In the ever-evolving landscape of professional sports, a new player has emerged, revolutionizing the way teams strategize, perform and make crucial decisions. Enter sports analytics, a dynamic field that harnesses the power of data to unravel intricate patterns, identify hidden trends and unlock game-changing insights. When the Key Data Creation Statistics came out for 2022, it was published that 2.5 quintillion bytes of data are created daily. 2.5 quintillion is 2.5 billion billions, a number incomprehensible by the human mind. Nowadays, utilizing and accessing this data is easier than ever. For sports, this data and analytics have become an indispensable tool. From coaches to players to social media managers, this data-driven solution is revolutionizing the industry and equipping teams with a competitive edge.
Sports analytics is essentially the study of athletic performance and business activities used to optimize the operations and success of sports organizations. There are two main components to sports analytics: on-field and off-field analytics. On-field is the component that involves tracking all the key data metrics and utilizing that data to influence the methodologies that could be used to improve aspects of athletic performance such as in-game strategies, nutrition plans and so on. Off-field analytics is the component that focuses on the business side of sports. Off-field analytics will include things like ticket sales, fan engagement, merchandise sales, burn rate, etc.
On-field analytics seeks to answer questions relating to on-field performance such as “which hockey player scored the most goals in a season?”
Off-field analytics seeks to assist business professionals make more informed decisions with the goal of increasing growth and profitability.
According to the business research company’s research, between 2021 and 2023, the sports industry grew from $354.96 billion to $512.14 billion with a compound annual growth rate of 5.2%, making it one of the most substantial and profitable industries out there. With this knowledge, sports organizations are spending more and more money on fields like sports analytics to earn a competitive edge. The sports analytic market alone is projected to reach $4.5 billion by 2025.
Some of the largest benefits when it comes to using sports analytics are having more informed decision making and increased revenue. When discussing informed decision making, sports analytics play an integral part when making strategic decisions. Decisions that are backed with data typically show that they lead to more powerful and accurate decision making. An example of this is within the NBA. Several teams in the league (Philadelphia 76ers as an example) are leveraging intricate data analysis techniques such as data visualization and hypothesis testing to examine NBA games and influence the team’s coaching strategy.
For teams that are investing in data analytics, it's generally expected and noticed that they experience substantial financial returns. An area where sports teams are directly using data analytics to increase revenue is through ticket sales. They do this by setting ticket prices with the knowledge that data analytics gives them; it helps them look at their key financial insights and determine the best possible value. Data analytics can also be used to optimize online sports retail revenue. Teams are using techniques and strategies such as data manipulation, aggregation, cleaning and many more to optimize their profit.
History of Sports Analytics
Though sports analytics is an emerging field, when you look back, you’ll notice sports and data have always gone hand in hand. From newspapers publishing the scores of last night's games to trading cards and radio announcers sharing statistics during their commentary, data has always been an influential part of sports.
Coaches, general managers and scouts have always used a mix of stats - points, batting average, goals, yards thrown, etc. - to evaluate a player, but beyond extremely surface level statistics, decisions lacked deeper analysis. Statistician Bill James challenged these shallow statistics in the 1980’s, pushing for more profound data. James went on to create a mathematical system called Sabermetrics.
Sabermetrics is a system that is still used today to evaluate baseball players. He released his system to the public in a book called The Bill James Historical Baseball Abstract. In the book, he created equations like “runs created” (runs created factored in a baseball's offensive stats and helped predict how many runs they would likely score). This was James’ first attempt at objectively analyzing players and helping general managers optimize their teams.
Sports analytics took off in 2002, when Oakland Athletics general Billy Beane relied on data analytics when he assembled a team of lesser-known players that went on to nearly win a world series. His strategy for optimizing a team by using statistical analysis became known as “Moneyball” and many teams quickly started using similar techniques.
Nowadays, every major sport has had their own analytical evolution, even going as far as many teams hiring their own data scientists and finding ways to objectively analyze their players and gain a statistical and competitive edge. Basketball is a great example as teams now optimize offenses for three-pointers and layups because a shot chart analysis shows that they are the most efficient shot in the game.
Tracking softwares and machine learning have been able to take sports analytics to the next level. There are now companies like Genius Sports that are able to generate statistical content and breakdowns using video footage that helps coaches optimize their play calling during games and post-game takeaways. Some teams also use cameras and machine learning software to track ball speed, spin rates, player movement and more. All these things will factor into the outcome and decisions for the team.
How Different Sports Use Analytics
Though the fundamentals of sports analytics are relatively universal and consistent as they are used to gain a competitive edge through statistics and analysis, the way that sports analytics are employed from sport to sport vary and each sport will deploy many methods to collect and analyze the data.
For soccer, off-field analytics are an integral part of decision making. Soccer clubs have invested a lot into data science and related technology with the hope of boosting their on-field performance. They track things such as in-game positioning, fatigue during training, distance covered and many other aspects that provide detailed insight into players' conditioning.
With baseball being one of the first sports to utilize sports analytics, the sport has been the benchmark when it comes to sports analytics for many years. Many statistics are used to aid MLB teams’ decision making processes. Some of those are the players’ batting average (batting average is a statistic calculated by dividing the number of hits by the number of at-bats for the player, this will help demonstrate players tendencies and which pitch tends to strike them out), on-base percentage (illustrates how often a batter can avoid being put out at the plate and this is defined as the percentage of times a player reaches a base) and their slugging average (this measures a players batting power. It shows the number of bases a player earns on hits. The higher the slugging average means the more likely the player is to hit for extra bases).
Nowadays, most NBA teams have sports data analysts as a staff member on their team. Their role is to support the players and coaches with data to aid them in maximizing on-court performance and identifying undervalued players and prospects. At the highest level, most basketball teams use data-tracking cameras at all angles of the basketball arena to track every movement made by every player on the court. They then sync the data to the player’s statistics and this provides a full breakdown of the player’s performance.
One of the most important parameters in hockey is puck possession. This is used to gain a thorough understanding of who has the lead on the game. However, with hockey there are many advanced statistics that are strictly used within hockey. These include Corsi (Corsi is named after a former coach and it is the sum of total shots on goal. Corsi includes shots at goal, shots attempted that missed the net and shots attempted that were blocked), Fenwick (is known as unblocked shot attempts. Similar to Corsi, however Fenwick does not include shot attempts that were blocked, only shots at the goal and shots attempted that missed the net) and PDO (the sum of the teams shooting percentage and save percentage).
Sports analytics has transformed the landscape of professional sports, offering teams and organizations a wealth of opportunities to optimize their performance and enhance their decision-making processes. From on-field strategies to off-field business decisions, sports analytics has become an indispensable tool for achieving a competitive edge. As sports analytics continues to evolve, incorporating advanced technologies like tracking software and machine learning, teams are harnessing its power to unlock new insights and gain a statistical advantage. As we continue to unravel the intricate patterns and hidden trends within the world of sports, the possibilities for improvement and innovation are limitless.