“The frontier of analytics is just beginning and there is no end in sight to the potential”Dr. Lynn Lashbrook, Sports Management Worldwide President and Founder
Well gone are those days when chalk was used to put scores on the boards of stadiums. Pen and paper used by coaches to analyse the performance do not find a place in the sports kit these days. The tools of analytics have evolved over time and with technology taking a leap, the process of sports analysis has taken a different form and shape that was hard to imagine a few years back. Do you know that all the major clubs and teams have a dedicated analytics team nowadays? Let us explore when all of this started happening and how analytics is changing the way sports are being played.
Background of Sports Analytics surge
The first usage of analytics in sports is known to be in a baseball sport. Henry Chadwick, a sportswriter, developed a metric called the box score back in 1858. The box score presented the baseball player’s performance in a tabular form which helped the baseball statisticians measure players’ and team performance quantitatively. Till the middle of the 20th century, many others made unsuccessful attempts to show some real usage of analytics in sports. It was Bill James’ Baseball Abstracts, a collection of annual baseball data, which won the public’s attention in 1977. His abstracts became very popular and later, he coined a term called ‘Sabermetrics’ to define the science behind a baseball game. ‘Sabermetrics’ term is derived from SABR, which stands for Society for American Baseball Research. His first definition of ‘Sabermetrics’ is: ‘It is the mathematical and statistical analysis of baseball records.
Another striking event that brought sports analytics culture to the mainstream was the book written by Michael Lewis, Moneyball, in 2003. In this book, the Oakland Athletics General Manager, Billy Beane, focused mainly on analytics to build a competitive baseball team with a minimum budget to clinch American League West title. Later in 2011, Bennett Miller directed a movie, Moneyball, starring Brad Pitt, inspired by the same book. The film was a big hit on the box office and served as an eye-opener to many sports analysts. Since then, this field has been gaining popularity and many companies have realized the potential of this highly growing field. Some of the organizations started using data analytics in sports as early as the 1960s. Over the years, they have progressed a lot and are adopting the latest technologies to give a whole lot of new experience to sports.
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|Organizations that are known first for using data analytics in sports|
|ChyronHego||1966||real time data visualization of live sports telecast, news, weather|
|Hawk-Eye||2001||ball tracking with very high precision cameras|
|Synergy Sports Technology||2004||basketball analytics for the purposes of scouting, entertainment, and efficient operations|
|FocusMotion||2006||track motion of humans through wearable gadgets|
|Krossover||2008||generates insights from sports video clips|
These companies and many more are exploring the applications of sports analytics. Next question is, why sports analytics is becoming hugely popular?
We know that human beings have limited capacity to process and generate immediate insights from massive data present in raw form. If this data is processed to present in tabular or graphical construct, this helps us to observe trends and find valuable insights empowering our decision-making abilities. The latest technology and powerful machines have simplified this process of data analysis. For example, in sports, parameters like – weather conditions, recent wins/lose statistics of players are used to make predictive machine learning algorithms that aid managers in making game-winning decisions. Each win of a team speaks volumes and brings added perks like increasing fan base, attracting more sponsors, retaining top players, increasing merchandise sales, and getting concessions for high-quality sports equipment.
It also increases the team’s confidence and local pride. There is majorly two areas of study in sports analytics – on-field and off-field analytics. On-field analytics is a data-driven approach to improve player and team performance, whereas Off-field analytics studies the parameters that aid the rights-holders to maximize business revenues. Traditional Business Intelligence (BI) methods are now being replaced with more advanced analysis like sentiment analysis to understand and quantify fan (human) behavior. Also, much sophisticated deep learning and cognitive algorithms are used to predict future scenarios. That is why the sports analytics market is growing at a rapid rate.
Sports Analytics Market
A new study published by Grand View Research Inc states that the global sports analytics market size will expand at a CAGR of 31.2% and reach $4.6 billion by 2025. Sports analytics has contributed on and off-field and has also helped the gambling industry grow rapidly. The gambling industry is valued at around $800-$1,000 billion, out of which 13% share is of sports gambling. A huge amount of data and information help sports gamblers to analyze more and place the right bet. Many teams and clubs now have collaborated with big companies to develop analytical products that can help managers in their decision-making process.
One of the World’s greatest football clubs – Real Madrid, takes the help of Microsoft Analytical tools to manage its operation, performance, and relationship with 550 million global fans. Also, Manchester United trusts Aon for planning their game strategy to stay one step ahead in the competition. Some of the works of sports analytics have been so accurate that they have been written in the books of history.
There have been many instances in sports where analytics has performed outstandingly. One such incident is when Daryl Morey, General Manager of the Houston Rockets, an American basketball team, found three-pointer shot attempts from corners had a higher chance of going in than trying two-pointers shots. The result was that the Rockets broke the record for most 3-point attempts during NBA 2018-19 season. Also, ScoreWithData of IBM predicted seven hours prior to the first quarter-final of the World Cup that Imran Tahir, South African spinner, would become the power bowler.
And this prediction came out to be perfectly accurate and Tahir won the match for South Africa against Sri Lanka. Today, all professional sports – cricket, basketball, football, hockey, etc. – use analytics to maximize their team performance and enhance their chances of winning. All these sports use different kinds of metrics to measure players’ and team performance.
Statistics used in sports to measure the player’s performance
All sports are beginning to realize the potential of analytics but some are also critical of adopting this technology in sports.
Challenges for Sports Analytics
Though Sports Analytics is growing rapidly it faces many challenges as well. Sports analytics critics point out that there are certain factors that analytics is not capable of capturing, like player diving in the game, misleading the opponent, player yelling. They argue that such things can only be captured and processed by humans.
However, to a certain extent, analytics can still handle such kinds of unstructured data. Such things are documented and using text analytics models, and this unstructured information is converted into standard structured data with rows and columns for processing. Rules-based categorization or algorithm-based models are used to gauge the frequency of words and generate insights. The efficiency of these models can be improved by collecting more data from various sources. For example, using scouting reports from different scouts makes sense so that the results are not biassed towards one opinion. Nevertheless, with technological advancement, these challenges can be overcome.
Next in Sports Analytics
Sports analytics has led to a breakthrough revolution in the sports industry, but it still has a long way to go. The day is not far, with the integration of technology and wearables when analytics would assess the mental and emotional makeup of the player and how it correlates to the player’s on-field performance. Sports analytics has a lot of scopes and will evolve manifold in the years to come.