It is no secret that data is the driving force of most industries today and the field of Sports is not an exception. An enormous amount of data is generated daily in Sports and the professionals who can find useful patterns from this deep sea of data are sought after by teams. It is one such position for which teams hire skilled Data Analysts who can provide insights that can bolster the decision-making process.
At the top level of the game, where the margins between victory and defeat are often all but razor-thin, teams are investing heavily in the necessary technologies and expertise to gain a competitive edge over their rivals. Across Baseball, Basketball, Football, and Ice Hockey, an increasing reliance on Data Analytics is transforming the world of Sports as we know it. This article will discuss the main responsibilities that accompany the role of Data Analyst Sports. Read along to learn about this popular occupation.
Introduction to the Role of Data Analyst Sports
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Data has modified the decision-making process when it comes to Sports. Nowadays, professionals who are skilled at Analyzing data are in huge demand for a new profile called Data Analyst Sports. They are responsible for supporting the team behind the scenes, and providing Analytical support to the coaching staff before, during, and after the game. They help break down the strengths and weaknesses of both their teams and the opposition.
The role of Data Analyst Sports requires them to spend the majority of their time collecting on-field and off-field data from disparate sources and then building Statistical Models based on that data to drive data-informed decision-making. The data includes but is not limited to Training, Game, Physiology, Performance, and Similar Information. Developing insightful reports and user-friendly visualizations by using different BI tools to support business operations and organizational growth is also the task of someone in the position of Data Analyst Sports.
Teams hire those candidates as Data Analyst Sports, who understand Statistics, Data Management, and Data Analysis and know how to share insights and conclusions with stakeholders, such as Players, Coaches, Scouts, and Executives.
Types of Responsibilities for Data Analyst Sports
The role of Data Analyst Sports involves the following 2 types of responsibilities, each of which has its own value:
- Business Based Responsibilities: These kinds of responsibilities involve, using Data Analytics to make insightful decisions that can improve the team’s performance. The data collected for this purpose is confidential and the results of Data Analysis are crucial for the team’s overall development. This kind of responsibility involves Analytics that will impact the decisions regarding Player Transfers, Team Selection, Strategy Planning, etc. The below image represents the use of Data Analytics in strategy building.
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- Entertainment Based Responsibilities: The focus here is on providing data to fans who want to know more about their teams and athletes either for Fantasy Sports, to inform their betting decisions, or for after-game banter. The data is not confidential and is supplied by the team to their fans in a planned manner. The Data Analysis involved in this will provide solutions that can boost the Popularity of the Team, T-shirt Sales, Social Media Followers, etc. The below image represents a Fantasy Football team created by a fan.
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Responsibilities in the Role of Data Analyst Sports
The role of Data Analyst Sports is accompanied by numerous responsibilities. Some of those responsibilities include:
1) Predicting Outcomes
Computers can gather and process more data than humans can, which allows them to better predict future performance. The role of Data Analyst Sports is to use Computer Programming and Data Science to predict the performance of a player or team in a specific game or throughout the season. These insights are very valuable to Fantasy Sports, Sports Gambling and also offer unique value to teams.
Furthermore, Data Analyst Sports use Deep Learning to analyze and examine past data to understand what a given team will do compared to what an ordinary team’s action will be in the same situation. For example, on May 19, 2012, Chelsea became European champions for the first time, defying all odds to beat Bayern Munich — 6 time European champions — in a penalty shootout. While many football fans maintain that penalties are a lottery, the Chelsea Goalkeeper, Peter Cech, guessed correctly for every Bayern penalty. So how did he do it? In an interview with Copa90 in 2019, Cech admitted to analyzing footage dating 5 years showing every penalty every one of Bayern’s players had taken thus eliminating guesswork and intuition.
Predictive Analytics is also used to help predict players’ risk of injury by fitting them with sensors that measure and monitor Intensity Levels, Collisions, and Fatigue. The Data Analyst Sports have a responsibility to provide such kind of information to coaches, who can then use these insights to determine how to alter a players’ training schedule.
2) Evaluating Players or Teams
In Sports, Data Analytics has been used extensively by teams to help them develop player Evaluation Models. Now, most professional teams are recruiting players based on Predictive Analytics regarding how they’ll perform on the field.
In this role, the Data Analyst Sports tries to measure the impact of a player or team. The focus here is analyzing how many wins a team will create or how much value a player will add to the team.
Sporting organizations want to understand how the attributes of a player can impact team performance. For example, the physical data of Goalkeepers in Soccer is used to understand if their wingspan is correlated to the number of goals conceded or in Basketball, whether arm length and height have a strong correlation to success for offensive tackles in the NFL.
This can inform decisions on whether they should draft a certain player or whether they should trade a certain player. So it is the responsibility of the Data Analyst Sports, to accurately evaluate the players and teams so that more fruitful decisions are made regarding transfers. The below image represents the data related to team evaluation in NFL.
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3) Identifying the Areas for Improvement
In the position of Data Analyst Sports, one is often required to build Predictive Models that take into account who is playing which role, the type of opposition, and the tactics in play. The outcomes of this model can also help to design a player’s practice sessions, to teach movement based on references from elite players around the world, and, also, from players in direct competition for a position so that they have a bigger impact on the game.
The information that one gleans as Data Analyst Sports and the insights that one brings, can help coaches optimize their decision-making, tweak their training plans, and help determine patterns about competitors to eliminate guesswork from the game.
4) Enhancing Understanding of the Game
Coaches largely rely on intuition when breaking down games, but for someone in the role of Data Analyst Sports, metrics are required to explain what the audience is perceiving and in what context. A lot of the data that the coaches don’t see on the pitch but is observed by the Data Analyst Sports, can help transform how the game is played by introducing new tactics based on the insights from that data.
For example, as NBA Teams continue to get insights into data, most teams have started taking more 3 point shots as opposed to 2 point shots. Data Science has also transformed the way teams play offense and defense.
Conclusion
This article discussed the responsibilities associated with the role of Data Analyst Sports. It introduced you to the importance of this role and the impact it can have on the field of professional Sports. It also explained the Business and Entertainment aspect of this role.
Share your understanding of the role of Data Analyst Sports in the comments section below!
Talha is a Software Developer with over eight years of experience in the field. He is currently driving advancements in data integration at Hevo Data, where he has been instrumental in shaping a cutting-edge data integration platform for the past four years. Prior to this, he spent 4 years at Flipkart, where he played a key role in projects related to their data integration capabilities. Talha loves to explain complex information related to data engineering to his peers through writing. He has written many blogs related to data integration, data management aspects, and key challenges data practitioners face.
