Summary of Optimizing Fantasy Sports Team Selection with Deep Reinforcement Learning, by Shamik Bhattacharjee et al.
Optimizing Fantasy Sports Team Selection with Deep Reinforcement Learning
by Shamik Bhattacharjee, Kamlesh Marathe, Hitesh Kapoor, Nilesh Patil
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a reinforcement learning (RL) approach to optimize fantasy cricket team selection by framing the team creation process as a sequential decision-making problem. The model is trained on historical player data and predicts future performance to optimize team composition, maximizing potential performance. This not only presents a significant business opportunity but also enhances user experience. The authors compare their RL-based strategy with traditional methods, demonstrating its effectiveness in constructing competitive fantasy teams. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fantasy cricket has become very popular in India, allowing people to build and compete with virtual teams based on real athletes’ performances. To help people choose the best team, researchers used a special type of learning called reinforcement learning (RL). They looked at past player data and used it to train an algorithm that could predict how well players would do in the future. This allowed them to create a team that was likely to win. The authors compared their method with traditional ways of picking teams and found that it worked better. |
Keywords
» Artificial intelligence » Reinforcement learning
