Optimizing Fantasy Sports Team Selection with Deep Reinforcement Learning (2412.19215v1)

Abstract: Fantasy sports, particularly fantasy cricket, have garnered immense popularity in India in recent years, offering enthusiasts the opportunity to engage in strategic team-building and compete based on the real-world performance of professional athletes. In this paper, we address the challenge of optimizing fantasy cricket team selection using reinforcement learning (RL) techniques. By framing the team creation process as a sequential decision-making problem, we aim to develop a model that can adaptively select players to maximize the team's potential performance. Our approach leverages historical player data to train RL algorithms, which then predict future performance and optimize team composition. This not only represents a huge business opportunity by enabling more accurate predictions of high-performing teams but also enhances the overall user experience. Through empirical evaluation and comparison with traditional fantasy team drafting methods, we demonstrate the effectiveness of RL in constructing competitive fantasy teams. Our results show that RL-based strategies provide valuable insights into player selection in fantasy sports.

• The paper frames fantasy sports team selection as a sequential decision-making task using a Markov Decision Process (MDP) to optimize player choices.
• Deep Reinforcement Learning algorithms, specifically DQN and PPO, are applied to historical player data to train agents for optimal fantasy cricket team composition.
• Empirical evaluation shows that the proposed RL frameworks significantly outperform traditional methods, achieving higher percentile rankings in fantasy sports competitions.

The paper "Optimizing Fantasy Sports Team Selection with Deep Reinforcement Learning" explores the application of advanced reinforcement learning techniques to the problem of fantasy cricket team selection. In the context of the rapidly growing fantasy sports market in India, particularly for cricket, the authors propose using RL frameworks to automate and optimize the team selection process, leveraging historical player performance data.

Key Contributions:

1. Framing the Problem as a Sequential Decision-Making Task:
   • The authors model the task of selecting a fantasy sports team as a Markov Decision Process (MDP). This structured framework allows the use of reinforcement learning algorithms to systematically evaluate and select players with the aim of maximizing team performance in upcoming contests.
2. Reinforcement Learning Techniques:
   • Two RL algorithms, Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), are used to train agents in crafting optimal cricket teams. These techniques are adept at handling problems involving high-dimensional state spaces and sequential decision-making.
   • The RL agent starts with a randomly selected team and iteratively refines it via player swaps, guided by a reward structure specifically designed to reflect the objective of maximizing fantasy points.
3. Data Curation and Preprocessing:
   • The methodology involves collecting and processing detailed historical performance data over a rolling ninety-day window for players participating in T20 international matches, the IPL, and other major tournaments. The dataset includes metrics like batting averages and bowling strike rates, essential for capturing player form and potential.
4. State and Action Space Definition:
   • States are defined based on the performance metrics of selected and reserved players, while actions entail swapping players between these two groups. The action space is thus generated as a tuple indicating which player is removed from and added to the team.
5. Empirical Evaluation:
   • The paper compares the performance of RL-based team selection methods against traditional approaches such as selecting based on previous performance or user selection percentages. The results indicate that RL frameworks significantly outperform these traditional methods, consistently achieving high percentile rankings in various competitions.
6. Numerical Results:
   • The RL models demonstrate improved team selection capabilities, as evidenced by an analysis of the predicted team scores against the empirically determined best team scores. Notably, the PPO algorithm exhibited superior performance among the methods tested, achieving scores placing constructed teams in favorable percentile positions within competitive fantasy sports contests.

Methodologies:

• Data Normalization: Ensures that the model accounts equally for varying scoring patterns across different matches.
• Training Framework: Utilizes the Stable-Baselines3 library and was deployed on GPUs, showcasing both high computational demand and potential for distributed processing.
• Hyperparameter Tuning: Analyzed trade-offs in reward function design, particularly the use of parameter $\alpha$ to modulate goal state criteria, thereby balancing training efficiency and accuracy.

Conclusion and Future Work:

The authors conclude that RL methods provide a robust framework for improving fantasy sports team selection strategies. This work enhances the potential for data-driven decision-making in sports. Future directions may include real-time data integration and exploration of advanced RL methodologies to further refine predictions and strategic team compositions across diverse sports settings.

Overall, this paper makes significant strides in applying RL to fantasy sports, setting a foundation for subsequent research in this intersection of data science and sports analytics.