Data-Driven Team Selection in Fantasy Premier League Using Integer Programming and Predictive Modeling Approach

• URL: http://arxiv.org/abs/2505.02170v1
• Date: Sun, 04 May 2025 16:21:59 GMT
• Title: Data-Driven Team Selection in Fantasy Premier League Using Integer Programming and Predictive Modeling Approach
• Authors: Danial Ramezani,
• Abstract summary: This paper proposes novel deterministic and robust integer programming models that select the optimal starting eleven and the captain.<n>A new hybrid scoring metric is constructed using an interpretable artificial intelligence framework and underlying match performance data.<n>Results indicate that the proposed hybrid method achieved the highest score while maintaining consistent performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fantasy football is a billion-dollar industry with millions of participants. Constrained by a fixed budget, decision-makers draft a squad whose players are expected to perform well in the upcoming weeks to maximize total points. This paper proposes novel deterministic and robust integer programming models that select the optimal starting eleven and the captain. A new hybrid scoring metric is constructed using an interpretable artificial intelligence framework and underlying match performance data. Several objective functions and estimation techniques are introduced for the programming model. To the best of my knowledge, this is the first study to approach fantasy football through this lens. The models' performance is evaluated using data from the 2023/24 Premier League season. Results indicate that the proposed hybrid method achieved the highest score while maintaining consistent performance. Utilizing the Monte Carlo simulation, the strategic choice of averaging techniques for estimating cost vectors, and the proposed hybrid approach are shown to be effective during the out-of-sample period. This paper also provides a thorough analysis of the optimal formations and players selected by the models, offering valuable insights into effective fantasy football strategies.

Related papers

• Multi-Step Alignment as Markov Games: An Optimistic Online Gradient Descent Approach with Convergence Guarantees [91.88803125231189]
  Reinforcement Learning from Human Feedback (RLHF) has been highly successful in aligning large language models with human preferences.<n>While prevalent methods like DPO have demonstrated strong performance, they frame interactions with the language model as a bandit problem.<n>In this paper, we address these challenges by modeling the alignment problem as a two-player constant-sum Markov game.
  arXiv  Detail & Related papers  (2025-02-18T09:33:48Z)
• How to Select Datapoints for Efficient Human Evaluation of NLG Models? [57.60407340254572]
  We develop a suite of selectors to get the most informative datapoints for human evaluation.<n>We show that selectors based on variance in automated metric scores, diversity in model outputs, or Item Response Theory outperform random selection.<n>In particular, we introduce source-based estimators, which predict item usefulness for human evaluation just based on the source texts.
  arXiv  Detail & Related papers  (2025-01-30T10:33:26Z)
• Optimizing Fantasy Sports Team Selection with Deep Reinforcement Learning [0.2399911126932527]
  We develop a model that can adaptively select players to maximize the team's potential performance.<n>Our approach leverages historical player data to train RL algorithms, which then predict future performance and optimize team composition.<n>Our results show that RL-based strategies provide valuable insights into player selection in fantasy sports.
  arXiv  Detail & Related papers  (2024-12-26T13:36:18Z)
• Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities [69.34646544774161]
  We formulate a new variant of multi-player multi-armed bandit (MAB) model, which captures arrival of requests to each arm and the policy of allocating requests to players. The challenge is how to design a distributed learning algorithm such that players select arms according to the optimal arm pulling profile. We design an iterative distributed algorithm, which guarantees that players can arrive at a consensus on the optimal arm pulling profile in only M rounds.
  arXiv  Detail & Related papers  (2024-08-20T13:57:00Z)
• Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning [55.65738319966385]
  We propose a novel online algorithm, iterative Nash policy optimization (INPO)<n>Unlike previous methods, INPO bypasses the need for estimating the expected win rate for individual responses.<n>With an LLaMA-3-8B-based SFT model, INPO achieves a 42.6% length-controlled win rate on AlpacaEval 2.0 and a 37.8% win rate on Arena-Hard.
  arXiv  Detail & Related papers  (2024-06-30T08:00:34Z)
• Predicting Football Match Outcomes with eXplainable Machine Learning and the Kelly Index [0.0]
  A machine learning approach is developed for predicting the outcomes of football matches. The dataset originated from the Premier League match data covering the 2019-2021 seasons. The paper also devised an investment strategy in order to evaluate its effectiveness by benchmarking against bookmaker odds.
  arXiv  Detail & Related papers  (2022-11-28T19:32:58Z)
• 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)
• Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction [97.99938802797377]
  Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in recommender systems. Recent deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success. We propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper.
  arXiv  Detail & Related papers  (2022-06-28T03:28:15Z)
• Prediction of Football Player Value using Bayesian Ensemble Approach [13.163358022899335]
  We present a case study on the key factors affecting the world's top soccer players' transfer fees based on the FIFA data analysis. To predict each player's market value, we propose an improved LightGBM model using a Tree-structured Parzen Estimator (TPE) algorithm.
  arXiv  Detail & Related papers  (2022-06-24T07:13:53Z)
• Explainable expected goal models for performance analysis in football analytics [5.802346990263708]
  This paper proposes an accurate expected goal model trained consisting of 315,430 shots from seven seasons between 2014-15 and 2020-21 of the top-five European football leagues. To best of our knowledge, this is the first paper that demonstrates a practical application of an explainable artificial intelligence tool aggregated profiles.
  arXiv  Detail & Related papers  (2022-06-14T23:56:03Z)
• 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.