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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 Decisions Project

The Supreme Court of Canada is Canada’s highest Court. While lawyers across the country carefully read and parse individual decisions, there has historically been limited quantitative analysis of its appeal decisions. We hope that this project will change that.

We were provided a dataset of every Supreme Court of Canada decision from 1954 through 2013 that had initially been prepared under the supervision of Professor Ben Alarie and Andrew Green of the University of Toronto Faculty of Law. We validated and cleaned the historical data, and we also updated the dataset to the present. Our dataset now contains dozens of datapoints about every Supreme Court of Canada decision, including information about appellants and respondents, history of the case, the case disposition, the issues in the case, and judges’ votes in each case.

We use this dataset to enrich our expertise about the Supreme Court of Canada. Quantitative analysis of the Supreme Court’s decisions provides a useful complement to close textual analysis.

The Supreme Court of Canada Decisions project is unique among our projects, because we make our entire dataset public and available for anyone to download and use as they see fit. We hope that this dataset will provide a useful resource for everyone interested in the Supreme Court, including academics, practicing lawyers, law students, and legal technologists.

You can access the dataset and associated coding manual here:

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.


Click here to review the latest leave application decisions that the Supreme Court of Canada will be releasing and our model’s predictions for the outcomes of those cases.

The Ontario Court of Appeal Project

For most cases that are started in Ontario, the Ontario Court of Appeal is the last stop. Our lawyers have a wealth of experience litigating cases at the Court of Appeal, and now we’re complementing that with two new datasets that helps us analyze cases from the Court of Appeal.

One dataset is a hand-coded dataset containing information about every Ontario Court of Appeal decision from 2020 onward. Our dataset contains a wealth of information about each case, helping us understand the factors that impact the likelihood of success on appeal.

In parallel, we are also working with Professor Wolfgang Alschner of the University of Ottawa Faculty of Law to build tools to automate the collection and analysis of data pertaining to more than a decade of decisions from the Court of Appeal. These efforts will provide us with insights as to long-term trends and developments at the Court of Appeal.

The Federal Court of Appeal Patent Cases Project

Patent disputes are high-stakes, complex matters. While trials and summary judgments are a milestone, they are seldom the end of the road. Whether it's a patent infringement action, a patent impeachment action, or a proceeding under the PM(NOC) Regulations, an appeal is always likely. Understanding how those appeals unfold is important to our clients.

That's why we have prepared a database of every substantive decision of the Federal Court of Appeal in patent disputes from 2000 onward. Our database includes approximately 30 characteristics of every appeal decision. This dataset allows us to provide our clients with benchmarks for the likelihood of success on different types of appeals and the timelines for resolution of appeals, among other things.

Our 2022 Year in Review: Patents guide contains our analysis of this data, as well as commentary on recent developments in patent case law.

The Class Actions Project

Class actions are seldom simple or straight-forward. They often raise important questions of public policy, and they can have significant ramifications for both plaintiffs and defendants. To successfully litigate a class action, you have to know the ins and outs of class actions practice.

Lenczner Slaght is working together with the Windsor Law Class Action Clinic to develop and expand their Class Action Database. Initially developed in conjunction with the Law Commission of Ontario’s 2019 report on Class Actions, this database is intended to be a comprehensive dataset of class actions in Ontario, which will help advance our knowledge of, and give us unique insights into, class actions practice. In turn, these insights will help us provide expert advocacy and advice to our clients.

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.