What is Soccer Analytics?

The Beautiful Game Meets Data Science

Soccer analytics has transformed from simple statistics like goals and assists to sophisticated data-driven insights that influence every aspect of the modern game. From tactical decisions to player recruitment, data science is now an integral part of professional football.

Evolution of Soccer Analytics

Early Days: Basic Statistics

Traditional soccer statistics focused on simple counting metrics:

• Goals, assists, and clean sheets
• Shots, corners, and possession percentage
• Yellow and red cards

The xG Revolution

Expected Goals (xG) changed everything by asking: "How many goals should a team/player have scored?" Rather than just counting shots, xG evaluates the quality of each chance based on historical data.

import pandas as pd
import numpy as np

# Simplified xG calculation based on shot distance and angle
def calculate_simple_xg(distance, angle, shot_type='foot'):
    """
    Calculate basic xG value for a shot

    Parameters:
    - distance: Distance from goal in meters
    - angle: Angle to goal in degrees
    - shot_type: 'foot', 'header', or 'freekick'
    """
    # Base probability decreases with distance
    base_prob = np.exp(-0.1 * distance)

    # Adjust for angle (wider angle = better chance)
    angle_factor = angle / 90  # Normalize to 0-1

    # Shot type multipliers
    multipliers = {
        'foot': 1.0,
        'header': 0.7,
        'freekick': 0.05
    }

    xg = base_prob * angle_factor * multipliers.get(shot_type, 1.0)
    return min(xg, 1.0)  # Cap at 1.0

# Example shots
shots = pd.DataFrame({
    'player': ['Player A', 'Player B', 'Player C'],
    'distance': [6, 18, 25],
    'angle': [45, 30, 15],
    'shot_type': ['foot', 'header', 'foot']
})

shots['xG'] = shots.apply(
    lambda row: calculate_simple_xg(row['distance'], row['angle'], row['shot_type']),
    axis=1
)

print("Shot Quality Analysis:")
print(shots)
print(f"\nTotal xG: {shots['xG'].sum():.2f}")
print(f"Average xG per shot: {shots['xG'].mean():.2f}")

library(ggplot2)
library(dplyr)

# Create sample xG data
xg_data <- data.frame(
  player = c("Ronaldo", "Messi", "Haaland", "Mbappe", "Kane"),
  goals = c(18, 21, 24, 19, 20),
  xG = c(15.2, 19.8, 22.1, 16.5, 18.9)
)

# Calculate over/underperformance
xg_data$difference <- xg_data$goals - xg_data$xG
xg_data$performance <- ifelse(xg_data$difference > 0, "Outperforming", "Underperforming")

# Create visualization
ggplot(xg_data, aes(x = reorder(player, difference), y = difference, fill = performance)) +
  geom_col() +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  coord_flip() +
  scale_fill_manual(values = c("Outperforming" = "#28a745", "Underperforming" = "#dc3545")) +
  labs(
    title = "Player Goal Performance vs Expected Goals",
    subtitle = "Positive values indicate scoring more than expected",
    x = "Player",
    y = "Goals - xG",
    fill = "Performance"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    legend.position = "bottom"
  )

# Print summary statistics
cat("\nPerformance Summary:\n")
print(xg_data %>% arrange(desc(difference)))

Modern Soccer Analytics Capabilities

1. Event Data Analysis

Every action on the pitch is recorded with precise coordinates, timestamps, and context:

• Passes (successful, failed, direction, length)
• Shots (location, body part, pressure, technique)
• Dribbles, tackles, interceptions
• Defensive actions and clearances

2. Tracking Data

GPS and optical tracking systems capture player and ball position at 25-50 times per second:

• Distance covered and sprint speed
• Acceleration and deceleration patterns
• Space occupation and team shape
• Pressing intensity and defensive lines

3. Video Analysis

Computer vision and machine learning extract insights from match footage:

• Automatic player and ball tracking
• Action recognition and classification
• Tactical formation detection
• Heat maps and movement patterns

Real-World Applications

Getting Started with Soccer Analytics

Essential Skills

Key Metrics to Understand

The Future of Soccer Analytics

The field continues to evolve rapidly:

• AI and Machine Learning: Predicting player development and injury risk
• Real-time Analysis: Live tactical insights during matches
• Wearable Technology: Enhanced biometric and performance tracking
• Fan Engagement: Interactive data experiences for supporters
• Broadcast Enhancement: Data-driven graphics and commentary