Using statistics for an effective game plan: Data Science in sports

Time has witnessed how different sports have evolved and innovated throughout the years. Rules, tactics, and execution have been altered and transformed to parallel the continually escalating standards being set by outstanding athletes in various sporting competitions.

Along with these breakthroughs are an athlete’s relentless dedication and resolve to overcome adversities and obstacles en route to victory. But one other element has emerged that propelled athletic development with the help of technology—the knowledge contributed by Data Science.

Data Science, or analytics, provides valuable assistance in the training and development of an athletes’ skill, especially those in higher-level competitions. Using the principles of Data Science, decisions, strategies, and routines become more reliant on numbers and statistics and much less on simple instincts.

But while its influence has reached some teams in the local scene, it has yet to draw attention on the national stage. Coach Enzo Flojo of the Ateneo Blue Eaglets and Coach Paolo Layug of College of St. Benilde Blazers share their insights about the role of Data Science in Philippine sports games.

Investment to winning

Data Science has gradually fastened its place in sports as winning has always been a battle of game plans, especially for team sports. It can also be beneficial in analyzing the performance statistics of individual athletes. Depending on the objective, it can cover various aspects essential to sports, including strength and conditioning, scouting, team performance, and sports business.

With the numbers and charts, coaches and athletes alike, can monitor their performance and extract valuable information necessary for improvement and decision making. As Flojo describes, “It helps in the understanding of the game.”

To further enhance one’s edge, analytics can also be used to study the tendencies of the opponent and devise strategies to counter them in preparation for the game proper. As Layug sees it, sports somehow resembles business where, “you want [more] information to help you make the best decision,” implying the importance of Data Science in sports. Simply put, it is an investment to winning. 

Varying relevance

As expected, analytics play a different role when utilized in individual sports compared to team sports. As such, there is a significant divergence between the tasks of a data analyst in tennis compared to football when it comes to the data itself and its interpretation to improve the athletes’ training regimen.

Layug touches on this distinction, “For individual athletes, any analytics can be tailor-fit to their performance, whereas in a team environment, you have to factor in all sorts of differences, such as physique, player type, role on the team, [among others].”

The Blazers coach also emphasizes that analytics in individual sports may be more relevant because the data is all focused on one person. Conversely, some of the most important factors in any squad are chemistry and teamwork, which severely affect the efficiency of any game plan, no matter how intricately it was constructed.

Reliability of utility

Analytics also has varying degrees of significance and effectiveness on different levels of any sport. Its relevance shifts in each of these stages, be it collegiate, professional, or international, because the gathered data would actually be reliable in reference to future matches.

Flojo explains that most collegiate and all Philippine Basketball Association (PBA) teams have personnel who are assigned to gather the complex data, interpret them, and report to the head coach.

However, the Blue Eaglets assistant coach argues that the scarcity of games in international tournaments affects the reliability of the data. He expounds, “In an international tournament, they play fewer games. For example, this short [FIBA] window where they (Gilas Pilipinas) played three games. The data you’re going to get is a small sample size only. The value of that is not as much as the value you would get in a longer season or tournament because more data constitutes a smaller room for errorand assumptions.”

Flojo further emphasizes the importance of an active competition, noting that because the National Basketball Association (NBA) has a minimum of 82 games in a season unaffected by a pandemic, the collected data would be rich and substantial since the patterns can be determined from a large contingent of games.

Further ahead

The use of analytics has garnered contrasting opinions from retired players. Former NBA Most Valuable Player Charles Barkley claims that it ruins the sport, while people like Layug view it as a necessity in sports because of its now indispensable part of the winning formula. Layug even goes as far assaying that renouncing its use leaves one at the risk of being left behind while others gain a competitive edge.

No matter the side taken, there is no denying that the availability of Data Science technologies has radically shifted the landscape of sports. It has intensified the playing field—from training, conditioning, coaching, and scouting, among others.

Flojo claimed that it will only continue to evolve. He concludes, “There are so many amazing minds breaking down and analyzing the sport in different ways that someone is bound to change it again at some point in

the future.”