There’s a common misperception that data analytics were introduced to the sporting world only recently. The truth is that data analytics has been applied to sports since 1964. At the time, it was used in a book by Earnshaw Cook that discussed baseball statistics and analytics.
Back then, the intrigue and science behind analysing data did little to catch the world’s attention. Although some instances were noted, it wasn’t until the 2003 release of Moneyball: The Art of Winning an Unfair Game that sporting bodies and teams took note. Telling the story of a coach with a limited budget, the book outlines how a losing team’s data points were analysed to create the ultimate game plan that made them a surprisingly successful team.
Moneyball highlighted data analytics in a way that precursors failed to do and ignited a global interest in the science. From soccer to baseball, many sports teams were suddenly interested in what data analytics could do to increase their chances of winning.
Timeline of Data Analytics Growth
The first practical use of data analytics occurred in 1984, when the manager for the New York Mets, Davey Johnson, lobbied for its inclusion in Major League Baseball. At the time, he used statistics to help form starting lineups, plan pitch rotations, and develop in-game tactics.
By the mid-1990s, the idea of using data analytics spread beyond baseball to the National Football League. Though used on a much smaller scale, it was adopted by the Philadelphia Eagles to help make scouting decisions based on data points drawn from player performance.
Few teams followed the Eagles until after Moneyball was published. In 2006, the National Basketball Association began looking into the science. Using the SportVU tracking system, the NBA started collecting hundreds of data points on players based on their in-game movements to see where each player could improve and help create play strategies.
By the 2010s, data analytics spread like wildfire. Premier League soccer teams began using the technology, with many investing huge sums into acquiring and decoding data points. Teams in the NFL also started deepening their use of the tech beyond scouting and began using it to develop game plans and identify opponent weaknesses.
Sports Betting
Alongside the growth of data analytics in sports, wagers placed on sporting events also grew. Between 2000 and 2024, the iGaming industry experienced an explosion of popularity, with sports betting at the epicentre.
A 2024 report estimated that almost one-third of the global population has participated in gambling activities, with wagers on sports being the most common way to play. This number is set to increase as gambling becomes more accessible and incentives, such as the chance to enjoy free bonuses without depositing, are offered.
With an increase in sports betting has come a giant leap in the number of tips offered for gamblers and predictions made to forecast the outcome of upcoming games. While many of these are based on specific algorithms that may help improve the odds of winning slightly, none have made a sizable difference over the years. That is until data analytics was applied.
Data Analytics in Sports Betting
Although it is unclear when data analytics were first applied to sports betting, it has become a regular occurrence worldwide. From sportsbooks that help gamblers by offering predictions to tipster sites that aim to provide direction on how to bet, many websites, entities, and people predict upcoming games.
Many of these use the myriad data points available for each sport, team, location, and player to help their prediction model and an intelligent algorithm to help determine the outcome. These data points have become increasingly more accessible and are collected from several sources, including:
- Fitness trackers and wearables: These are worn by players and measure their heart rate, VO2 max, speed, acceleration, step counts, stamina, and other metrics. Other wearables, such as the WHOOP straps used by EPL teams, track players’ sleep patterns and quality and the speed at which their bodies heal.
- Stadium technology: This includes sensors or cameras in the stadium where games occur. These sensors and cameras collect various information, from where players gravitate on the field to how likely they are to take on an opponent under specific conditions.
- Match data: This information provides unique data points such as how a team performed, who was on the team and in what position, the circumstances of the game (e.g., weather and fan turnout), and at what point of the game-specific incidents occurred.
These data points help build a map that can be followed and analysed to show patterns or trends defined by specific players, team lineups, or outside influences. Based on these predictions, more accurate tips can be procured and more efficient bets can be placed.
This is particularly true given the growth of artificial intelligence and machine learning. These technologies are actively applied to sports analytics and used to create algorithms that make game predictions and recommendations—even mid-game.
The efficiency of AI and ML in creating accurate bets comes down to their ability to process millions of data points in an incredibly short time. With in-game betting on the rise, this is particularly important as it allows gamblers to feed real-time information into the algorithm and see how it will change or affect the predicted outcome.
Where actions in the game are likely to change the winning team, gamblers can adjust their bets or place alternative bets to cover their losses and make a profit. It should be noted, however, that even AI is fallible and that predictions made by these algorithms aren’t foolproof.
Conclusion
While data analytics have existed for decades, the science’s integration with AI and ML is ushering in a new era of information and knowledge—especially for gamblers.
With more data points available, more intelligent algorithms capable of analysing live information, and more accurate bet predictions, data science is transforming gambling into a sophisticated science with incredible results.
