The FIFA Player Worth Determination Model is a sophisticated AI/ML project designed to assess the value of football players based on their statistics and their compatibility with a specific team. Unlike traditional player valuation models, this project considers the existing chemistry of the team and the positional dynamics to provide club-specific player valuations. This enables club owners to make informed bidding decisions in both in-game and real-life FIFA auctions.
The primary objective of this project is to determine the worth of football players by integrating various factors:
- Player Statistics: Analyzing the performance statistics of players.
- Chemistry with Team: Evaluating how well a player fits into the existing team dynamics.
- Positional Analysis: Assessing the positional needs of the team and the player's alignment with those needs.
- Club-Specific Valuation: Calculates the value of a player based on the current players of the club and their statistics.
- Bid Recommendation: Recommends optimal bidding strategies based on the player's worth and team chemistry.
- Interactive Interface: Provides an intuitive interface for users (club owners) to input data and receive valuation insights.
- Scalability: Designed to handle large datasets of player statistics and adaptable to different football leagues and teams.
- Input Data: Provide player statistics and team details via the interface.
- Valuation Process: The model analyzes the input data to determine the player's worth to the team.
- Recommendations: Receive valuation insights and bidding recommendations based on the analysis.
The project uses a dataset comprising statistics of thousands of active football players across different leagues. The dataset is preprocessed and curated to facilitate accurate player valuations and team chemistry analysis.
The FIFA Player Worth Determination Model employs a multi-step process to evaluate player worth:
Data Preprocessing: Player feature data is preprocessed to ensure quality and consistency. Player Chemistry: The chemistry of the player with neighboring positions is determined. Best Position Analysis: The best position for the player to play is identified based on team needs. Team Attributes Assessment: Overall team attributes are analyzed before and after integrating the new player, comparing the new player's worth with the existing player. Regression Model: The outputs from the DNN models are fed into a few fully connected layers to form a regression model, which determines the player's worth using these features.