Sports and science have always been naturally intertwined. An athlete improves their ability to kick or throw a ball by practicing over and over again, each time analyzing the path of the ball so they can do it better the next time. In physics, the path of a projectile is studied so we can better understand how objects move in the air. We’ve analyzed the way we run so that we can run faster and faster as long as we’ve walked on two feet. The mechanics of running are analyzed across numerous fields of study. A scientific field and an athlete have a substantial central commonality: they study and analyze past performance to improve future outcomes. They strive to progress and grow.
However, in the past twenty to thirty years or so, sports and science have begun to cooperate like never before. Not only are we studying a player’s body in a medical lens to extend their career and aid in conditioning, but we’re studying the way their foot plants when they begin to run, we’re measuring the way their arm moves when they throw, and we’re using recorded stats to study and analyze a team or player’s performance.
The last example in that list is what we’ll explore here. By name, this analysis has come to be known as sports analytics. But despite it holding a namesake, the raging river of sports analytics is inseparably dependent on its academic tributaries for its speed and fervor. Since its birth, numerical sports analysis has been done using processes and practices from a variety of other fields, as most fields do when they first begin. When working with sports data, financial analysts use financial processes, data scientists use data science practices, statisticians rely on traditional statistical methods, and so on.
But now, sports analytics has reached a point where the river has carved a large enough valley into the earth that it can begin to foster its own ecosystem, separate from its tributaries. Sports analytics needs to truly become a science. One with its own principles, fundamentals, best-practices, processes, theories, and all of the other elements that come along with an application evolving into a separate field of study.
Achieving that next step in the development of sports analytics lies in the acknowledgment that the goal is to analyze sports. Not necessarily do statistical, financial, or physical analysis on sports information, even if that remains a vital part of it all. Analyzing sports requires a deep understanding of these games and how the data being worked with is connected to and leads to that understanding. That means looking at the data we’re given and breaking down what it is, how it can be used, what it means in a sports context, what it doesn’t mean in a sports context, and so much more.
Though, a science never reaches its final form. It is constantly improving and changing, and sports analytics is no exception. As the games themselves change and technology improves, this growing science will change and improve with it. So, most everything written here is subject to change, and be revised and improved. But the hope is that this serves as a beginning to the collection, unification, and systemization of the field of sports analytics.