Sports Analytics: How Different Sports Use Data Analytics

According to the key data creation statistics for 2022, 2.5 quintillion bytes of data are created daily. With such amounts of data being created, more people have access to information that will enable them to make better decisions. Thus, it is no surprise that data plays an ever-increasing role in almost every industry. One such area that is thriving in this new era is sports analytics. 

Anyone interested in sports broadcasting knows the vast nature of the role of data and statistics in the sports industry today. An ever-growing number of people in sports are keen on embracing the power of analytics to attain any statistical edge they can find, giving rise to the sports analytics industry. 

What is Sports Analytics? 

Sports analytics is the study of athletic performance and business health to optimize the processes and success of a sports organization. Three are essentially two components to sports analytics:

• On-field data analytics. This area involves tracking key on-field data metrics to influence methodologies that may be used to improve in-game strategies, nutrition plans, and other vital areas that could ethically boost athletes' performance levels. It seeks to answer questions relating to on-field performance, such as “which soccer player has created the most chances in Europe?” or “which player has run the fastest over 20 meters?” etc. 
• Off-field data analytics. Here, the focus is on the business side of sports. It involves monitoring important off-field data metrics such as ticket sales, merchandise sales, fan engagement, etc. This type of data analytics seeks to assist decision-makers in sporting teams make better decisions directed toward increased growth and profitability. 

Data analytics is vital in modern business. Several businesses in various industries have leveraged data analytics to assist them in optimizing their performance. By implementing data analytics into their business model, businesses now have enough information to help them make better business decisions and reduce costs more efficiently.  

Why is Data Analytics in Sports Useful? 

The global sports market grew from $354.96 billion in 2021 to $496.52 billion in 2022, according to the business research company's research, making it one of the biggest markets in the world. As spending in the global sports market increases, sporting teams are much more committed to investing in sports data analytics for a competitive edge, hence the projection of the sports analytics market reaching upwards of $4.5 billion by 2025. 

Some key benefits of data analytics in sports include: 

Informed decision-making

Sports data analytics can be a useful tool for making important strategic decisions. For example, when Thomas Tuchel, former Chelsea Football Club manager, was asked why he brought on goalkeeper Kepa Arrizabalaga late into extra time, he responded, “So we had some statistics, we were well prepared, that Kepa is the best in percentage in saving penalties.” He added, “There is proof that Kepa is better at this discipline.” Chelsea went on to win the penalty shootout, with Kepa saving two penalties in the process. 

Although it may have played out differently in the scenario above, decisions backed by data on and off the field typically lead to more powerful and accurate decision-making. 

Another example of this is in the NBA. Several teams, such as the Philadelphia 76ers, are leveraging intricate data analysis techniques, such as data visualization and hypothesis testing, to analyze NBA games to influence the coaching strategy. 

This type of analysis has had a profound effect on Basketball. More of the action is taking place at the three-throw line since teams have realized taking more three-point shots is worth the tradeoffs (teams may miss more shots, but when it goes in, they earn more points). 

Increased revenue 

Generally, companies investing in data and analytics can experience notable financial returns. According to research conducted by McKinsey & Company, the average initial increase in profits from big was six percent, which increased to nine percent for investments spanning five years. 

One area where sports teams are using data analytics to increase revenue is ticket sales. Setting ticket prices effectively is an important yet tricky part of sales. With data analysis, sporting organizations can get greater insight into key financials, which enables them to determine the best value price for customers and the organization. 

For example, Houston Astros used data analytics to run “what-if” scenarios to understand single-game ticket buyers with the hopes of converting them into season ticket holders while retaining the current season ticket holders. 

In another instance, analysis was employed to understand the trade-offs that fans make between variables such as seat location, food & beverage options, and other alternatives in the club section. The idea was to understand fans better so franchises could tailor ticket offerings to best meet the fans' needs in a specific section.

Data analytics in sports is also great for optimizing online sports retail revenue. Sporting teams are applying techniques such as data manipulation, aggregation, cleaning, and more to analyze product data to optimize revenue.  

The Development of the Sports Analytics Industry 

Sports and data analytics have been tied closely together for a long time. Baseball was one of the early adopters of sports analytics, with Earnshaw Cook publishing Percentage Baseball in 1964. However, several statisticians would likely credit the popularization of sports analytics to Billy Beane, Oaklands Athletics' general manager from 1997 to 2016.

Up until 2002, coaches and athletes in baseball tended to turn a blind eye to deeper data analytics in favor of instinctive gut feeling, a player’s aesthetics, or shallow statistics like batting average. Billy Beane, strapped with a minimalist budget, noticed that getting runners on base was vital for scoring more runs. With this enlightenment, Beane focused his strategy on acquiring overlooked starting pitchers – usually for a fraction of the price of a big-name pitcher –  that have a high on-base percentage with the idea that teams with higher on-base percentages were more likely to score runs. 

Beane’s success with Oaklands Athletics provoked other major league baseball (MLB) teams to take notice. In 2003, Boston Red Sox was the next MLB to adopt a similar approach. His strategy of optimizing a team with sports data analytics became known as “Moneyball.” But it was not until  2011, when the film Moneyball was released, that the term “sports analytics” became popular in mainstream sports culture.

Ever since then, every major sport has experienced its own analytics evolution. Several sporting teams are keen on gaining a competitive edge with data analytics and are investing heavily in hiring sports data scientists to analyze objectively on and off-field data.   

How Different Sports Use Analytics

Although the fundamental purpose of sports data analytics is universal – to gain a competitive advantage through statistics and data analysis – different sports deploy varying methods to collect and analyze data efficiently for their sport. 

Soccer

Sports data analytics forms an integral aspect of off-field decision-making in soccer. Soccer clubs worldwide have invested heavily in data science and related technology to help boost players' on-field performance and improve off-field decision-making. This involves tracking and monitoring data such as players' in-game positioning, fatigue during training, distance covered, and other data that may provide greater insight into players' conditioning. 

Other statistics may be collected to investigate an individual player's performance, such as successful dribbles, passes, and interceptions. Analysis of this data provides coaches and players with greater insight into the strengths and weaknesses of their game, which is useful for their development. You can read more about how data science is changing soccer in a separate article. 

Baseball

As one of the first sports to embrace sports analytics, Baseball has been setting the benchmark for several years. Some of the brightest minds in the game, like Theo Epstein, have never even set foot into a major or minor league baseball game. Instead, these people depend on their advanced education and passion for crunching numbers to help make decisions on and off the field.

Due to the in-depth collection of statistics collected in baseball over a long time, more people like Theo Epstein have flocked to baseball's sports data analytics community. A breadth of statistics is tracked to aid the MLB team's decision-making, such as:

• The batting average: A statistic calculated by dividing the number of hits by the number of at-bats for that player. This stat reveals a player's tendencies and which pitch tends to strike them out. The purpose is to aid players in identifying what type of pitch is their weakness – it is also the most commonly discussed stat in baseball. 
• On-base percentage: This is one of the most important offensive stats; It illustrates how often a batter can avoid being put out at the plate and is defined as the percentage of times a player reaches base on a hit, walk, or being hit by a pitch.  
• Slugging average: The slugging average measures a player's batting power. It measures the number of bases a player earns on hits. The higher the slugging average, the more likely the player is to hit for extra bases.

You can apply your knowledge of Baseball, Python, and data visualization with Matplotlib and Seaborn in our New Era of Data Analysis in Baseball project.    

Basketball 

Daryl Morey was one of the first NBA general managers to implement advanced statistical metrics as a key part of player evaluation. Now, most NBA has sports data analysts as staff on their teams. Their role is to support coaches and players with data to aid them in maximizing on-field performance and identifying undervalued players. 

At the most elite level of basketball, teams use data-tracking cameras at all angles of the basketball arena to track every movement made by each player on the court. This data is then synced with the players' statistics to provide a full breakdown of players' performance.

What Do Sports Analysts Do? 

A sports analyst will likely watch their designated sporting event, prepare commentary, and deliver that commentary on air or in print. Thus, they must have exceptionally strong analytical and communication skills as it accounts for a large proportion of their day-to-day responsibilities. In addition, sports analysts have a deep knowledge of their chosen sport, which includes a good idea of the historical context and key statistics that define the competition. 

How to Become a Sports Analyst

As the growth of the sports industry continues, a natural by-product is a wealth of job opportunities. Individuals with a knack for data analytics and a compelling interest in sports can merge their talent with their hobbies to build a fulfilling career as a sports analyst. 

Becoming a sports analyst typically comprises three key components: getting educated, developing skills, and gaining experience. If you’re wondering how to get into sports analytics, these steps can help you. 

Getting educated

The majority of sports analysts start as regular sports fans. However, upon deciding your goal is to become a sports analyst, you must absorb as much information about sports as possible. Sports analysts are often expected to be a kind of walking sports encyclopedia.

Additionally, statistics knowledge is essential. Statistics is the preferred language in sports analytics. It generally helps to pay close attention to current game outcomes, such as player statistics, win-loss records, and other available statistical information, to enhance your statistical vocabulary while learning what metrics are important for sports performance.

In the long run, education in these two areas will aid you in creating authoritative assessments and predictions based on statistical models.

Many sports analysts will take some form of sports data science degree, whether focused mainly on the sports side or the data science side. Of course, other types of education can help, whether in sports, statistics, data science, or related fields.     

Developing skills

Many soft and hard skills are required to become a sports analyst: 

Soft skills 

Becoming educated in statistics and a chosen sport is only one part of the process. The other part is to leverage the knowledge of statistics and combine it with the contextual information from your chosen sport to generate insights. Having a good relationship with stakeholders certainly goes a long way, as sports analysts are expected to understand the needs of stakeholders (i.e., coaches, directors, etc.) and deliver accurate insights in a timely fashion. 

How insights are delivered is also important. Stakeholders are likely less technical than the sports analyst, meaning insights must be reported in an understandable and useful format to simplify stakeholders' planning and decision-making process.

Hard skills

Anyone who works with data must be data literate and conscious – take our Understanding Data Science course to learn more. A key element of a data professional's work is ensuring data is accurate and reliable, which requires good knowledge of how the data is collected, stored, and retrieved. This also includes ensuring data is clean: it is often estimated that 80% of a data scientist's time is dedicated to data cleaning. 

You can put your data cleaning skills to the test in this importing and cleaning data project, which focuses on soccer attendance data during the 2019 FIFA Women's World Cup. 

Beyond the ability to read, write, communicate, and reason with data, sports analysts must also be equipped with significant technical knowledge of various analytics tools and programming languages. These sports analytics tools include Excel spreadsheets, SQL, R, Python, and Tableau.