By Ashley Jonson

The way data is collected and analyzed has had a profound impact on professional sports teams. With so much money involved at elite levels of sport, gaining an advantage via analytics has itself grown into a multimillion-dollar industry with millions of jobs created worldwide. Cutting-edge technology and meticulously gathered data help sportspersons and teams to maximize their performance in the arena, help define tactical approaches, and create highly personalized regimens in training, lifestyle, fitness, and diet for individuals.

Data is becoming one of the most powerful tools available in pro sports, from basketball to boxing, from football to fencing – it can be the key to success at the highest level. Let’s look closer at how data analysis is used in sports.

Big game performance

One of the main functions of data analysis is to examine performance under the microscope and dissect it to highlight strengths, weaknesses, causes, and consequences. Take basketball for example – any NBA team will have multiple cameras recording various facets of the game. Each player will be tracked throughout the match, and after the game, their movements can be examined in depth. Data collected isn’t just about where they have moved on the court, it is also about footwork, feints and tells, and overall presence in the game. This data can be analyzed to form a winning strategy in the future. This kind of detailed data can form new approaches to both offensive and defensive strategy, as well as helping to determine which players are the strongest against specific opponents.


Data is also collected and analyzed on opposing players. With virtually every major sports event covered extensively by cameras and usually featuring punditry, slow motion, and tactical breakdowns it is fairly easy to gather data.

One-on-one sports such as boxing have benefited enormously from the advent of data analysis. Being able to break down an opponent’s strengths and weaknesses to formulate a game plan which exploits both is crucial to a boxer’s preparation. Team sports also benefit – knowing which player negates another on-field is determined by data gathered in training and in previous games.

Player development

Identifying where a player succeeds and where they lack skills or commitment is a central idea behind data analysis.

Let’s use an example: Romelu Lukaku, former Manchester United striker was criticized for his weight and his work rate during his time at the club. He moved to Inter Milan who analyzed his performances and diet. Within a matter of weeks, he had shed 3 kg using the Bresaola diet, and he helped guide Inter to Serie A success the next season, becoming an effective counter-attacker rather than a lone wolf upfront.


It’s not only sports teams that reap the benefits of data analysis – pundits, fans, and bettors can also use information. Data provides many more interesting angles to discuss on sporting programs and in magazines, adding a new angle for fans.

For example, those who like to place football bets do so not only by consulting football betting blogs like which helps punters choose good sportsbooks, bets, and leagues, but they also rely on knowledge and insight about players, leagues, and teams, taking into account up-to-the-minute news about injuries, tactics, even weather reports.

With large upcoming events such as the Olympics and the FIFA World Cup in Qatar, bettors will be preparing almost as extensively as the athletes in predicting the outcome of events. And the benefits from it are obvious.


In the world of professional sport, injuries are inevitable – from minor muscle pulls through to more serious complaints. Data analysis may not be able to fully prevent injuries, but it can help to predict when they may occur and anticipate the amount of treatment a sportsperson will need.

Many injuries are related to fatigue, and data gathered by wearables, sleep monitors, and even saliva samples can predict energy levels. This means that players can be effectively – and preemptively – rested if fatigue-related injury is likely. And physiotherapy and therapeutic treatments help to rehabilitate injured players quickly and effectively.


Data can be used to seek out and develop fresh new talent. Most major sports teams have junior academies – through these, they can monitor the development of younger players who are spotted by scouts. These younger players are often contracted to the team, but this long-term process is an investment for the future – homegrown talent is far more economical than buying established players from other teams.

With so much at stake, it isn’t surprising that advanced data analysis techniques play a large role in modern sport. At the elite level, data can give an athlete the winning edge.

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