کتاب تحلیل فوتبال با یادگیری ماشین

چشم‌انداز تحلیل فوتبال

The Problems That Soccer Analytics Helps Solve • Soccer Clubs: Making Decisions Across the Organization • Betting Markets: Modeling Uncertainty and Pricing Outcomes • Fans and Analysts: Reading the Game Beyond the Scoreline • The Data-Driven Evolution in Soccer • From Outcomes to Process: The Limits of Traditional Analysis • The Data Revolution: From Event Logs to Tracking Systems • Analytics in Practice: Metrics, Applications, and Scope • Why Soccer Resists Easy Analysis • A Framework for Soccer Analysis • The Three Types of Analysis • Data Sources for Modern Analysis • From Player to League: Levels of Aggregation • Principles That Run Through Everything • Looking Ahead: A Guided Tour of the Book • Conclusion

مبانی پایتون: ساخت جعبه‌ابزار تحلیل

Python: The Language Behind Modern Analytics • Your Analytics Workspace: Setting Up the Environment • Installation: Getting Python Ready • Virtual Environments: Isolating Your Projects • Jupyter Notebook: Creating a Workspace for Interactive Analysis and More • Repositories: Storing Your Project Files • Python Basics: The Core Moves • Primitive Data Types: Representing Basic Information • Basic Operations: Working with Values and Comparisons • Variable Assignment: Storing and Reusing Information • Data Structures: Organizing Information • Conditional Statements: Writing Decision Rules • Loops: Repeating Computation Efficiently • List Comprehensions: Performing Compact and Pythonic Iteration • Functions: Providing Reusable Logic for Analysis • Looking Ahead: From Python Basics to Real Analysis • Conclusion

تحلیل اکتشافی داده در فوتبال

Understanding Soccer Data Levels • Match-Level Data • Player-Level Data • Event-Level Data • What Is EDA in Soccer Analytics? • Loading StatsBomb Soccer Data • Getting the Data • Loading the Data • Taking a First Look at the Data • Structured EDA by Data Types • Numerical Features: The Quantifiable Game • Categorical Features: The Qualitative Aspects • Ordinal Features: The Ordered Categories • Preliminary Exploration: Getting Your Bearings • Building Tournament DataFrames • Counting the Frequency of Event Types • Filtering: Passes by Player or Team • Grouping: Per-Match Pass Summaries • Aggregating: Shots, Goals, and xG by Team • Basic Data Cleaning for Soccer • Handling Missing Values • Standardizing Names • Visualizing Soccer Data • A Quick Note on Matplotlib and Seaborn • Bar Charts and Rankings • Histograms and Distributions • Boxplots and Variability • Time Series and Cumulative Plots • Scatterplots: Shot Volume Versus Goals • Soccer-Specific Visualizations with mplsoccer • Pass Location Heatmaps • Advanced Pitch Visualizations: Passing Networks • Basic Pitch Visualizations • Advanced Heatmap Visualizations • Case Study: Japan Women’s at the 2019 World Cup • Team-Level Overview • Match Flow • Spatial Patterns • Player-Level Insights • Wrap-up of the Case Study • From EDA to Machine Learning • Features as Building Blocks • The Standard Pipeline • Looking Ahead: From Exploration to Regression • Conclusion

تکنیک‌های رگرسیون برای تحلیل فوتبال

From Lines on a Field to Lines of Best Fit: Linear Regression • Predicting Market Value: A Player’s Worth • Building the Model: The Line of Best Fit • Visualizing the Model • Evaluating Performance: How Good Is Our Model? • Counting Goals: Poisson Regression • Predicting Goals in a Match • Visualizing the Poisson Model • Finding Similar Players: K-Nearest Neighbors Regression • Predicting Player Value with KNN • Choosing the Right k • Evaluating and Interpreting Regression Models in Soccer Contexts • Using Key Performance Metrics • Understanding the Importance of a Validation Set • Interpreting Coefficients • Visualizing KNN: Decision Boundaries • Comparing Models • Advanced Topics in Poisson Regression: Overdispersion and Zero-Inflation • Overdispersion: When Variance Exceeds the Mean • Negative Binomial Regression • Zero Inflation: Too Many Zeros • Hurdle Models: An Alternative Approach • Model Diagnostics: Ensuring That Your Model Is Sound • Residual Analysis: Looking for Patterns • Goodness-of-Fit Tests • Influential Observations: Outliers that Matter • Model Selection: Choosing the Best Model • Practical Case Study: Predicting Match Outcomes • Generating the Dataset • Performing Exploratory Analysis • Building and Comparing Models • Evaluating on Test Data • Interpreting the Results • Feature Engineering for Better Predictions • Interaction Features • Polynomial Features • Categorical Features • Lagged Features • Domain-Specific Features • Common Pitfalls and Practical Tips • Pitfall 1: Overfitting to the Training Data • Pitfall 2: Ignoring Multicollinearity • Pitfall 3: Using the Wrong Model for the Data • Pitfall 4: Forgetting About Data Quality • Tip 1: Start Simple, Then Add Complexity • Tip 2: Visualize, Visualize, Visualize • Tip 3: Document Your Process • Tip 4: Validate with Domain Experts • Looking Ahead: Where Regression Meets the Real World • From Regression to Classification • Ensemble Methods: Combining Models • Deep Learning: The Frontier • Time Series and Sequential Data • Causal Inference: Beyond Prediction • The Human Element • Conclusion

پیش‌بینی نتایج فوتبال با طبقه‌بندی

From Coin Flips to Goal Predictions: Understanding Classification • Logistic Regression: Your First Classifier • The Intuition: From Numbers to Probabilities • Hands-On: Building an Expected Goals Model • Measuring Success: How Good Are Your Predictions? • The Confusion Matrix: A Report Card for Your Model • Precision and Recall: The Two Sides of Performance • The ROC Curve: A Complete Performance Picture • Evaluation of Probabilistic Models with Cost Functions • Beyond Binary: Multiclass Classification • K-Nearest Neighbors: Finding Similar Situations • The Intuition: Birds of a Feather • Hands-On: KNN for Shot Prediction • Choosing the Right K • Case Study: Building a Complete Match Outcome Predictor • Step 1: Define the Problem • Step 2: Gather and Prepare the Data • Step 3: Train and Evaluate the Model • Step 4: Interpret the Results • Key Takeaways from the Case Study • Practical Tips and Common Pitfalls • Handling Imbalanced Data • Selecting and Engineering Features • Avoiding Overfitting • Choosing When to Use Which Algorithm • Tuning the Decision Threshold: Finding the Sweet Spot • Understanding the Threshold Trade-Off • Finding the Optimal Threshold • Deciding When to Adjust the Threshold • Comparing Models: Which Classifier Is Best? • The Model Comparison Framework • Hands-On: Model Comparison for xG • Visualization of Model Performance • Advanced Topics: Going Deeper • Scaling and Normalizing Features • Handling Missing Data • Combining Classifiers with Ensemble Methods • Real-World Considerations • Data Quality and Availability • Model Maintenance and Updates • Ethical Considerations • Looking Ahead: From Linear Boundaries to Decision Trees • Conclusion

روش‌های پیشرفته طبقه‌بندی

The Classification Tree: Soccer’s Decision Flowchart • The Intuition: Learning the If-Then Rules of Soccer • Hands-On: Building a Tree to Predict Pass Outcome • Interpreting the Soccer Intelligence • Understanding Pros and Cons: When to Use a Single Tree • Tuning the Tree: Preventing Overfitting • Random Forest: The Wisdom of the Crowd • The Intuition: Ensemble Learning • Hands-On: Building a Random Forest for to Predict Match Outcome • Interpreting Feature Importance • Tuning Random Forests • Using Out-of-Bag Error • Understanding When Random Forests Struggle • XGBoost: Learning from Mistakes • The Intuition: Sequential Learning • Hands-On: XGBoost for xG Prediction • Performing SHAP Analysis: Explaining the Opaque Model • Tuning XGBoost: The Art of Hyperparameter Optimization • Knowing When to Use XGBoost Versus Random Forest • Practical Applications: From Theory to Practice • Example 1: Building a Comprehensive xG Model • Example 2: Predicting Player Actions • Example 3: Recognizing Tactical Patterns • Handling Imbalanced Data in Soccer • Using Feature Engineering for Tree-Based Models • Looking Ahead: From Handcrafted Rules to Learned Representations • Conclusion

یادگیری عمیق برای تحلیل فوتبال: از شهود تا پیاده‌سازی

Why Deep Learning for Soccer? Beyond Traditional Models • Looking Inside the Model: How Neural Networks Capture Patterns • Learning from Data: Training, Loss, and Optimization • Building Your First Soccer Prediction Model with PyTorch • Binary Outcomes: Will the Home Team Win? • Match Results: Win, Draw, or Loss • Goal Prediction: Modeling Scoring Output • Full Match Simulation: From Goals to Outcomes • A Second Lens: Implementing the Model in TensorFlow • Binary Outcomes: Will the Home Team Win? • Match Results: Win, Draw, or Loss • Goal Prediction: Modeling Scoring Output • Full Match Simulation: From Goals to Outcomes • Looking Ahead: From Flexible Models to Smarter Inputs • Conclusion

مهندسی ویژگی برای تحلیل فوتبال

Feature Engineering: From Raw Data to Predictive Signals • Performance Features: The Fundamentals • Goals For and Goals Against: Measuring Scoring Performance • Venue Splits: Considering Home Versus Away • Shot Quality: Evaluating Chance Creation • Tactical Features: Describing How Teams Play • Passing and Possession: Measuring Ball Control • Tackles, Interceptions, and Recoveries: Tracking Defensive Actions • Pressing Intensity: Applying Defensive Pressure • Set Pieces: Capturing Dead-Ball Situations • Contextual Features: Incorporating Match Conditions • Rest and Scheduling: Modeling Fatigue and Congestion • Form and Momentum: Capturing Recent Performance Trends • Ranking Features and Advanced Ranking Algorithms: Estimating Team Strength • The Colley Matrix: A Linear Algebra Approach • PageRank: A Network-Based Perspective • Elo Ratings: A Dynamic Rating System • A Comparison of Methods: Strengths and Trade-Offs • Player-Level Features • Feature Engineering Pipeline: From Data to Model Input • Case Study: Predicting the 2019 Women’s World Cup Final • Looking Ahead: From Better Inputs to Harder Decisions • Conclusion

هنر و علم شرط‌بندی فوتبال

Soccer Betting Basics • Types of Soccer Bets • Odds • Arbitrage Opportunities • The Real Dataset Used in This Chapter • Choosing a Side • Always Pick the Home Team • Always Pick the Lowest Odds • Use a Simple Machine Learning Baseline • Betting Strategies • Fixed-Stake Betting • Kelly Criterion • From Predictions to Potential Profit • The Reality of Profitability • Practical Considerations • The Importance of Discipline • The Role of Technology • Ethical Considerations • Looking Ahead: Building on Top of the Fundamentals • Conclusion

آینده تحلیل فوتبال

What This Book Was Really About • From Questions to Data • Data Preparation Is Part of the Analysis • Exploration Before Prediction • Feature Engineering Is Where Domain Knowledge Enters • Models Support Judgment, They Do Not Replace It • The Next Stage in Soccer Analytics • From Static Datasets to Living Data Pipelines • From Team-Level Models to Player-Level and Lineup-Level Models • Richer Data: Tracking, Video, and Multimodal Analysis • Stronger Modeling Directions • From Notebooks to Analytical Products • AI and Large Language Models in the Analytics Workflow • Challenges and Limits • Data Quality and Access • Interpretability and Trust • Uncertainty Will Not Disappear • Ethics, Privacy, and Responsible Use • Beyond Soccer • A Practical Roadmap After This Book • Final Thoughts