Optimizing Fantasy Sports Team Selection with Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2412.19215v1
• Date: Thu, 26 Dec 2024 13:36:18 GMT
• Title: Optimizing Fantasy Sports Team Selection with Deep Reinforcement Learning
• Authors: Shamik Bhattacharjee, Kamlesh Marathe, Hitesh Kapoor, Nilesh Patil,
• Abstract summary: We 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. Our results show that RL-based strategies provide valuable insights into player selection in fantasy sports.
• Score: 0.2399911126932527
• License:
• 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.

Related papers

• FanCric : Multi-Agentic Framework for Crafting Fantasy 11 Cricket Teams [0.0]
  This study concentrates on Dream11, India's leading fantasy cricket league for IPL, where participants craft virtual teams based on real player performances to compete internationally. This research introduces the FanCric framework, an advanced multi-agent system leveraging Large Language Models (LLMs) and a robust orchestration framework to enhance fantasy team selection in cricket. FanCric employs both structured and unstructured data to surpass traditional methods by incorporating sophisticated AI technologies.
  arXiv  Detail & Related papers  (2024-10-02T08:01:28Z)
• An Integrated Framework for Team Formation and Winner Prediction in the FIRST Robotics Competition: Model, Algorithm, and Analysis [0.0]
  We apply our method to the drafting process of the FIRST Robotics competition. First, we develop a method that could extrapolate individual members' performance based on overall team performance. An alliance optimization algorithm is developed to optimize team formation and a deep neural network model is trained to predict the winning team.
  arXiv  Detail & Related papers  (2024-01-06T23:11:50Z)
• All by Myself: Learning Individualized Competitive Behaviour with a Contrastive Reinforcement Learning optimization [57.615269148301515]
  In a competitive game scenario, a set of agents have to learn decisions that maximize their goals and minimize their adversaries' goals at the same time. We propose a novel model composed of three neural layers that learn a representation of a competitive game, learn how to map the strategy of specific opponents, and how to disrupt them. Our experiments demonstrate that our model achieves better performance when playing against offline, online, and competitive-specific models, in particular when playing against the same opponent multiple times.
  arXiv  Detail & Related papers  (2023-10-02T08:11:07Z)
• Understanding why shooters shoot -- An AI-powered engine for basketball performance profiling [70.54015529131325]
  Basketball is dictated by many variables, such as playstyle and game dynamics. It is crucial that the performance profiles can reflect the diverse playstyles. We present a tool that can visualize player performance profiles in a timely manner.
  arXiv  Detail & Related papers  (2023-03-17T01:13:18Z)
• Data Science Approach to predict the winning Fantasy Cricket Team Dream 11 Fantasy Sports [0.0]
  The application of Data Science and Analytics is Ubiquitous in the Modern World. We built a predictive model that predicts the performance of players in a prospective game.
  arXiv  Detail & Related papers  (2022-09-15T01:58:57Z)
• Retrospective on the 2021 BASALT Competition on Learning from Human Feedback [92.37243979045817]
  The goal of the competition was to promote research towards agents that use learning from human feedback (LfHF) techniques to solve open-world tasks. Rather than mandating the use of LfHF techniques, we described four tasks in natural language to be accomplished in the video game Minecraft. Teams developed a diverse range of LfHF algorithms across a variety of possible human feedback types.
  arXiv  Detail & Related papers  (2022-04-14T17:24:54Z)
• Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition [88.26752130107259]
  In real-world multiagent systems, agents with different capabilities may join or leave without altering the team's overarching goals. We propose COPA, a coach-player framework to tackle this problem. We 1) adopt the attention mechanism for both the coach and the players; 2) propose a variational objective to regularize learning; and 3) design an adaptive communication method to let the coach decide when to communicate with the players.
  arXiv  Detail & Related papers  (2021-05-18T17:27:37Z)
• Generating Diverse and Competitive Play-Styles for Strategy Games [58.896302717975445]
  We propose Portfolio Monte Carlo Tree Search with Progressive Unpruning for playing a turn-based strategy game (Tribes) We show how it can be parameterized so a quality-diversity algorithm (MAP-Elites) is used to achieve different play-styles while keeping a competitive level of play. Our results show that this algorithm is capable of achieving these goals even for an extensive collection of game levels beyond those used for training.
  arXiv  Detail & Related papers  (2021-04-17T20:33:24Z)
• Optimising Long-Term Outcomes using Real-World Fluent Objectives: An Application to Football [16.456411166427188]
  We model the teams' objectives for a season and track how these evolve as games unfold to give a fluent objective that can aid in decision-making games. We develop Markov chain Monte Carlo and deep learning-based algorithms that make use of the fluent objectives in order to learn from prior games and other games in the environment. Simulations of our approach using real-world datasets from 760 matches shows that by using optimised tactics with our fluent objective and prior games, we can on average increase teams mean expected finishing distribution in the league by up to 35.6%.
  arXiv  Detail & Related papers  (2021-02-18T16:42:04Z)
• CRICTRS: Embeddings based Statistical and Semi Supervised Cricket Team Recommendation System [6.628230604022489]
  We propose a semi-supervised statistical approach to build a team recommendation system for cricket. We design a qualitative and quantitative rating system which considers the strength of opposition also for evaluating player performance. We also embark on a critical aspect of team composition, which includes the number of batsmen and bowlers in the team.
  arXiv  Detail & Related papers  (2020-10-26T15:35:44Z)
• Faster Algorithms for Optimal Ex-Ante Coordinated Collusive Strategies in Extensive-Form Zero-Sum Games [123.76716667704625]
  We focus on the problem of finding an optimal strategy for a team of two players that faces an opponent in an imperfect-information zero-sum extensive-form game. In that setting, it is known that the best the team can do is sample a profile of potentially randomized strategies (one per player) from a joint (a.k.a. correlated) probability distribution at the beginning of the game. We provide an algorithm that computes such an optimal distribution by only using profiles where only one of the team members gets to randomize in each profile.
  arXiv  Detail & Related papers  (2020-09-21T17:51:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.