Soccer Analytics with Machine Learning

Index

Symbols

• 1%-5% rule, Bankroll management
• 1X2 (match result), Match result

A

• activation function, Why Deep Learning for Soccer? Beyond Traditional Models-The hyperbolic tangent (tanh)
• Adam optimizer, Defining the model architecture
• AI in sports analytics, AI and Large Language Models in the Analytics Workflow
• Akaike information criterion (AIC), Model Selection: Choosing the Best Model
• analytical products, From Notebooks to Analytical Products
• API-Football, From Static Datasets to Living Data Pipelines
• arbitrage, Arbitrage Opportunities-Calculating optimal stakes if arbitrage exists
  • execution risk, Calculating optimal stakes if arbitrage exists
  • optimal stakes, Calculating optimal stakes if arbitrage exists
  • rarity, Calculating optimal stakes if arbitrage exists
  • restrictions, Calculating optimal stakes if arbitrage exists
  • speed, Calculating optimal stakes if arbitrage exists
• area under the curve (AUC), The ROC Curve: A Complete Performance Picture
• ARIMA (autoregressive integrated moving average), Time Series and Sequential Data
• arithmetic operators, Basic Operations: Working with Values and Comparisons
• assists, Passing and Possession: Measuring Ball Control
• association versus causation, Principles That Run Through Everything
• AUC (area under the curve), The ROC Curve: A Complete Performance Picture
• autoregressive integrated moving average (ARIMA), Time Series and Sequential Data

B

• backpropagation, Backpropagation
• bagging, The Intuition: Ensemble Learning
• bankroll, Bankroll management
• bankroll management