Home Strategy On Science & Data In Sport Part 1: The implications of data science for elite sport.
On Science & Data In Sport Part 1: The implications of data science for elite sport.

On Science & Data In Sport Part 1: The implications of data science for elite sport.

1.31K
0

“Where there is a wealth of information, there is a poverty of attention” – Herbert Simon.

In 2003, award winning non-fiction writer Michael Lewis published ‘Moneyball’. In his book, Lewis tells the tale of Billy Beane, the cash strapped manager of the Oakland A’s who orchestrated the longest winning streak in pro baseball history, pioneering the use of data science and modeling in sport.

Along with others in elite sport, I took a deep interest in the book’s ideas. Digging deeper into literature and history to understand how this new technology can and is impacting outcomes in elite sport, and the opportunities and challenges it creates for leaders, coaches and athletes.

New Technology?

Simply defined, technology is anything that allows us to do more with less. When framed in this manner, technology as a concept in sport has been used for thousands of years.

Greek Olympians of the Ancient era had their training and nutrition planned by physicians, Bill Bowerman of Nike fame created new technology with his waffle shoe sole, and larger racquet heads in tennis changed the game.

Though technology in sport is far from new, Information technology and the skillsets (such as data science) that come with it is still in its infancy in elite sport and for many teams it has yet to understood or used effectively.

The challenge then for leaders of ambitious organisations in sport is how best to respond to the coming tidal wave of technology in a manner which creates a competitive edge without detracting from core objectives.

The highly competitive nature of the sports industry has led to an emerging trend of pro sports teams and professionals relying more and more heavily on data science and data scientists.

In 2002 AC Milan invested millions to design and build the state of the art Milan Lab, a sophisticated science and research facility designed to extract vast quantities of data related to every athlete and predict injury before it occurs.

In 2014 Super Rugby Premiers NSW Waratahs partnered with IBM to combined data science and modeling with a new movement diagnostic tool called Sparta Trac to better manage their athletes.

It seems every year there is there more technology creating more data, so much so that demand for database software as service companies such as Smartabase has exploded and created a whole new industry within the broader sports context.

As a physical performance manager I now access and interpret thousands of data points each tied and tagged to every athlete I am responsible for daily. The crazy thing is many of these advances have been made in the last five years.

Of course, this all makes perfect sense when you consider the rising costs of athlete salaries, match payments and bonuses combined with the inherent risk of injury that comes with increasing exposure to physiological demands at or close to ones limits.

It can only be prudent to try to use whatever means available and legal to find a competitive edge, and attempt to mitigate as much risk as possible to the most valuable assets in any sporting organization, its athletes.

This new technology related data and data science has given rise to a new kind of expertise, and new questions like:

  • What are the implications of data science for elite sport?
  • How is data science and the technology that enables it best used in sport?

This two part series article attempts to answer these questions in order to stimulate intelligent discussion around the subject within teams and among industry professionals.


The Implications of data science in elite sport:

In his groundbreaking book ‘Good to great’ Jim Collins identified ‘technological sophistication’ as one factor that consistently differentiated the best-performed corporations from their competitors.

Yet Collins is quick to qualify his assertions by explaining that great organisations are rarely early adopters of new technology but carefully considered in choosing what and when with regards to technology.

Collins goes so far as to suggest that the best organisations and the people within them have a healthy disregard for technology, and rarely credit it with any significant influence in overall success.

Collins writes:

“Thoughtless reliance on technology is a liability, not an asset. Yet when linked to a simple, clear and coherent concept rooted in deep understanding – technology is an essential factor in accelerating forward momentum ”.

But of what relevance is this in elite sport?

In truth, elite sport is almost a decade behind big business and many other industries when it comes to data science. In many ways big organisations built these tools to improve processes and outcomes by using technology to better describe their environments and variables within them.


Probability & Psychology

Data science in elite sport is the new shiny tool that many of us are currently besotted with. The promise of big data and predictive analytics is that we will be able to accurately forecast the incidence of injury or the suitability of an athlete for selection and intervene proactively to ensure the outcome is a desirable one.

However data science and its findings can only ever really point to correlation, not causation. Yet as humans we find it quite hard to distinguish the two, and our evolutionary instinct is always to take the easy way out by ‘trusting the numbers’.

The problem is, the numbers don’t tell us what is going to happen, they only offer us a closer look at the probabilities of events that MAY occur. Where we get into trouble is in the way we treat the numbers, like gospel.

Daniel Khannemans book ‘Thinking fast and slow’ describes two ‘systems’ we use for decision making; system one and system two. System one is fast, automatic, instinctual and gullible. System two is slow, deliberate and calculative however much more effective in decision making.

As humans we have a tendency to lean on system one because it requires less energy and concentration to do so, allowing us to preserve our reserves for dangerous situations that may require action.

We look to preserve energy because we have evolved to be highly motivated to avoid pain or the possibility of painful situations (like making a bad decision). This instinct is called loss aversion, and up until recently it has been our most important survival mechanism.

The combination of these two very human habits; being lazy in critical thinking and instinctively avoiding pain or situations that might cause pain, mean that we frequently make mistakes when it comes to interpreting and acting on data.

The danger that data science presents is that if we the users are not aware of our own limitations and our inherently human flaws, little can or will be done to mitigate them.

This will cause us to rely too heavily on technology and ignore or overlook information from alternative sources while unconsciously moving away from crucial aspects of our work. Consequently we will actually be more prone to error than we were without this technology.

In the Justice system we can see how the use and the users of data science has evolved in this way, at first it was used effectively and ethically, however more recently these tools are increasingly being utilized in troubling ways that significantly impact peoples lives.

As far back as 1990s the police force used data science to make decisions on where it would deploy its resources based on past trends and new information. Seems like smart use of resources right?

Fast forward to the present and we can see where our evolutionary instincts can lead us; Judges are now using data science to determine whether a felon has been properly rehabilitated and should be released back into society.

If this seems like a good idea to you, it’s probably because you have confused correlations with causation, or you too have fallen victim to your evolutionary instincts. The implications for incarnated people with the certain background experiences and poor socioeconomic status could be damming.

In 2002 Tom Cruise starred in the action drama Minority report, in the movie Cruises character was tasked with arresting people who were allegedly about to commit a crime. Just over ten years later and we are seemingly not far from that reality.

In sport, the dangers are less severe but still worth considering. By mistaking correlation with causation and relying too heavily on technology we may never have experienced the greatness of athletes such as Tom Brady who’s poor showing at the draft almost saw him lost to the game.

Should we fall prey to these flaws as managers, coaches or trainers we are less likely to be aware of those small but crucial observations and interactions that provide additional ‘data’ for decision making.

Possibility and mastery are the very tenets that sport rests upon. Sports stars symbolize what is possible when one sets their mind to task and carries it out with discipline and focus.

American football fans watch Tom Brady not just because of what he has achieved, but because of what he has overcome and who he has become in the process. This is what sport is all about.

By its very nature, the highest achievement occurs outside the bell curve, it is the outliers who defy the odds when people least expect it. Imagine what world we would live in if Roger Bannisters coach felt it necessary to educate him on the odds of his success?

Science can help us explain the world, and the best minds have helped advance humanity through the scientific method, but we must remember that the ultimate scientific instrument is the human mind. It was after all Einstein who said: ‘Look deeply into nature and you will understand everything better’.

Polynesians learned to sail hundreds of years before Columbus famous journey and the invention of instruments like the compass. Using deep observation they understood patterns in the direction, temperature and strength of the wind, the colour, shape and content of waves and water its currents and contents.

So advanced were their methods, that when the Spanish first discovered their culture they simply could not comprehend or accept that such primitive people could be technically superior navigators of wind and waves. As a result the superiority of Polynesian people as sailors was ignored for centuries.

So what does all this mean?

The future of data science and technology in elite sport is dependent on its users, like most tools it can be used wisely by well meaning people, or it can be used poorly by people who fail understand themselves and fall into lazy ways of being.

The tool itself is not good or bad, but the way it is used can result in either.

Having now explored the implications of data science in sport, the next post in this two part series covers how to use these new tools effectively in order to create a competitive advantage.

 

Popular

Popular