incentives can be effectively analyzed.
Data
The analysis will utilize data from multiple esports databases, including HLTV.org, Liquipedia, and Esports Earnings. These sources provide detailed information on CS tournaments, such as prize pool breakdowns, player and team statistics, and match outcomes. The sample population comprises professional CS players and teams who have participated in tournaments from approximately 2015 to the present.
Key variables for this analysis include the structure of the prize pool (total pool and payout format,such as winner-takes-all vs. tiered distributions),team performance metrics (e.g., win-loss records, rankings, and standings), and individual player statistics (e.g., kills, deaths, damage per round). The data can be accessed through public APIs or web scraping, ensuring a comprehensive and timely dataset for the analysis.
Empirical Strategy
To evaluate the causal impact of prize pool distribution on player effort and team performance, I will employ a difference-in-differences (DiD) approach. This method will compare changes in effort and performance metrics across tournaments before and
after changes in prize pool structure, while controlling for confounding variables. The specific equation to be estimated is:
Individual
Yirt = α + β × Treatment + Xᵢ + δrt + εᵢᵣ
● Yirt: Outcome variable representing player or team performance for team i at time t.
● Treatment: Indicator variable for tournaments with altered prize pool distribution (e.g., tiered vs. winner-takes-all).
● Xᵢᵣ: Control variables, such as historical performance, player rank, and team composition.
● δᵣ: Time fixed effects to control for time-specific variations.
● β: The key coefficient of interest, representing the effect of prize distribution structure on performance outcomes.
Team Level:
Yᵣgt = αᵣgt + β × Treatment + Ɣt + ωg + εᵣgt
● Yrgt: Outcome variable representing team performance for team g in tournament r attime t.
● β: The key coefficient of interest, representing the effect of prize distribution structure on performance outcomes.
● Treatment: Indicator variable for tournaments with altered prize pool distribution (e.g., tiered vs. winner-takes-all).
● Ɣt : Time fixed effects to control for time-specific variations.
● ωg : Team fixed effects to control for team-specific characteristics.
● εᵣgt: Error term capturing any unobserved factors affecting performance.
The DiD approach is particularly suited to this research as it helps isolate the effect of prize pool changes by comparing performance between treated and untreated groups over time. To address potential endogeneity issues, robustness checks will be conducted, including instrumental variable (IV) methods, such as using tournament size as an instrument. This empirical strategy will provide robust evidence on how financial incentives impact effort and performance in esports, which can offer valuable guidance to tournament organizers in optimizing prize pool structures.
Conclusion
This research proposal seeks to explore the intricate relationship between prize pool distribution and player effort in the esports industry. By drawing on established economic theories and utilizing high-quality tournament data, this study aims to contribute to both the esports and economics literature, offering actionable insights for tournament organizers and stakeholders in this rapidly growing industry.