The Evolution of Sports Analytics
6 minute read · Issue Number 64 · April 16th, 2021
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Today’s technology in sports enables scientists, staff, and coaches to monitor athlete performance like never before.
Primarily to enhance performance and mitigate the risk of injury.
One critical element in the development of solutions and improvements in sports is data analytics – with it, we measure, track, and evaluate what we do, with the sole objective of doing more of what works and less of what doesn’t.
In today’s edition of the magazine, we’ll discuss the evolution, value, challenges, and future of sports analytics.
Sports analytics is nothing new — for a while now, scientists have been trying to find correlations between characteristics that dictate performance to predict and prepare athletes for competitive success.
A few recent years ago, the methodology to gather data was manual and old-fashioned, which was time-consuming and expensive.
Today, innovation and technology have created non-invasive sports performance measurements, facilitating a shift in sport science and performance analysis from laboratory-based testing to field-based monitoring.
Now, we can collect immense amounts of data during training and competition, providing real-time insights into various aspects of sports performance.
You may be thinking, "Ok, Ronen, but why is it important anyway?"
Computers, machine learning, and AI are coded to not only describe and diagnose the data faster than a human mind but they’re also able to create predictive models that go beyond the spectrum of the human brain’s capabilities.
Sports analytics went from capturing and monitoring performance to efficiently process vast volumes of data and predict athletic and competitive performance before or during the event.
In short, we use analytics to make better decisions.
In sports, players, coaches, and management staff make dozens of decisions at all times. Through analytics, this process becomes faster, easier, and more reliable.
The following diagram illustrates the evolution of analytics.
One thing I found fascinating is the way the question evolved from “what happened” to “what will happen” — an outright challenge to a quote that my grandpa always says:
“Past performance is not indicative of future results.” –… until now?
In sports, we have moved from traditional retrospective analyses that provide low-to-moderate-level insights to the high-level competitive advantage afforded by predictive modeling and machine learning.
One of the biggest problems in almost all markets is the ability to serve the data. Sometimes decisions are not made only based on data.
Analytics is a model where you basically connect the dots and find numbers that support each other. Sometimes you may be connecting the wrong dots.
Even though we may monitor every aspect of an athlete, the output could be detrimental if the algorithm is wrong.
Other challenges in sports analytics?
Storage and security
Privacy (i.e., who owns the data)
Sufficiency (i.e., how much information is enough)
Today, sports organizations are building or using external software for data management systems to conduct the different analyses they think they need.
Live custom dashboards and reports deliver essential information and alerts to staff and coaches, providing a complete overview of an athlete’s or team’s performance at any given time.
At the predictive stage, with the development in analytics, machine learning, and AI, I’d expect every organization in sports (at least at the highest performing levels) to have analytics as an integrated priority. Else, I can guarantee they’ll be left behind, instantaneously.
🎙 Halftime Snack of the Week
SportsTrace is a company that provides data and insights to coaches and athletes in a digestible format to improve the mechanics of their movements.
In our conversation, we snacked about Microsoft, computer vision technology in sports, product management, data, and so much more.
Check out the transcript of this conversation here.
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Until next week,
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Halftime Snacks Podcast