Football Talent Evaluation Using Machine Learning

Abstract

Football Talent Evaluation Using Machine Learning Abstract: Football, as one of the most popular and competitive sports globally, continuously seeks innovative ways to gain a competitive edge. The traditional methods of talent evaluation, relying on subjective judgments and scouting, are now complemented by advanced technologies, particularly machine learning. Machine learning applications in football talent evaluation leverage data analytics to provide objective insights into a player's performance, potential, and overall contribution to a team. In recent years, the abundance of data generated during matches, combined with advancements in wearable technology and player tracking systems, has opened up new possibilities for assessing and predicting player capabilities. Machine learning algorithms can process and analyse vast datasets, uncovering patterns, correlations, and trends that might elude human observers. The key elements of football talent evaluation with machine learning include the collection of diverse datasets encompassing player performance metrics, physical attributes, injury history, and even off-field factors. This data serves as the foundation for training models that can predict a player's future performance, identify areas for improvement, and aid in making informed decisions about recruitment, development, and strategic planning. Machine learning models employed in football talent evaluation range from regression models for predicting specific performance metrics (e.g., goals scored, assists) to classification models that categorise players based on their overall talent level. These models are trained on historical data, learning from the patterns and relationships within the data to make predictions about players who haven't been previously assessed.

Specification

Description:CLAIMS:

1. The integration of machine learning into football talent evaluation is a dynamic process, requiring collaboration between data scientists, football analysts, coaches, and scouts.
2. Ethical considerations, such as addressing biases in the data and ensuring fairness in evaluations, play a crucial role in the responsible deployment of machine learning systems within football organisations.
3. As the football landscape continues to evolve, the fusion of traditional scouting expertise with cutting-edge machine learning technologies promises to revolutionise how talent is identified, nurtured, and maximised, ultimately contributing to the success of teams at both domestic and international levels.
4. This introduction marks the intersection of sports and artificial intelligence, creating a paradigm shift in the way football talent is discovered and developed.
, Claims:Objective:

2.1.1 Machine Learning to Identify and Evaluate Football Talent

Scouting classification with machine learning involves using machine learning algorithms to classify players based on their potential as a professional athlete. This can be done by training a machine learning model on a dataset of players that includes features such as their physical attributes, performance statistics, and other relevant characteristics. The model can then be used to predict the likelihood that a player will be successful at the professional level based on these features.
Business problem: Scouting classification Predicting which class (average, highlighted) players are according to the scores given to the characteristics of the football players watched by the Scouts.

Dataset story: The data set consists of information from Scoutium, which includes the features and scores of the football players evaluated by the scouts according to the characteristics of the footballers observed in the matches.
Dataset content:

● Goalkeeper
● Stopper
● Right-back
● Left-back
● Defensive midfielder
● Central midfielder
● Right wing
● Left wing
● Attacking midfielder
● Striker


Model evaluation: Model evaluation is the process of assessing the performance of a machine learning model on a set of data. It helps to understand how well the model is able to make predictions or classifications on unseen data. There are several evaluation metrics that can be used to measure the performance of a model, and the choice of metric will depend on the specific task and the goals of the model.
Some common evaluation metrics for classification tasks include:

● Accuracy: This is the most basic evaluation metric and it measures the proportion of correct predictions made by the model.
● Precision: This metricmeasures the proportion of correct positive predictions made by the model.
● Recall: This metric measures the proportion of positive cases that the model correctly identified.
● F1 score: This is the harmonic mean of precision and recall, and it is often used as a single metric to evaluate the performance of a classification model.

Split the data into training and test sets: It is important to evaluate a model on a separate test set, rather than using the training data, to get a more accurate assessment of its performance on unseen data. You can use the train_test_split function from scikit-learn to split your data into training and test sets.
Feature importance: Feature importance is a technique used to evaluate the importance or relevance of each feature in a machine learning model. It helps to identify the features that have the most impact on the model's predictions and can be used for feature selection, which is the process of selecting a subset of the most relevant features for building a model.
Description:
1. Player Performance Prediction Models

Technology: Machine learning algorithms predicting player performance based on historical data, playing style, and various metrics.
Advantages:
● Provides objective and data-driven insights into a player's potential.
● Allows for continuous improvement as the model learns from new data.

2. Position-Specific Algorithms

Technology: Machine learning models tailored to evaluate players based on their specific positions.
Advantages:
● Acknowledges the diverse skill sets required for different positions.
● Enhances the precision of talent evaluation for specific team needs.

3. Player Similarity Analysis

Technology: Clustering algorithms grouping players based on similarities in playing style and attributes.
Advantages:

● Enables personalised development plans by identifying comparable players.
● Facilitates targeted recruitment strategies.

4. Injury Risk Prediction

Technology: Classification models predicting the likelihood of injuries based on player workload, history, and physical attributes.
Advantages:
● Enhances injury prevention strategies and player management. ●
Allows for proactive measures to mitigate injury risks.

5. Scouting Assistance with Natural Language Processing (NLP)

Technology: NLP algorithms analysing textual data from scouting reports, news, and social media.
Advantages:
● Extracts valuable insights about a player's behaviour, attitude, and off-field activities.
● Augments traditional scouting with comprehensive off-field information.

6. Player Value Prediction Models

Technology: Regression models predicting a player's market value based on performance metrics, age, and contract status.
Advantages:
● Assists clubs in making informed decisions about transfers and contracts.
● Provides a data-driven approach to player valuation.

7. Tactical Analysis with Graph Theory

Technology: Graph theory and network analysis for studying player interactions and team dynamics.

Advantages:
● Enhances tactical understanding by visualising player relationships on the field.
● Identifies key players and patterns in team strategies.

8. Game Outcome Prediction Models

Technology: Classification models predicting match outcomes based on team compositions and recent performances.
Advantages:
● Aids in strategic planning by anticipating match results.
● Provides insights for pre-match analysis and preparation.

9. Advanced Analytics - Expected Goals (xG) Models

Technology: Statistical models assessing the quality of goal-scoring opportunities.
Advantages:
● Offers a nuanced evaluation of attacking performance beyond traditional metrics.
● Enhances understanding of the effectiveness of goal-scoring chances.

10. Interpretability with Explainable AI (XAI)

Technology: Techniques to make machine learning models more interpretable. Advantages:
● Ensures transparency in decision-making, fostering trust in the evaluation process.
● Facilitates collaboration between data scientists and football experts.


Data Pre-Processing: This section will briefly state the process and results of the pre-processing of data obtained from two football websites, and how these data will be used in two studies to build the regression model. The main programming tool used for data preprocessing is the Pandas and NumPy library of python - which transfers all the data in Excel to the data frame to do further operation and processing.
Table Combining: After the research acquires the data, both Performance data from the website FBREF and Salary data from the website CAPOLOGY are separated into different leagues. The first step is to combine all the data from different leagues into one data table. Performance data from the website FBREF for the players are separated into several tables, and each table corresponds to one type of character of players - including standard stats, shooting, passing, defensive actions, and possessions. Therefore, the study combines all feature tables for one season into an integrated table based on the name of the players and does the same operations for another two seasons.

Data Cleaning: After integrating all the character tables, all players will correspond to the data of all their features. But not all the features in the table are needed in the study, according to the research on the features of players, only some selected features' data will be used to build the model. Thus, the first step is to extract the needed features' data from the table. Moreover, some players do not play much time in a season or even they never have a chance to play in a season, therefore, to keep the efficiency and accuracy of the data set, the research will only consider the players whose total playing time is larger than 90 minutes, and delete all the players who do not reach the 90 minutes requirements.

Data Type Transferring for some features: The salary data from the website CAPOLOGY also include the player's name, current club, and current league of the players. But these three factors are the string type, and they cannot directly be used to build the statistical model. So, the study needs to transfer these characters
into the integer, and players with the same clubs or leagues will have the integer for those characters.

The calculation of player's total achievements: The study calculates a total achievement index from the ranking of three tournaments that this thesis has done the analysis and explained. this research has claimed that the domestic league, domestic league cup, and UEFA Champions League (UCL) will be counted in the total achievements index, but the domestic league and UEFA Champions League (UCL) are much more important than the domestic league cup, so domestic leagues and UCL will also have much more weights than domestic league cup. The equation will be:


𝑇𝑜𝑡𝑎𝑙 𝐺𝑟𝑎𝑑𝑒 = 1/𝑙𝑒𝑎𝑔𝑢𝑒 𝑟𝑎𝑛𝑘 * 4. 5 + 1/𝑈𝐶𝐿 𝑅𝑎𝑛𝑘 * 4. 5 + 1/𝐿𝑒𝑎𝑔𝑢𝑒 𝐶𝑢𝑝 𝑟𝑎𝑛𝑘 * 1

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