Test Research¶

Overview¶

This page serves as a template for future research articles and demonstrates the formatting capabilities of our documentation system.

Research Question¶

How can we effectively analyze and present NWSL data to provide actionable insights?

Data Visualization¶

Sample Chart¶

Chart Placeholder

Interactive visualizations will be embedded here using JavaScript libraries.

Statistical Analysis¶

Descriptive Statistics¶

Metric	Value	Std Dev
Goals per Game	2.73	0.45
Shots on Target	8.2	2.1
Pass Completion	78.5%	5.3%

Advanced Metrics¶

We calculate several advanced metrics:

xG (Expected Goals): Probability-based goal prediction
xA (Expected Assists): Likelihood of pass becoming assist
PPDA (Passes Per Defensive Action): Pressing intensity metric

Mathematical Formulas¶

The expected goals model uses logistic regression:

\[P(goal) = \frac{1}{1 + e^{-(\beta_0 + \beta_1x_1 + ... + \beta_nx_n)}}\]

Where: - \(x_i\) represents feature values (distance, angle, etc.) - \(\beta_i\) represents learned coefficients

Code Snippets¶

Data Processing Example¶

import numpy as np
import pandas as pd

def calculate_xg(shot_distance, shot_angle):
    """
    Calculate expected goals based on shot location
    """
    # Simplified xG calculation
    distance_factor = np.exp(-0.1 * shot_distance)
    angle_factor = np.cos(np.radians(shot_angle))

    xg = distance_factor * angle_factor * 0.5
    return min(xg, 1.0)

Interactive Elements¶

Try It Yourself

Future versions will include interactive calculators and data explorers.

Conclusions¶

This template demonstrates the various content types and formatting options available for research articles on this platform.

References¶

StatsBomb Open Data
American Soccer Analysis
NWSL Official Statistics