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Let’s start with a clarification. Sport Performance Analysis (SPA) is not new. It has roots in the late 1800s, when coaches and media began methodically documenting and analyzing the actual actions, strategies, and results of athletes during live or recorded performances. Its focus is narrow and practical: to understand what happens in the performance, how actions influence outcomes, and how athletes or teams can improve.

In contrast, Sport Analytics is a more recent umbrella term that exploded in popularity with the rise of data science. It encompasses any analytical procedure applied to sport-related data, including—but not limited to—performance data. Think ticket sales, fan behavior, injury trends, organizational policy, broadcast engagement, even climate data for venue planning. If it’s data in sport, it’s sport analytics.

Sport Analytics Includes Performance Analysis

Here’s the catch: performance analysis is a subset of sport analytics. Every modern SPA process uses analytical techniques—ranging from simple statistics to complex machine learning models. However, not all sport analytics deals with performance. A marketing campaign analysis or a predictive model for fan engagement has no link to what the athlete did on the field.

In short: All SPA uses analytics, but not all sport analytics is SPA.

Why Data Scientists Should Start with SPA Foundations

If you’re a data scientist entering the world of sport performance, here’s a key recommendation: learn the domain. The best practices in analytics, regardless of sector, emphasize one principle above all: understand your data and the context that generates it.

This means two things in sport:

• Get familiar with the sport itself—its rules, flow, and tactical structure.
• Explore the rich academic field of Sport Performance Analysis, which has decades of peer-reviewed knowledge, validated observation models, and frameworks.

Diving into SPA literature is not just academic—it’s practical. You’ll discover:

• What variables matter in different sports (e.g., “possessions” in basketball vs “phases” in rugby).
• Established notational systems and observational models used by elite coaches.
• Common pitfalls in interpreting isolated performance data without contextual framing.

Rather than starting from scratch or reinventing models, leverage this knowledge to accelerate your analytics journey. You’ll not only produce more relevant insights—you’ll position yourself to innovate.

Final Thought

Sport Performance Analysis and Sport Analytics are partners. One is the focused study of what athletes do in their field of play; the other is the toolkit that analyzes everything about the sport ecosystem. Respect the roots of SPA, embrace the power of analytics, and you’ll be better prepared to create impact—on and off the field.