“I need more money, Steve... I need more money,” pleads Billy Beane, the exhausted General Manager of the Oakland A’s, to a firm but resigned top executive, who responds with a stark reality: “We’re not going to compete with these teams that have big budgets. We’re going to work within the constraints we have, and you’re going to get out there and do the best job that you can.”

It’s a refrain we experience often: no matter how large our resources, it remains a challenge to explore, innovate, and deliver solutions that meet the scale and complexity of our clients’ needs.

If you’ve watched the 2011 film Moneyball (based on the 2003 book of the same title), you already know where this is heading. Billy Beane (Brad Pitt) tackles the challenge of limited resources when economist Peter Brand (Jonah Hill) introduces him to the sabermetric method—a data-driven design approach that uses analytics to uncover undervalued talent. This strategy transformed the Oakland A’s into an unlikely contender, proving that smart, evidence-based decision-making can outperform traditional models.

The design disciplines have their own versions of this “sabermetric” method, often described as parametric, generative, or algorithmic design. The core idea remains the same: use data-driven design to guide smarter decisions. Just as Beane and Brand used analytics to optimize their lineup, architects, engineers, and planners use computational design tools to solve complex problems and improve design performance.

What is computational design?

Computational building design strengthens the resilience and sustainability of urban and architectural systems. Through modeling and iterative analysis, it supports buildings development, infrastructure development, and infrastructure planning—all aimed at improving long-term building sustainability.

By analyzing parameters such as land use, transport connectivity, energy efficiency, and social equity, computational tools make evidence-based decisions possible, helping optimize urban layouts and community planning. This approach reduces carbon footprints, enhances social interaction, and promotes equitable access to essential amenities—creating sustainable, inclusive, and livable cities.

At a high level, the Computational Design team works primarily in the fields of design, sustainability, development, and complete communities.

A few projects to illustrate

Sustainability:

Carbon Sequestration Model

Computational design allows for the calculation of carbon sequestration by trees in a park, comparing it to the carbon emissions from paving materials. By analyzing data on species, growth rates, and climate, the model estimates how long it will take for trees to offset the carbon footprint of the paving. This method supports building sustainability by guiding responsible material use and planting decisions.

Design:

Campus design

For the University of Ottawa’s Lees Campus, Arcadis designed a 220,000 sq. ft. Faculty of Health Sciences building that brings together five schools to promote interdisciplinary learning. Located by the Rideau River and near a new LRT station, the site’s potential was maximized through a computational building design model that explored thousands of design options.

Criteria such as solar optimization, open space quality, and sightlines were evaluated using data-driven design. The top-performing options were refined into the final schematic design, demonstrating how computation can inform both architecture and infrastructure planning.

Development:

Generative housing

Computational design can evaluate thousands of parcels simultaneously to identify suitable sites for housing development based on customizable metrics. In San Diego, the Generative Housing project applied this approach to test feasibility, optimize massing, and assess built-form options through a dynamic pro forma.

This data-driven design process provides stakeholders with reliable, evidence-based insights that guide better decisions for infrastructure development and housing strategy.

Complete Communities:

The design of 15-minute walkable neighborhoods

In Detroit, Arcadis applied computational building design models to develop the 15-minute neighborhood strategy—ensuring residents can access all daily needs within a short walk. The analysis integrated demographics, land use, transit, and economic data to model infrastructure planning scenarios for equitable urban growth.

These insights informed district-level buildings development strategies and helped shape an inclusive and resilient city vision.