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Here We Go! Predicting Transfer Market Valuations of Premier League Footballers
- As the January transfer window approaches in the Premier League, clubs evaluate their squads and prepare for potential mid-season changes.
- Transfermarkt, a respected data company, influences player valuations in world football by considering factors like age, performance, and experience.
- An analysis of player market values is conducted using GraphSAGE, which involves creating nodes for players and teams with relevant statistics.
- The model's dataset is sourced from open platforms like FBREF and Transfermarkt, allowing for comprehensive data processing and analysis.
- A graph structure is constructed to represent relationships between players, teams, and their performance metrics over multiple years.
- GraphSAGE, a framework for large graphs, is employed to generate node embeddings and update them iteratively through neural network layers.
- Different variations of the GraphSAGE model are tested, including models with dropout and neighborhood sampling, to optimize player valuation predictions.
- The models' performance is evaluated based on training, validation, and test losses, highlighting the effectiveness of dropout and sampling techniques in improving accuracy.
- A focus on preventing overfitting and high loss is maintained through measures like Mean Absolute Percentage Error calculation, normalization of input features, and gradient clipping.
- Results show that GraphSAGE models can accurately estimate player market values by leveraging graph neural network design and incorporating dropout and sampling methods.
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