Debiasing Talent Decisions in High-Performance Sports
The Big Problem
Every sport has its surprises. A fifth-round draft pick becomes a Stanley Cup MVP. A so-called undersized forward builds a dynasty. A once-ignored prospect turns into a global icon. We celebrate these stories as proof that grit beats odds—but what if they also reveal how unpredictable talent really is? Talent identification isn’t necessarily struggling because of missing data. It’s constrained because even the best minds can’t fully escape the biases that shape human judgment under pressure.
Scouts, coaches, and general managers make choices in rooms thick with hierarchy and history. Analytics were supposed to bring objectivity; however, even with models and metrics, human judgment still decides who gets a shot and who’s left unseen. When millions of dollars and future contracts ride on a single call, people fall back on what feels safe, familiar, or culturally “right,” often without realizing how cognitive biases shape that instinct. To make progress, we need to coach bias out of both humans and machines—training decision-makers and the algorithms they trust to see talent more clearly.
About the Author
Maryam Sorkhou
Maryam holds an Honours BSc in Psychology from the University of Toronto and is currently completing her PhD in Medical Science at the same institution. She studies how sex and gender interact with mental health and substance use, using neurobiological and behavioural approaches. Passionate about blending neuroscience, psychology, and public health, she works toward solutions that center marginalized populations and elevate voices that are often left out of mainstream science.
