Top 4 ways Big Data Science elevates sports

Big Data Science is much more than a buzzword. The ability of Big Data solutions in handling Big Data’s gigantic-volume, high-velocity nature has opened up so many opportunities for Data Science. One of these opportunities is in healthcare, which we’ve previously talked about.

Another opportunity is in the sports industry, which we’ll talk about in this article. Without further ado, here are 4 ways Big Data Science can be a game-changer for sports.

How is Big Data Science used in sports?
There are several uses of Big Data Science to sports. In this article, we list four of them.
Image Source: BBN Times

Hyper-personalised sports broadcasting

Professional sports presenters provide play-by-play explanations and discuss predictions based on data collected from sports matches.

Stats Perform, for instance, uses machine-learning tech to collect and provide insights to leading sports broadcasters like ESPN and Sky Sports for them to present pre-game predictions, in-game commentary, individual player projections, and so on. Maybe you’d be familiar with this if you watch sports channels on TV.

An example of using Big Data Science in Sports: Match Analysis K2 Panoramic Video with TrueView Visualizations.
Big Data tools can be used to analyse the players’ positions and movements in a match.
Image Source: MMAston

Big data science can also influence the content generated by broadcasters by analysing what consumers do and want. MyCujoo, a live football streaming platform, collects consumer data to personalise sports viewing experiences for fans.

Pedro Presa, MyCujoo’s CEO, said this about analytics for personalisation:

“By securing exclusive and multi-year rights to live stream different football leagues, we are best positioned to understand football consumer demographics, as well as consumer habits – for instance whether they watch highlights, or full games, or both.”

As we collect further data on the consumers, we are able to construct diversified offers for different markets and partner with various organisations through scalable business models.”

Another sports streaming platform, DAZN, analyses data to make decisions about which markets to enter, what sports to invest in, predict how much demand streams will have and figure out how to better serve clients.

For example, if DAZN finds that the typical Malaysian football fan persona only watches the English Premier League and avoids every other league including the one in his country, DAZN can use that insight to tailor the streaming content to mostly that of English football for the Malaysian user.


Accelerated training results through Big Data Analytics

In the same way commentators use data from matches, sports teams can also take advantage of such data to learn from their mistakes in matches and make improvements to their training. They can also examine the behaviour of their opponents to come up with any counter moves they can apply.

An essential part of a sports team is a coach since the coach’s main role is to guide athletes on the skills of a sport, among other responsibilities. For amateur teams, the coach may teach the foundations, rules and techniques of the sport through demonstrations and practice drills, so that new athletes can get the hang of the sport 101 before advancing to the next skill level.

A coach training a young baseball player.
Learning the basics of a sport would be very difficult without a coach to demonstrate the techniques.
Image Source: KeithJJ

Once athletes start competing, they’ll have the chance to learn what needs improvement and refine their techniques with the help of their coach. For this purpose, the coach studies team statistics and videos of past practice sessions and competitions.

Usually, the coach has to spend hours on painstakingly clipping game videos to bring attention to the strengths and weaknesses of teams and opponents.

However, using a sports performance analysis tool like Hudl can save coaches a lot of time since it enables coaches to conveniently upload game films to its platform, generate reports, and share feedback with their teams.

Hudl Sportscode to elevate your current processes with customizable coding and scripting tools that allow you to analyze what matters most to your team.
Hudl’s product Sportscode provides customisable coding and scripting tools for teams to analyse what’s most important to them.
Image Source: Hudl

Depending on the teams’ needs, such reports can show so many things about each individual player during a game like positions, movements, speed, reactions, shots, goals, passes, accuracy, dribbles, penalties, and tackles.


Data-backed player recruitment

Big Data also removes the guesswork or gut instinct when it comes to choosing players to accept into a team and figuring out where each player fits in the team.

One of the pioneering sports to use Big Data is baseball. In the late 1990s, Billy Beane, the manager of the Oakland Athletics baseball team, was facing budget constraints in replacing three of his top players who moved to bigger teams in the Major Baseball League.

With the help of a Harvard economics graduate Paul DePodesta, Beane applied the moneyball theory in mining big data to recruit underrated players, who would eventually win twenty games in a row.

In the Moneyball theory, teams can buy assets that are undervalued by other teams and sell assets that are overvalued by other teams.

In baseball, the undervalued asset is on-base percentage (how frequently a batter reaches base) while the overvalued asset is slugging percentage (how frequently a player gets extra-base hits – doubles, triples or home runs).

The on-base percentage was a significant factor for success but not for players’ salaries, which means they’re talented but cheap. So, Beane recruited players with higher on-base percentages while paying lower prices.

The Moneyball movie poster.
The Oakland Athletics story and the moneyball theory were popularised by Michael Lewis’ book Moneyball, which was later adapted into a movie by the same name.
Image Source: Wolf Gang

Now, we have another player in the big data-based recruitment game: Profile 90, a smart scouting platform that uses big data science to help sports clubs thoroughly assess a player before signing them up. And this includes the digital psychological profiling of players to understand them.

Profile 90 CEO Dr Jagdish Basra said, “Understanding and developing a player’s psychological characteristics will enable them to translate their talent into successful performance.”

Profile 90’s no-stone-left-unturned approach to talent identification and profiling optimises the efficiency of sports clubs, cuts the cost and time of player acquisition and increases player sales revenue.


Intelligent athlete recovery tracking and advancement

An athlete’s preparation is key to the athlete’s performance. Athletes are responsible for making sure that they have well-planned nutritious meals, sleep well at night, have sufficient energy to train and play, practice the right training and exercise regimes and, can tackle the mental challenges that accompany the world of sports.

Luckily for them, there are apps that guide them in taking care of all these aspects of their lifestyle and one such app is Inspire Sport Online.

Inspire Sport provides athletes with educational content about mental and physical health, nutrition, fatigue, sleep, training quality and so on, as well as daily holistic well-being tracking for coaches to understand the well-being of athletes.

The Inspire app helps an athlete keep track of all aspects of the athlete's wellness each day. This helps young athletes get to know and understand themselves better, and stay on top of their mental and physical health, what they put into their body, and more. The app allows them to input data on various aspects that make up their overall wellness; including mood, sleep, fatigue, sickness, female health, nutrition and training quality, and more.
Thanks to Big Data tech, athletes can keep track of their wellness using an app like Inspire.
Image Source: Inspire Sport

And just like the wearable trackers used in healthcare, wearable trackers can also be used in sports to monitor the athlete’s well-being, particularly fatigue.

Zephyr‘s wearable biomodule devices measure over 20 biometrics including heart rate, breathing rate, heart rate variability, heart rate recovery, posture, peak acceleration, impact, jump height and flight time, caloric burn, body temperature and training intensity.


Breaking down barriers between geeks and jocks?

Who knew that Big Data Science could bring good news to sports? Thanks to the work of Data Scientists in making sports easier to manage objectively with their skills and tools, sports broadcast content can be enhanced, clearer training feedback can be provided to athletes, talented athletes can be identified and athletes can know how well they’re taking care of their own health.

To end this article on a sentimental note, let me quote someone who knows why sports means so much to so many people. American football junkie Stix Symmonds poured his heart out in his article on Bleacher Report:

Sports will likely never solve any of the world’s greatest problems.  Wars won’t be averted over a simple game of football or a rousing match of cricket.  Diseases won’t go away just because we compete in America’s pastime.  Hunger won’t subside because two people entered a tennis court and dueled it out over several sets.  Life doesn’t work that way.

However, sports can do more than entertain or provide an escape.  They’re more than just a way to pass the time.  They are a microcosm of the struggles we face every day, played out in a fashion that leaves few lasting damages.  They’re hope, inspiration and life-lessons, all rolled into a contest that means nothing on the grander scale. 

Just because the outcome may mean nothing on the grander scale however, doesn’t mean that a game is ‘just a game’.

Sum-up FAQ

What are examples of how Big Data elevates sports?

– Big Data Analytics and machine learning is used to personalize and optimize the broadcasting experience.
– Patterns of successful training are mined and get used to optimize the training results of other athletes.
– Analytics can support data-driven player recruitment to get the best suitable players for the need of a team.
– Recovery times, health, and routines of players are tuned through fine-grained data tracking and measurements.

What has Moneyball to do with Big Data analytics?

Sports clubs can apply the “Moneyball theory” in mining big data to determine who the underrated players are and recruit them. Ultimately, a club can leverage this intelligence in negotiations to invest the money into strategies with the best statistical likelihood of winning future games.

What are the benefits for sports broadcasting companies to use Big Data?

– Optimization and hyper-personalization of advertisements.
– Insights of viewer interests to align content to customers interest.
– In or pre-game predictions and analysis to give the fans additional value.

How can wearables and Big Data improve athletes’ success?

Preparation is key to the athlete’s performance. With wearables, every small thing of an athlete can be tracked, analyzed and optimized. Ultimately, tracking the athlete completely gives a more complete picture and optimization of potential. Once the athlete is perfectly prepared, s/he can train at his/her maximum and is more likely to win the game.

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