Summary of Deep Learning and Transfer Learning Architectures For English Premier League Player Performance Forecasting, by Daniel Frees et al.
Deep Learning and Transfer Learning Architectures for English Premier League Player Performance Forecasting
by Daniel Frees, Pranav Ravella, Charlie Zhang
First submitted to arxiv on: 3 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents a groundbreaking model for forecasting English Premier League (EPL) player performance using convolutional neural networks (CNNs). The authors evaluate Ridge regression, LightGBM, and CNNs on predicting upcoming player Fantasy Premier League (FPL) scores based on historical FPL data. The baseline models, Ridge regression and LightGBM, achieve solid performance, highlighting the importance of recent FPL points, influence, creativity, threat, and playtime in predicting EPL player performances. The optimal CNN architecture outperforms previous EPL player performance forecasting models and achieves strong Spearman correlation with player rankings, indicating implications for developing FPL artificial intelligence (AI) Agents and providing analysis for FPL managers. Additionally, the authors perform transfer learning experiments on soccer news data, but do not identify a strong predictive signal in natural language news texts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps predict how well English Premier League football players will do using special computer models called convolutional neural networks (CNNs). The researchers tested different kinds of models to see which one worked best. They found that a special kind of model, the CNN, was really good at predicting player scores based on what happened in previous games. This is important because it can help create computer programs that can choose the best football players for teams and give advice to people who play fantasy football. |
Keywords
» Artificial intelligence » Cnn » Regression » Transfer learning
