Deep Learning and Transfer Learning Architectures for English Premier League Player Performance Forecasting

• URL: http://arxiv.org/abs/2405.02412v1
• Date: Fri, 3 May 2024 18:13:52 GMT
• Title: Deep Learning and Transfer Learning Architectures for English Premier League Player Performance Forecasting
• Authors: Daniel Frees, Pranav Ravella, Charlie Zhang,
• Abstract summary: This paper presents a groundbreaking model for forecasting English Premier League (EPL) player performance using convolutional neural networks (CNNs) We evaluate Ridge regression, LightGBM and CNNs on the task of predicting upcoming player FPL score based on historical EPL data over the previous weeks. Our optimal CNN architecture achieves better performance with fewer input features and even outperforms the best previous EPL player performance forecasting models in the literature.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a groundbreaking model for forecasting English Premier League (EPL) player performance using convolutional neural networks (CNNs). We evaluate Ridge regression, LightGBM and CNNs on the task of predicting upcoming player FPL score based on historical FPL data over the previous weeks. Our baseline models, Ridge regression and LightGBM, achieve solid performance and emphasize the importance of recent FPL points, influence, creativity, threat, and playtime in predicting EPL player performances. Our optimal CNN architecture achieves better performance with fewer input features and even outperforms the best previous EPL player performance forecasting models in the literature. The optimal CNN architecture also achieves very strong Spearman correlation with player rankings, indicating its strong implications for supporting the development of FPL artificial intelligence (AI) Agents and providing analysis for FPL managers. We additionally perform transfer learning experiments on soccer news data collected from The Guardian, for the same task of predicting upcoming player score, but do not identify a strong predictive signal in natural language news texts, achieving worse performance compared to both the CNN and baseline models. Overall, our CNN-based approach marks a significant advancement in EPL player performance forecasting and lays the foundation for transfer learning to other EPL prediction tasks such as win-loss odds for sports betting and the development of cutting-edge FPL AI Agents.

Related papers

• MatchTime: Towards Automatic Soccer Game Commentary Generation [52.431010585268865]
  We consider constructing an automatic soccer game commentary model to improve the audiences' viewing experience. First, observing the prevalent video-text misalignment in existing datasets, we manually annotate timestamps for 49 matches. Second, we propose a multi-modal temporal alignment pipeline to automatically correct and filter the existing dataset at scale. Third, based on our curated dataset, we train an automatic commentary generation model, named MatchVoice.
  arXiv  Detail & Related papers  (2024-06-26T17:57:25Z)
• Estimating Player Performance in Different Contexts Using Fine-tuned Large Events Models [0.7373617024876725]
  This paper introduces an innovative application of Large Event Models (LEMs) in soccer analytics. LEMs predict variables for subsequent events rather than words. We focus on fine-tuning LEMs with the WyScout dataset for the 2017-18 Premier League season.
  arXiv  Detail & Related papers  (2024-02-09T22:47:25Z)
• Bayes-xG: Player and Position Correction on Expected Goals (xG) using Bayesian Hierarchical Approach [55.2480439325792]
  This study investigates the influence of player or positional factors in predicting a shot resulting in a goal, measured by the expected goals (xG) metric. It uses publicly available data from StatsBomb to analyse 10,000 shots from the English Premier League. The study extends its analysis to data from Spain's La Liga and Germany's Bundesliga, yielding comparable results.
  arXiv  Detail & Related papers  (2023-11-22T21:54:02Z)
• Betting the system: Using lineups to predict football scores [0.0]
  This paper aims to reduce randomness in football by analysing the role of lineups in final scores. Football clubs invest millions of dollars on lineups and knowing how individual statistics translate to better outcomes can optimise investments. Sports betting is growing exponentially and being able to predict the future is profitable and desirable.
  arXiv  Detail & Related papers  (2022-10-12T15:47:42Z)
• CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation Alignment [146.3128011522151]
  We propose a Omni Crossmodal Learning method equipped with a Video Proxy mechanism on the basis of CLIP, namely CLIP-ViP. Our approach improves the performance of CLIP on video-text retrieval by a large margin. Our model also achieves SOTA results on a variety of datasets, including MSR-VTT, DiDeMo, LSMDC, and ActivityNet.
  arXiv  Detail & Related papers  (2022-09-14T05:47:02Z)
• 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)
• Transfer Portal: Accurately Forecasting the Impact of a Player Transfer in Soccer [0.0]
  Predicting future player performance when transferred between different leagues is a complex task. In this paper, we present a method which addresses these issues and enables us to make accurate predictions of future performance. Our Transfer Portal model utilizes a personalized neural network accounting for both stylistic and ability level input representations for players, teams, and leagues to simulate future player performance at any chosen club.
  arXiv  Detail & Related papers  (2022-01-27T14:15:09Z)
• From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension [51.953428342923885]
  We develop a two-stage approach to enhance the model performance. The first stage targets at recall: we design a hard-learning (HL) algorithm to maximize the likelihood that the top-k predictions contain the accurate answer. The second stage focuses on precision: an answer-aware contrastive learning mechanism is developed to learn the fine difference between the accurate answer and other candidates.
  arXiv  Detail & Related papers  (2021-12-09T07:31:15Z)
• Ranking Creative Language Characteristics in Small Data Scenarios [52.00161818003478]
  We adapt the DirectRanker to provide a new deep model for ranking creative language with small data. Our experiments with sparse training data show that while the performance of standard neural ranking approaches collapses with small datasets, DirectRanker remains effective.
  arXiv  Detail & Related papers  (2020-10-23T18:57:47Z)
• Interpretable Real-Time Win Prediction for Honor of Kings, a Popular Mobile MOBA Esport [51.20042288437171]
  We propose a Two-Stage Spatial-Temporal Network (TSSTN) that can provide accurate real-time win predictions. Experiment results and applications in real-world live streaming scenarios showed that the proposed TSSTN model is effective both in prediction accuracy and interpretability.
  arXiv  Detail & Related papers  (2020-08-14T12:00:58Z)

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.