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Data Science Reaches the Podium

As in business, data analytics can be useful when strategically applied to sports

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As in business, data analytics can be useful when strategically applied to sports. Data science has revolutionized the way we do business, analyze situations, and make decisions. It can be applied in various sectors, bringing financial benefits, optimizing processes, enhancing productivity and business knowledge -- and sports achievements.

Let's look at some examples:

Player Performance

Using player data, it is possible to generate insights and form the best lineup, as well as create a heat map to analyze the opponent's technical deficiencies.

Physical Preparation

Wearable devices (such as wristbands and vests), make it possible to monitor the health indices of each athlete, in order to prevent injuries or physical wear and tear.

Results Forecasts

Machine Learning algorithms can be applied to create models that, using historical data from championships and teams, can make predictions about game results. The Liverpool Football Club team, for example, uses data analysis in its elaboration of plays and player selection.

(Picture:Liverpool)

The German national football team uses health data analytics to monitor their athletes, Adidas miCoach System has helped them in the World Cup in 2014. They also use the Match Insights solution (a partnership between the national team and SAP) for a complete x-ray not only of the team and its opponents.

Match Insights make it possible to identify:

- which sectors of the field the opponent stayed in for the longest time;

- which team performed better;

- the most common passes and plays for each team;

- which goal point each player finished most often.

Interesting fact: Data Analytics were credited for victory at the 2014 Fifa World Cup.

The application of data science to sports is not limited to football. The Olympic and Paralympic Games are great examples of how to gain insights far beyond field lines, pool lanes, or court boundaries. Data exists from 1896 for the Olympics, and 1959 for the Paralympics, yielding a vast store of data available for analysis.

For example, this multi-generational data can help us understand:

  •  whether a country’s economic factors influence their performance in the Games
  •  what is the impact of the competition on the host country (considering factors such as weather and fans, for example)
  • what characteristics of the competitors have relevance in winning medals (such as age and years of experience).

Analysis can be performed using web scraping techniques to collect data from official websites, or using ready-made datasets on platforms such as Kaggle. Data visualization can help us understand the correlations between the desired variables and, thus, reach conclusions with knowledge of the situation to be analyzed.

 

About the author: Ana Carolina Oliveira:

https://www.linkedin.com/in/ana-carolina-p-oliveira/