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Analysis vs. Analytics The Age of Performance Intelligence Part II


Stephen Smith


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We say football, you say soccer. We say boot, you say cleat. We say training, you say practice. We say knockouts, you say playoffs….

The global vocabulary of elite sports is filled with synonyms such as these, which are indeed synonyms. But some words that are thought to be synonyms are actually quite differentAt the top of my list? ANALYSIS and ANALYTICS. Although often used interchangeably, the differences are significant and enormously impactful. While it is true that the intent of the words – finding answers to questions or justifications for a hypothesis  – is typically the same, the approach and outcome are typically miles apart.

As we enter what I am calling the Age of Performance Intelligence, understanding the difference between analysis and analytics — and most importantly how they are used to identify the drivers of elite sports performance — is key to recognizing how to leverage these concepts within your own organization.  

The Perils of Synonyms

From a data scientist’s point of view, “analysis” is generally defined as the separation of a whole into its component parts, whilst “analytics” is about the discovery, interpretation, and communication of meaningful patterns in data. Those two simple definitions provide valuable information about the role and value of both analysis and analytics within a sporting context — but also begin to provide us with context about the perils of thinking they mean the same thing.

Analysis: Slicing and Dicing the Past

If analysis is the separation of a whole into its component parts, it can be easily understood that the value of analysis within the context of sports data is the construction of visualizations and reports to document and dissect the detail of games, training, testing, etc. Analysis inherently deals with what has already happened — reports and visualizations are convenient and powerful views into the past.

With modern data collection and reporting solutions like your typical Athlete Management System (AMS), one can slice and dice and display data in many ways in order to try and make sense of the various elements of performance over different periods of time. The better the data set, the more detailed the picture of past performance that can be created. 

The Inherent Limitations of Analysis

Examining past performance is surely valuable, as evidenced by the mountains of reports that teams generate daily in their pursuit to find meaning in their data. However there is a limit to how much one can rely upon the connections found in data using the techniques of analysis.

Analysis often begins with a preconceived concept about what is happening. For example, over the last 15+ years athletes have become noticeably bigger, faster and stronger — and our games are played at a noticeably increased intensity and physicality. Therefore it is reasonable to ask,“Is this increase in high-speed running causing an increase in risk for injury?” and then set out to analyse patterns to confirm that hypothesis. But how do you avoid concluding that the increase in high-speed running is the sole reason, or perhaps is the most significant factor, increasing the risk of injury? Once the pattern and correlation in the data are identified through analysis, have we uncovered the truth about injuries? Are we ready to take our findings and start making changes? 

We need to embrace and accept the complexity of the problems we are trying to solve.

The problem with analysis is that it strips away the complexity needed to find meaningful patterns. Analysis tends to paint simplistic pictures that show relationships in isolation which can often be misleading.

Analysis is the separation of the whole into the parts to simplify finding an answer, and is often led with a preconceived notion about what is happening or what the answer is.


We know that athlete health and performance are impacted by many factors from weather, to diet, to sleep patterns, to injury history, physical profile, training load, recovery and a myriad of other factors large and small, — so how do we determine which factors to eliminate? 

The answer is that we need different tools to examine these types of questions in sports.  We need to consider more factors and better understand a broader range of inputs that may have contributed to an increased risk of injury.

This is where analytics comes in.

Analytics: Embracing Complexity

Unlike analysis where complexity is reduced in order to show patterns, the tools and techniques of analytics respect, understand, and embrace the complexity of the data of sports.

Performance coaches and data scientists no longer need to eliminate data from their calculations based upon a single hypothesis. Instead, they can use analytics to take advantage of all of the data they have and discover the true drivers of health and performance

Lets take a look at the difference between analysis and analytics in practice.

Analysis starts with a hypothesis about what is driving an aspect of performance or what might be the risk factors related to a type of injury. For example, a coach thinks that the increased intensity in practice and games (e.g, high intensity running, sprinting, acceleration, deceleration, etc.) may be contributing to a rise in hamstring injuries. Time is spent ripping spreadsheets apart, layering reports on top of each other, and racking & stacking visualizations — all in an effort to extract meaning from the past and apply it to the future.

After running all of the reports and looking for patterns, all too often teams are left in a predictable scenario —  after sifting through enough metrics and calculations, there is a particular metric that appears to have increased in the build-up to an injury. The immediate assumption is that this isolated risk factor is the likely cause of this injury and that management of this type of increase in the future will reduce the risk of injury. Whilst, it is understood across the industry that the problems we are solving are complex and the solutions cannot be this simple, the reality is that it has often been the only way we can try to approach these issues as we are trapped by analysis-based toolsets.

Instead of focusing on a specific hypothesis,  (the relationship between specific high-intensity metrics and hamstring injuries), analytics takes a holistic approach looking at all potential factors and underlying patterns to determine the true drivers of hamstring injury risk such as:

  • each person’s strengths & weaknesses
  • their current physical state
  • unique travel schedule
  • sleep cycles
  • stress levels
  • Mood
  • Age
  • Game scenarios
  • Physiological and psychological freshness and or fatigue

(to name just a few). 

More than Pattern Recognition

Analytics accounts for the complexity of human beings and our sports by allowing us to understand how the accumulated risk of a myriad of factors, from our training plans, recovery strategies, and each individual’s unique profile, contributes to their susceptibility of injury. The picture analytics paints for us is a richer insight into how risk doesn’t just emanate from one specific factor (high-intensity activities) but is the accumulation of many different risk factors that marginally or substantially increase an athlete’s risk at any given time.

Analytics takes into account the complete picture and the interplay between different factors that increase or decrease risk and increase or decrease performance.

For example, an increase in games and minutes played combined with a reduction in sleep quality, a change in movement patterns based on a knock picked up in training, a player’s history of injuries, and a sustained high volume of high-intensity practice — all combine to increase hamstring injury risk. Coaches can see that it is the perfect storm of all of these events occurring simultaneously that causes the increased risk, not any of them in isolation. Whilst analysis can identify patterns, that does not mean they are meaningful. 

Analytics uncovers significance in our data and identifies the truly meaningful and complex relationships that we can leverage to make a difference.

The Takeaway

I am not suggesting that analysis lacks importance, but I’m highlighting that analysis alone paints a simple and static picture of a complex and dynamic environment. 

Having spent so long talking with coaches all over the world about this, it seems we all come to a common conclusion:

  1.  We want a simple way to find meaning in our data
  2.  We want an effective means to leverage our data
  3.  We only want to focus on what truly matters

My utopian vision is one where analytics and analysis don’t compete with each other, they compliment each other. Analytics helps us to focus on what matters and how to impact our performance outcomes, whilst analysis can help us to operationalize our data and leverage it in real-time. 

Harmonizing analytics and analysis helps us to better understand the dynamic nature of sports and the complexity of the problems we are trying to solve. Success within the Age of Performance Intelligence relies on the use of both analytics and analysis and the first step in that journey is for our industry to recognize that they are truly different — and for us to develop a common understanding across high-performance teams of what that means. 



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  • Stephen Smith

    Stephen is Kitman Labs’ CEO and founder. He was previously Senior Injury Rehabilitation & Conditioning Coach at Leinster Rugby Club. Stephen holds a BSc in Sport & Exercise Rehab and MSc in Football Rehab from Edgehill University.

    CEO & Founder of Kitman Labs



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