CASE STUDY

Demand & Supply Forecasting Framework
for Sports Participation

Big Data & Analytics
IT / Technology

Project Highlights

Scope

  • Demand–supply forecasting framework for ~4M population. Demand modelling for 20 sports across demographic and geographic segments
  • Forecast integration with facility supply and external data sources

Challenges Overcome

Data-driven forecasts: Decision-makers now rely on explainable, model-based projections to guide long-term sports strategy and investment planning.

Enhanced decision-making: Authorities now use demographic and regional insights to align facilities more closely with actual community needs.

Iterative validation: Stakeholders now validate assumptions at every stage, building confidence and trust in the outcomes.

Business Impact

The framework empowered sports authorities to make confident, evidence-based decisions for future investment and community engagement.

Greater Forecast Accuracy

Faster Scenario Planning

Better Facility Utilisation

Reduction in Manual Workload

The Client

A leading sports data and analytics consultancy firm supporting governments, federations, and organisations.

  • Specialises in data-driven modelling, analytics, and strategic forecasting for sports participation.
  • Provides insights to shape community health, engagement, and infrastructure development.
middle age of working woman and team serious mood, modern office

The Problem

The client needed a robust and transparent way to understand and forecast sports participation in a region.

  • Lack of integrated demand insights: Existing data sources were scattered and not consolidated into a single forecasting framework.
  • Limited visibility across demographics and regions: Authorities struggled to predict which sports would grow in popularity among specific age groups, genders, or geographic areas.
  • No alignment between demand and supply: Without accurate demand projections, it was challenging to match existing facilities with actual community needs or justify future infrastructure investments.

The Solution

Data preparation and cleaning

Large datasets from population surveys, demographic records, and sports preference studies were consolidated, standardised, and cleaned to ensure reliability and consistency.

Predictive modelling and analysis

Using Python libraries such as pandas, NumPy, scikit-learn, seaborn, and matplotlib, BGTS developed predictive and statistical models that could estimate demand for each sport across demographic and geographic segments.

Integrated forecasting framework

Demand projections were enriched with facility supply data and contextual external inputs, enabling the creation of regional forecasts that aligned available infrastructure with community sports participation needs.

Team & Technology

Tech Stack Utilised

Programming Language

  • Python

Data Processing & Modelling

  • Pandas
  • NumPy
  • Scikit-learn

Analytics & Visualisation

  • Matplotlib
  • Seaborn

AI & Data Techniques

  • Language models
  • Statistical modelling & forecasting

Team Composition

2 data scientists

1 data engineer

1 project manager

1 domain analyst

The Outcome

The project delivered measurable value to strategic sports planning.

Faster planning cycles

Automated modelling replaced manual analysis, allowing planning teams to prepare scenarios much more quickly.

Enhanced accuracy

Forecasts became significantly more reliable, with demand projections tailored to demographics and regions.

Greater transparency

Explainable models and iterative validation built trust and confidence among stakeholders.

Improved utilisation

Facility supply was better matched to actual community demand, reducing inefficiencies.

Scalable framework

The methodology can be reused and expanded to cover additional sports or other regions.

Informed investment

Authorities gained evidence-based insights to guide facility development and funding priorities.