The Evolution of Data in Sports: From Scorecards to Big Data
A focused learning path built around real broadcast journalism skills — structured for clarity, paced for retention.
Program structure
Course Modules
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Paper and Memory
The origins of record-keeping in baseball, cricket, and football. We look at what was tracked, what was ignored, and why those choices shaped the sports themselves.
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The Spreadsheet Years
How personal computers changed what a small front office could do. Students recreate a 1987-style team evaluation using only basic formulas and discuss its real limitations.
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Databases and the Sabermetrics Moment
SQL fundamentals applied to historical player data. We examine the statistical arguments that challenged conventional wisdom in professional baseball and how those methods spread to other sports.
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Video Tracking and Spatial Data
Optical tracking systems, heat maps, and the geometry of movement. Students analyse positional datasets from soccer and basketball to identify patterns a human eye would miss.
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Wearables, Sensors, and Real-Time Pipelines
GPS vests, heart rate monitors, and accelerometers. We cover data collection protocols, injury-load modelling, and the ethical questions around athlete monitoring.
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Big Data Infrastructure in Sport
Cloud storage, streaming ingestion, and machine learning basics applied to sport contexts. Students build a small prediction model using publicly available match data.
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Communicating Findings to Non-Analysts
Visualisation principles, dashboard design, and how to present a data-driven argument to a coaching staff or front office that did not ask for one.
Assessment and Completion
Each module includes a graded assignment. A final capstone project requires students to trace one analytical question across at least three historical eras using real data. Completion certificate issued on passing all assessments.
One-time payment. Lifetime access to all materials, exercises, and future updates.
Get in touch to enrolWhat's included
What you are stepping into
Broadcast journalism takes time to learn well — this program is built around that reality, not around shortcuts.
Not long ago, a coach's best tool was a clipboard and a sharp memory. Tracking a player meant watching film twice, scribbling notes, and trusting your gut. That world still exists in youth leagues, but at the professional level it has been replaced almost entirely by sensor data, computer vision, and predictive modelling.
This course walks through the full arc of sport analytic, starting from the paper scorecards of the early 20th century and moving through the spreadsheet era of the 1980s, the database revolution of the 1990s, and into today's environment where a single basketball game can generate millions of data points per second. The Evolution of Data in Sports: From Scorecards to Big Data is not just history — it is context that makes modern tools easier to understand and apply.
You will work with real datasets, including publicly available pitch-tracking and GPS movement records, so the learning stays grounded. Expect to spend time in Python and SQL, though no prior coding experience is required — we build from scratch and explain each step.
Practical assignments connect historical shifts to present-day decisions: how did the introduction of pitch count data change pitcher management, and what does that tell us about adopting wearable sensors today? Those kinds of questions run through every module.
This course suits analysts, coaches, journalists, and anyone curious about where sport data came from and where it is headed next.