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We make* data a key part of our litigation strategy. We advocate for and advise clients based not just on our judgment, experience, and analysis of case law, but also based on research and empirical data. We make data-driven§ decisions by harnessing available technology that make use of data analytics, engaging with pioneering empirical research on litigation, and developing our own proprietary datasets and analytics. This culture of “evidence-based litigation” allows our litigators to make expert decisions‡ that take our advice and advocacy for clients to the next level..

We believe in data-driven decision-making.

What does that mean? We analogize it to “evidence-based medicine”. The term “evidence-based medicine” emerged in the late 1980s and early 1990s to describe an approach to medicine that bases guidelines and treatment decisions not just on anecdotal experiences, but also on the available empirical evidence. In the same way, we are committed to creating a culture of practicing “evidence-based litigation”. That means that we advocate for and advise clients based not just on our judgment and analysis of applicable case law, but also based on research and empirical data, where it is available.

In practice, this approach means three things:

  • Harnessing available technology and products that make use of data analytics. As the legal technology industry develops, we will be on the front lines, harnessing technology that we believe can provide us with better insights to advise our clients.
  • Remaining constantly engaged with pioneering empirical research on litigation and advocacy. Legal scholarship is increasingly relying on empirical legal research, and we remain connected to cutting-edge developments from leading legal scholars. 
  • Developing our own proprietary datasets and analytics. We have built and will continue to build our own databases—sometimes collaboratively with third parties, and sometimes by ourselves—that help us give clients the best advice possible based on real-world data. 


We view these tools as important complements to conventional legal analysis that can help us provide more effective advice and advocacy to our clients.

The projects listed below are some of our current initiatives to collect and analyze empirical data. These are not our only data-driven projects; we also undertake bespoke data collection and analysis projects to be able to answer specific questions that our clients face. We are also working on additional projects that we will launch publicly soon.

The Supreme Court of Canada Leave Project

Virtually every civil case that goes to the Supreme Court of Canada needs leave from the Court. The process of seeking leave can take months and give rise to additional costs. And most cases don’t get leave: in any given year, only between 5% and 10% of the hundreds of cases in which leave is sought end up being granted leave and heard by the Supreme Court.

Because of that, it’s helpful to be able to predict what the probability is of a case getting leave. Using a proprietary dataset containing information on over 1,500 leave application decisions spanning several years, we have built machine learning models that predict both the likelihood of getting leave and how long it will take for that leave decision to be released. Harnessing techniques from the artificial intelligence space, these machine learning models allow us to provide quantitative information to clients who are considering whether to bring a leave application or are responding to one. These models can’t replace our expert legal analysis as to whether a case raises issues of national importance, but they are a useful complement to conventional legal analysis.

While the data and models are proprietary, a general description of our approach and some of our general conclusions are contained here.

The Commercial List Project

The Commercial List of the Ontario Superior Court is one of Canada’s leading courts for commercial disputes. It hears many of the country’s most significant bankruptcy and restructuring cases, corporate disputes, and other commercial matters. 

Since the beginning of 2019, we have maintained a database of all new decisions of the Ontario Superior Court’s Commercial List that are published on CanLII. This dataset includes over 40 characteristics for each and every reported Commercial List decision. This data allows us to gain important insights relating to the work of the Commercial List, which complements our lawyers’ expert judgment and first-hand knowledge.

Our 2020 Commercial List Year in Review contains our most recent analysis of this data for 2020. This report describes our data collection method, the analysis of the results, and a review of significant cases. 

The Competition Tribunal Project

The Competition Tribunal is the federal administrative body with jurisdiction over broad swathes of the Competition Act. It hears all cases relating to mergers and unilateral conduct, including abuse of dominance, refusal to deal, resale price maintenance, and exclusive dealing. It also hears most cases brought by the Commissioner of Competition relating to misleading advertising, as well as certain cases relating to agreements between competitors. It is the primary adjudicative body for most types of antitrust and competition cases in Canada.

To analyze the work of this body, we built a comprehensive database of every single case filed at the Competition Tribunal, from the late 1980s onward. Our database includes over 50 variables for every case before the Competition Tribunal. This provides us with a rich dataset that allows us to systematically analyze various characteristics and outcomes of cases filed with the Competition Tribunal. We use this dataset to provide our clients with rapid and objective analysis of risks and potential outcome of enforcement action by the Competition Bureau.

Our 2020 report, Empirical Analysis of Cases at the Competition Tribunal, analyzes this data.