# Optimizing Fantasy Sports Team Selection with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2412.19215 · HTML: https://mlsanity.com/paper/2412.19215/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 26 Dec 2024.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review](https://mlsanity.com/paper/1912.11762/) (2019)
- [Multi-stream Data Analytics for Enhanced Performance Prediction in Fantasy Football](https://mlsanity.com/paper/1912.07441/) (2019)

## Built on by (most cited first, 1 of 1)

- [Analyzing Skill Element in Online Fantasy Cricket](https://mlsanity.com/paper/2512.22254/) (2025)

## Abstract (arXiv)

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.