How Data Science is driving the word of professional sports
If you’ve ever wondered how to win the most competitive football league in the world with almost 20% of the season left to play and a 36%-point margin, here’s the recipe: a talismanic manager, superstars in the starting line-up and tactical brilliance using a bucket load of data science.
The story of Liverpool Football Club comprehensively dismantling all competition to win 4 major trophies in the last year-and-a-half, including its first English first-division League title in 30 years, may be one for the history books, but this extraordinary success wasn’t by chance. It was the result of careful curation of every aspect of their game and establishing ‘Pitch Control’ – a novel data science-based style of play perfected by those at the club, to make it the most dominant club in world football today.
The power of AI and Machine learning has traditionally found widespread application in sales, marketing, healthcare and finance, among several others. But the brilliance of this technology lies not only in this, but in the pliability of genre that it provides. Ever since Moneyball (2011) – where Brad Pitt eulogised data analytics by using it to build the most competitive team in the league on a minimal budget – sports analyses using advanced machine learning and analytical methods has taken over almost every major sport around the world. Big data has essentially become an unflinching pillar in the world of professional sports over the past few years.
IMAGE: Liverpool FC employing ‘Pitch Control’ (source: Liverpool FC)
The two major aspects of sports analyses – on-field and off-field – have both been revolutionised. Artificial Intelligence uses a plethora of technologies built upon specific test parameters to continuously gauge each player’s on-field performance. This reveals valuable insights regarding the individuals – and the overall team’s (such as in football or basketball) – tactics, fitness levels and performance pattern. Big data has also brought improvements in draft selections and game day decision making, allowing managers increased flexibility and team control, and consequently earning a seat for data managers right next to the coach’s chair.
In fact, even if we discount their on-pitch brilliance, Liverpool FC boasts of one of the most decorated benches of statisticians and data scientists continuously analysing every aspect of their game as well. Off-field analytics has also allowed team owners and managers to dig deep into the business aspect of sports including ticket and merchandise revenues, fan engagement, social media and the like, ensuring they can maintain trajectories of continued growth and increased profitability.
Sports analytics using AI has now become so very indispensable to the world of sports, that several organisations have even started offering courses on it – promising a rather fruitful career ahead. AI and machine learning have the unique ability to identify specific skills among players that could improve team balance or lend the edge needed for a team to succeed.
Mathematical models using identifiers to predict the player’s abilities and leveraging past data to forecast their future potential is something that has already been used in several video games (such as the FIFA series; check this for an interesting user-defined analysis) for decades now; but using it in real-life analysis is slated to be absolutely crucial going ahead.