The Growing Role of Analytics in Sports
By
Will Brown
Contributor Updated: 25 October 2024
Will Brown Contributor
William Brown is a Content Writer with strong expertise in US and global gambling industry trends. Having covered the local sports betting market since the repeal of PASPA in 2018, William approaches his gambling guides with passion and diligence. He is a keen player of online slots, blackjack, and a regular sports bettor.
The Rise of Analytics in Sports
In recent years, the sporting world has undergone a dramatic transformation due to the use of modern analytics technology. Analytics in sports has seen the collection of big data utilized by everyone from sports scientists, coaches, team organizers, and betting companies to change how we understand the games. Now, as artificial intelligence is being integrated, the world of sports is in the midst of a rapid evolution. We’ve taken an in-depth look at this topic in this comprehensive guide.
Intro to Big Data Sports Analytics
While data analysis in sports has been used for decades, it has seen rapid growth in the past 20 years. We’ve begun this guide by tracking this evolution, before exploring the types of analytics technologies that are being used and integrated.
From there, we’ve explored how advanced sports data is impacting team gameplay strategy and even further, how drafting decisions have been affected. To round out the guide, we’ve looked at the topic from the perspective of fans, sports audiences, and sports bettors.
- The Evolution of Sports Analytics
- Sports Analytics Technology
- Data’s Impact on Team Strategy
- The Effect on Drafting
- Impact on Fan Experience
The Evolution of Advanced Sports Data Analytics
Since the advent of major league sports, teams have been collecting data and analyzing it to inform their performance and training. In the early days, these relied on basic statistics such as batting averages in baseball, conversion rates in the NFL, and shooting percentages in basketball.
The NFL in particular has long embraced the use of statistical analysis to evaluate players and inform roster moves, but it wasn’t until the early 2000s that analytics in sports entered a new era. Much of this has come down to what is known as the “Moneyball” effect, which was based on Oakland A’s general manager Billy Beane’s reliance on sabermetrics.
This unique approach allowed Beane to build a competitive team on a budget, with advanced statistics revealing undervalued players and opportunities for strategy adjustment. When a book was published about Beane’s system, a revolution in how teams could be constructed swept professional sports.
Then, in the 2010s, the world entered a new age of digital technology. This saw advanced tracking technologies applied to player training and performance and processed in real time. This technology was refined in conjunction with sports sciences, allowing teams and leagues to collect and analyze huge troves of data on every element of a game.
This big data sports analytics is now an essential part of any competitive sports team, with teams now employing data scientists and engineers to ensure that their data is accurate and quality. Now with the advent of AI technology, this burgeoning field is undergoing another rapid revolution, its impact reaching as far as online sports betting in the US.
The Technology Behind Sports Analytics
The rise of sports analytics wouldn’t be possible without the concurrent evolution of cutting-edge technologies. The most essential of these are player tracking systems. The first of these is optical tracking, which uses high-speed cameras to record player gameplay and minute movements throughout training and games.
Sports data services such as the Second Spectrum system, which is used by the NBA and MLS, can track the positions and movements of every player and the ball dozens of times per second. Using machine learning, the system then exports detailed reports that provide an analysis of several metrics that are tailored to each team or league’s needs.
Another useful trackable system is wearable devices. These have several technologies built in, including GPS trackers and biometrics scanners. This provides the team with overviews of metrics such as players’ heart rates, speed, and movements. From there, the data is fed into sports stats AI software that rapidly undertakes complex statistical analyses.
Common platforms include Stata, R, and Python as well as specialized sports analytics software such as Sportlogiq or Hudl. One of the benefits of machine learning and AI is that it can make informed predictions about player performances or match outcomes, analyze risks, and monitor injuries. It uses both historical and current data to build its reports.
The efforts are aided by immense infrastructure that ensures that the complex information is easily visualized and accessible to stakeholders. This technology can facilitate rapid changes to strategies, reshape training programs, and ensure player longevity with very little human input.
How Sports Data Impacts Team Strategy
One of the most clear impacts of the use of analytics in sports is the effect it has had on team strategy. Both coaches and players can rely on a trove of quality data that can shift how each game is approached. For example, in basketball, shooting strategies have been largely adapted.
We have witnessed the rise of the three-point shot in the NBA as analytics have indicated a higher expected point value of three-pointers and close-shots compared to mid-range jump shots. In the NFL, coaches have been empowered to make informed calls that have been pre-tested with AI models.
In recent years, a trend has seen an increase in play-action passes and aggressive fourth-down decision-making, and much of this is attributed to data analytics. In all sports, player rotations can be strategized according to pair-ups with teammates and opponents, such as favoring a specific left-handed pitcher against a lineup of left-handed batters in baseball if the data suggests this.
That said, coaches can’t solely rely on advanced sports data to make all of their decisions, and there is still a strong element of human experience, skill, and intuition involved. Rather, this technology is used to aid and inform strategizing. Furthermore, as trends emerge, so too must strategies adjust as they become predictable.
Sports Analytics and Drafting
As was mentioned earlier, team drafting was one of the first to be revolutionized through the use of advanced sports data through the Oakland A’s innovative approach. This was detailed in the 2003 book “Moneyball”, which chronicled the implementation of a system that identified undervalued baseball players.
Noticing that certain baseball metrics were being overlooked such as on-based percentages, Billy Beane found a way to compete with larger teams to draft using a small budget. One metric that was used is the Wins Above Replacement (WAR) model, which provides a comprehensive view of a player’s abilities.
The team then identified which players could contribute significantly to their success for a much lower cost than star players. This approach completely changed the evaluation of talent by scouts through simple observation, and now sophisticated models have been created to assess potential recruits.
In basketball, standard player roles have been reevaluated to get the most value from them. This has led to the rise of “3-and-D” players, which are players that are adept at scoring three-pointers and providing defense. Furthermore, certain metrics can also identify players who may not have high scoring averages but contribute greatly to success in other ways.
In the NFL, statistical analysis is often tailored to identify potential talent to ensure that scouts are watching the right players. The data is then considered alongside scouts’ personal and human grade evaluations such as the player’s attitude and ability to play in a team. The analysis can also be used as a risk assessment measure, such as whether their statistics indicate that they will be a suitable NFL player.
How Sports Data Affects Fans
Even the least observant sports fan is likely to have noticed how much more real-time analytics in sports has become routine. Most broadcasts will now regularly feature digital visualizations of gameplay and statistics alongside plays and events. This could be the tracking of ball velocities or trajectories, the movement of players, or hit-and-miss ratios.
Ultimately, these statistics help viewers to understand sports at a more complex and sophisticated level. This is especially important when considering the rise of daily fantasy sports and sports betting in the USA. Fantasy sports player prices are informed by a wealth of accurate league statistics, and the most successful managers will research stats further to inform their picks.
In sports betting, odds too have been greatly impacted by data, ensuring more accurate prices. Still, the most savvy bettors can benefit from price discrepancies and undervalued markets. With betting and fantasy sports now integrated into the viewing experience for many leagues, understanding sports analytics is becoming instrumental for some.
However, not everyone has been in favor of this push toward more data-driven coverage. Old-school viewers may find that the use of statistics saps the emotion and drama of sports, or leads to more predictable games. Others may find the information to simply be too overwhelming to absorb or distracting. Whatever the opinion, it’s clear that data analysis will only intensify in the future.
