degree-of-interest: Master of Business Administration

Sports Analytics: Revolutionizing Decision-Making in Sports Management

Sports Analytics: Revolutionizing Decision-Making in Sports Management

Analytics power today’s digital world. We rely on a myriad of metrics to deliver accurate details about finance, logistics, and healthcare, but analytics also play heavily into modern entertainment. Sports analytics, in particular, shape every aspect of the athletic experience, producing vast improvements in training, coaching strategy, and even fan engagement.

Simply put, sports analytics are a game changer. Data-driven athletics promise actionable insights and, moving forward, we can expect metrics to be integrated into every league, team, and fan experience. This, in turn, will produce a wealth of exciting job opportunities that will appeal greatly to anyone with a passion for sports and a desire to take a deep dive into data. A business administration or sports management degree can pave the path to success in this exciting field.

To shed light on the exciting applications of data analytics in sports management, we’ve highlighted a few of the most important areas in which data-driven solutions promise considerable dividends. Keep reading to learn what sports analytics involves, why it’s so impactful, and what is possible as advanced technologies enter the fray. 

How Did Analytics Wedge Into Sports?

Analytics have always been part of the athletic experience, at least to some extent. While this has certainly grown more complicated over time, records dating back to the Ancient Greek Olympics reveal careful measurement of participants’ results in events such as the discus throw.

These days, sports analytics draw on complex algorithms and cutting-edge technologies. These practices are even more impressive when you understand just how far sports analytics have come in a few short decades. 

Pioneers in Sports Analytics

Records from the earliest days of baseball indicate that select metrics were tracked from the very beginning. These were usually simplistic, however, and they were relied on more for scoring purposes than for driving personnel decisions or game strategy.

The modern-day concept of sports analytics began to pick up steam during the second half of the 20th century, as evidenced by the publication of Earnshaw Cook’s landmark guide Percentage Baseball. A few years later, the Society for American Baseball Research (SABR) was formed, eventually attracting widespread media attention as this approach was detailed in a 1981 Sports Illustrated article.

Davey Johnson was another sports analytics pioneer. He created a FORTRAN baseball simulation and hoped it would convince Orioles manager Earl Weaver that he would be the ideal candidate to hit second in the lineup. Later, he relied on IBM programs while managing the minor league Tidewater Tides and eventually, the New York Mets.

Of course, no discussion of sports analytics and its complex history would be complete without at least mentioning the famed Billy Beane, who relied on his Moneyball theory to select a team of undervalued athletes who went on to excel despite extreme budgetary restrictions. 

The Analytics Attachment (MLB)

Analytics have long played a critical role in many sports, but their impact has been especially strong in baseball, where front offices rely on complex metrics to identify the strengths and weaknesses of their players. Given the economic realities of baseball, some teams depend on scouting, drafting, and developing homegrown players while others can simply lure top athletes to their side with big-money contracts in free agency.

Many young players are signed and drafted as teenagers, prompting years of development before they make it to the Major Leagues. During that time, it’s up to coaches and front-office personnel to use analytics to identify where these players excel and how they can improve.

Baseball is a perfect fit for analytics, as so many aspects of athletic performance can be measured via statistics and even physics. These range from the exit speed of a ball leaving the hitter’s bat to the spin rate of a curveball, but that’s only the beginning. 

Video Highlights

The proliferation of streaming video and social media has created a highlight culture that’s attracted a younger generation to sports. For some viewers, sitting through a nearly three-hour-long baseball game doesn’t make sense when they can see the highlights on YouTube.

Sports leagues have largely embraced this new way of consuming content and have even baked analytics into video highlights to give fans a new understanding of just how incredible these athletic feats really are. Not only have the leagues and their broadcast partners embraced video highlights, but teams themselves have also found ways to make video work for them.

For decades, players and teams have utilized video to break down and analyze past performance. For example, Hall of Famer Tony Gwynn had a huge library of tapes featuring his at-bats so he could refer to them when facing pitchers throughout the season. Now, these video highlights can be accessed instantly, so players and coaches enjoy access to real-time feedback. 

What Is Sports Analytics?

Sports analytics involves the strategic collection and analysis of athletic-oriented data, in which statistical models are applied to predict player performance and other parameters of the athletic experience. The scope of this practice has expanded greatly in recent years, and while sports analytics once emphasized player performance above all else, this term now encompasses any use of data to predict outcomes and drive improvements in the sports industry. 


Drawing on the power of statistics to bring empirical analyses to baseball, sabermetrics was defined by pioneer Bill James as “the search for objective knowledge about baseball.” This is a core component of modern sports analytics and has paved the way for the complex algorithms we now take for granted.

As MLB experts explain, “sabermetrics asks questions about how baseball is played and [the] most efficient ways to succeed” before striving to answer these questions through the power of empirical research. To qualify, answers to key questions must be backed by quantifiable data. This approach now extends beyond baseball to influence a variety of sports and leagues. 

Player Tracking

Athlete tracking is an increasingly common practice across multiple leagues but is especially prominent in football, where tracking systems monitor athletes in training and competition while gathering details on everything from their heart rate to distance traveled. Software solutions then analyze this data and generate reports that can be easily understood by coaches and athletes. 

Predictive Modeling in Sports

Drawing on the cliche “those who fail to learn from the past are doomed to repeat it,” predictive modeling relies on historical data to reveal what is possible in the future. This is valuable not only from a training and injury prevention perspective but also because sports betting is now such an integral part of the fan experience. Moving forward, predictive modeling may play a greater role in contract negotiations, helping athletes showcase their strengths through the power of analytics. 

How Does Data Analytics Apply to Sports?

Every decision, play, and fan response produces data that can be leveraged to influence nearly every aspect of the modern sports industry. Opportunities abound, but we’ve highlighted some of the most significant data analytics applications: 

Measure of Player Performance

A variety of factors determine athletic performance, and these can often be difficult for even the most knowledgeable coaches to recognize or understand. Data analytics brings clarity by drawing on numerous metrics to tell the full story of how players are performing on the field, rink, or court.

This goes beyond basics such as RBIs or yards per carry to encompass details such as wins above replacement — how many wins could be achieved with a current player versus a supposed replacement. Air yards represent another opportunity to get the real story: the distance the ball has traveled in the air past the line of scrimmage. 

Optimizing Game Strategy

There is no easy way to decide which lineups will be used or which pitchers should face certain hitters. Sports analytics, however, provide much-needed insights so that coaches and managers need not enter the planning process blindly. Real-time metrics help coaching staff make split-second, data-driven decisions during difficult moments in which intuition alone might be insufficient. 

Maximizing Fan Engagement

Strong fan engagement is crucial for ensuring the long-term profitability of any franchise. Fans form the economic engine fueling today’s leagues and teams, and as soon as their interest wanes, so do the financial prospects for these organizations. While little can overcome the disappointment of a losing season, metrics can help by revealing what, exactly, is most likely to get fans in the stands or, at least, committed via TV or social media.

Fan engagement can play heavily into marketing and promotions, as past attendance and even in-game behavior can reveal which aspects of the game experience fans find most compelling beyond what’s happening on the field. Everything from song requests to concessions can influence fan enjoyment and determine whether they return in the future. 

How Has It Impacted Professional Sports?

Name a league or team and you will find endless examples of data analytics already applied or, at a minimum, in the works. This applies beyond the major leagues, although the most prominent and cutting-edge examples of sports analytics largely center around the MLB, NFL, and NBA. 


As mentioned previously, the economic realities of baseball — and the competitive balance obstacles it creates — have led to an even greater reliance on analytics to help bridge the gap between the haves and the have-nots. This has led to teams completely changing their philosophy toward how they hit, pitch, and even play the field.

Teams have begun to prioritize home runs over hitting for a higher average, instructing hitters to increase the launch angle at which they hit the ball. This creates more fly-balls and home runs — but as a by-product, strikeouts have increased significantly.

Managers trying to field the best defense possible have gone to great lengths to shift players around the infield and outfield. This plan ultimately backfired, however, as the drastic shifts and the resulting lack of hits forced the MLB to ban exaggerated shifts at the end of the 2022 season. Baseball proves that there are still new ways to play an old sport, with analytics helping to sharpen these new rules and procedures. 


Many football analytics exist to measure actual player performance, instead of what could be perceived by simply looking at basic stats in a box score. Quarterbacks, in particular, are prone to being credited (or blamed) for things they ultimately have no control over, although analytics promise to help.

Analyzing individual player performance in football is tricky, as each side consists of eleven players working as a team to make a play. Analytics can be used to filter out less relevant plays and performances to give coaches a more accurate idea of which players are performing at their peak. 


The modern NBA has been changed drastically by two elements heavily influenced by analytics: the proliferation of the three-point shot and the implementation of planned rest days for veteran and star players. Much like in baseball (where teams may choose to risk more strikeouts if it means more home runs), NBA teams have shifted away from the painted area and further outside the arc. By attempting more three-pointers — and more shots in general — teams have found a new way to operate their offense.

Analytics determine how games should be played and who should be playing them. Load management can ensure that players get much-needed rest, so they’re better prepared to handle the rigors of athletics later in the season. This strategy has met fierce resistance from paying fans, who expect to see their favorite star players after spending their hard-earned money on tickets.

Analytics forced Major League Baseball to ban the shift, and now, it’s forced the hand of NBA commissioner Adam Silver, who has implemented new rules on how star players are managed during the season. 

Future of Data in Sports

The data-driven era of sports is well underway, and it’s abundantly clear that metrics will be a core part of this industry for years to come. How these metrics are gathered, analyzed, and disseminated, however, may evolve over time, especially as new tools and technologies enter the picture. 

AI in Sports Analytics

Artificial intelligence has made its mark in every corner of the data analytics field, so it should come as no surprise that AI solutions are also increasingly prominent in the world of sports. AI can amplify many of the opportunities we’ve already discussed, including everything from player performance predictions to ongoing efforts to boost fan engagement.

What is one of the most noteworthy examples of AI as a solution for assessing player performance? The MLB’s use of Statcast to reveal pitch velocity, exit speeds, and so much more. Meanwhile, the NFL draws on AI solutions to deliver a better experience with its app, which can deliver customized content that appeals to specific types of fans.

AI may even play into judging sports. This has traditionally proven controversial due to the split-second nature of the decision-making process. With AI tools built into the mix, data-driven insights are possible, thereby bringing greater fairness and transparency to processes that have often been difficult to understand. AI certainly made its mark at the 2022 FIFA World Cup by tracking players’ positions to aid referees. 

Player Injury Prevention

Moving forward, we will see sports analytics used to limit injuries and keep players as safe and healthy as possible. Sparta Science, for example, draws on AI technology to create visual representations known as Movement Signatures. The goal of this program is to produce more efficient movers, who will not only be less susceptible to fatigue but also less likely to suffer injuries as joint stress is optimally dispersed. 

Embrace the Future of Sports Analytics

Are you passionate about sports and eager to help shape this exciting industry? Get ahead of the game by learning about the future of data science in sports with a degree in business administration from Carson-Newman University. As a Christian university, we strive to help students reach their full potential as educated citizens and worldwide servant leaders.. Reach out to learn more or to take the next step and apply.

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