Game Analytics Dashboards

Now that we've set up our data pipeline and loaded our game data, we can create interactive dashboards to visualize and analyze player performance. This chapter covers the analytics dashboards we've built to monitor different aspects of the Balloon Popper game.

Purpose and Rationale

Analytics dashboards serve as the visual interface to our game data, allowing game designers, developers, and business stakeholders to:

1. Monitor player performance in real-time or based on historical data
2. Identify patterns and trends in gameplay that might not be apparent in raw data
3. Make data-driven decisions about game balance, difficulty, and feature development
4. Track the success of game modifications and updates

For our Balloon Popper game, we've created three specialized dashboards, each focusing on a specific aspect of gameplay analysis:

• Leaderboard Dashboard: For tracking competitive player performance
• Color Analysis Dashboard: For understanding player interaction with game elements
• Performance Analysis Dashboard: For analyzing scoring efficiency and patterns

Technology Stack

Our dashboards are built using these key technologies:

Technology	Purpose	Why We Chose It
Streamlit	Interactive web interface	Fast development, Python-based, easy integration with data pipeline
Altair	Data visualizations	Declarative API, statistical visualization focus, interactive features
Pandas	Data processing	Powerful time-series capabilities, aggregation functions, seamless integration

This stack allows us to quickly develop and deploy interactive dashboards that directly connect to our existing data pipeline.

Dashboard Architecture

Each dashboard follows a similar architecture:

# 1. Import libraries
import streamlit as st
import altair as alt
import pandas as pd

# 2. Access processed data from session state
if st.session_state.data_variable is not None:
    data = st.session_state.data_variable

    # 3. Create interactive controls
    with st.sidebar:
        # Filters, selectors, time range pickers, etc.

    # 4. Display summary metrics
    col1, col2, col3 = st.columns(3)
    with col1:
        st.metric("Metric Name", "Value")

    # 5. Generate visualizations
    chart = alt.Chart(data).encode(
        # Chart specifications
    )

    # 6. Display visualizations
    st.altair_chart(chart, use_container_width=True)

This consistent structure makes our dashboards maintainable and extensible.

Dashboard Overview

The following sections explain each dashboard in detail, including:

• Implementation details with code examples
• Key features and their technical implementation
• Data processing techniques used in each dashboard
• Visualization design decisions and best practices

Our dashboard implementations demonstrate several important concepts:

• Using multi-column layouts for organized information display
• Creating interactive filters in the sidebar
• Designing responsive visualizations with tooltips and interactive elements
• Implementing effective data tables with custom column formatting

Learning Focus

Pay special attention to how we handle time-based data and aggregations, as these are common patterns in game analytics.

In the following sections, we'll dive deep into each dashboard's implementation, providing the complete code and explaining the rationale behind our design decisions.

Next Steps

After reviewing the dashboard implementations, you'll be able to:

1. Understand how to build interactive Streamlit dashboards
2. Create effective data visualizations with Altair
3. Process and transform game data for visualization
4. Implement your own custom game analytics dashboards

Let's start by exploring the Leaderboard Dashboard implementation.