Sports data: why it can fall flat and why it doesn’t have to

Sport has never been more competitive. Today, every athlete, coach and team are tapping into data analysis to achieve the slightest winning edge over their rivals. There’s huge appetite for this, but is the analysis as effective as possible?

The context here is that the volume of data available to teams is expanding exponentially. If you take football as an example, as the amount of data increases, it’s becomes harder to analyse these millions of data points into something that can be quickly absorbed, tailored and shared to enhance teams’ performances and win more games. So instead, many teams only receive flat, statistical reporting that’s devoid of tactical context.

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This ‘flat’ statistical reporting and data is limiting for two reasons:

First, because data analysts are under increasing time pressures to produce new tactics and strategies.

With only two or three days before the next game, implementing different playing styles on a team can be challenging. This means that analysts more often than not dive deeper into flat reporting and video footage.

Where’s the time for analysis? Likewise, sports scientists feel they have to spend much of their time in raw data and this leaves less time for analysis and guidance.

Second, there is no guidance for those attempting to interpret the figures. Simply, the tools that provide the data outputs don’t provide interactive analysis that enables analysts, coaches and managers to better understand the opposition’s playing style and the impact of their own team’s style. This would enable better decision-making around the likes of team selection, tactics and training regimes.

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However, data can be segmented based on tactical situations and provide an understanding of how a team’s style will affect the opposition’s physical requirements. This can be done using a method called principle component analysis.

Principle component analysis, in the case of football, takes eight on-pitch incidents such as a dribble forward and reduces it to ‘playing styles’ for each team. These insights then allow analysts to classify teams into specific playing styles, for example the high press, counter attack or sustained threats.

Narrowing down the analysis into playing styles helps save time and provides contextualised actionable data. Simplifying data and adding context can help data scientists and coaches understand how an opposition’s playing styles will affect their player’s requirements. This then allows sport scientists, coaches and players to create evidence-based tactical training sessions and capture the tactical workload for each position.

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Does the fact that Manchester United or Chelsea have taken the highest number of shots this season really give valuable insight to an opposing coach to develop a strategy to stop that team shooting? The answer is no. Instead, insights in today’s competitive climate must take all factors into consideration to provide a clear and accurate picture of what happened and most importantly why it happened.

On average during a game, a footballer spends 97% of their time without the ball. That’s why it’s so important to know what is happening all over the pitch. Achieving the winning edge can only be done when the numbers provide context.

With context in place and algorithms that address data points on multiple levels across an entire game, scientists, coaches and players will have a more accurate predictor of what it takes to win and perform at the highest.


Sourced by Ryan Paterson, MD EMEA and APAC, STATS


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Nick Ismail

Nick Ismail is a former editor for Information Age (from 2018 to 2022) before moving on to become Global Head of Brand Journalism at HCLTech. He has a particular interest in smart technologies, AI and...

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