Hello friends! 👋

Welcome to this ✨  free edition ✨  of the SportsGrad Newsletter. Each week we take a deep dive into ‘how to get a job in sport’.

Meet Jay Magodia, Data Analytics Manager with experience at Cricket Australia, Microsoft, Paramount and REA.

Jay and I used to work closely in the Digital team at Cricket Australia, often I would come to him with requests for insights about our customers on cricket.com.au and the CA Live App (probably too many!).

Jay is an absolute wizard in this space and has a terrific ability to make complex topics easily understood, which is why he is the perfect person to explain the world of Data Analytics in sport!

If you’re thinking about careers in the field, you’re going to love this rundown from Jay.

Connect with Jay on LinkedIn

As you embark on your journey into the dynamic world of sports, it’s important to understand the diverse opportunities that await you. Data analytics plays a crucial role in two main domains within the sports industry:

1. Business Analytics - e.g. Help the Cricket Australia Marketing department understand customers that buy tickets

2. Performance Analysis - e.g. Help the Cricket Australia High-Performance department understand what shot Ben Stokes is likely to play to off-spin bowling

In this article, we will explore these domains and shed light on three primary career tracks:

1. Data Engineer 

2. Data Analyst

3. Data Scientist

Data Engineers are Architects of Data

Data engineers are the masterminds behind making data accessible, reliable, and usable. They play a pivotal role in preparing sports and business data, which fuels the creation of products and services that enhance the experiences of sports fans and teams worldwide.

Key requirements for this track include:

• Coding: You must love programming as it is a coding-heavy field. Proficiency in programming languages such as Java, SQL, Python, Scala, etc., is essential.

• Databases: A strong understanding of relational and non-relational databases and its functionality.

• Data processing & storage: A keen interest in data pipelines, data applications, data warehouse platforms, and data architecture.

• Cloud solutions: The ability to research and implement best practices for cloud storage and computing is highly valued.

• Analysis: Focusing on improving data quality, integrity, scalability, and overall performance.

• Collaboration: The capacity to work collaboratively with data science, product, and other business teams.

Working as a data engineer in the high-performance domain is particularly rewarding, especially for sports enthusiasts. You’ll have the opportunity to work with the latest technologies used by coaches and athletes to track training, recovery, nutrition, and game-day performance. Moreover, you’ll play a vital role in shaping the performance management of first teams and youth academies.

Check out this recently advertised dream role at Arsenal F.C. for future goals.

Data Analysts are Investigators and Storytellers

Data analysts make sense of vast amounts of historical data to generate insights that inform critical business decisions. They bring data to life by presenting findings in visually compelling ways, keeping the business context in mind. The role of data analysts can differ significantly depending on the domain:

a) Business Analytics: Data analysts can be part of marketing, product, digital, finance, or grassroots/community development departments. Key requirements for this track include:

• Programming: Proficiency in programming languages like SQL, Python, or R, progressing to an intermediate or advanced level for efficient work with large datasets.

• Visualization: Proficiency in BI tools like Tableau or Power BI is essential for automating analysis and conveying insights

• Soft Skills: Being a semi-technical role, skills like collaboration, communication and stakeholder management are highly valued

• Statistics: A basic to intermediate level of statistical knowledge is important for testing business hypotheses, using descriptive statistics for better comprehension

• Business Understanding & Storytelling: Ability to connect the dots and present findings to a non-technical audience while focusing on the big picture view for the business is the most valuable asset

Since the era of Moneyball, the demand for data analysts in high-performance domain has skyrocketed. While the technical and soft skill requirements remain the same, the sport element sets performance analysts apart from other data analysts in different industries.

Key aspects of being a performance analyst include:

• Knowledge of the Sport: Using contextual information to break down performance, technique, and skill levels into measurable events which form a part of the analysis.

• Building Relationships with Coaching Staff: Having a good relationship with coaches and players is crucial to gaining trust and receiving credit for the work done, or even being heard.

• Handling Tactical Tasks: Seamlessly transitioning between technical tasks like analysis and tactical tasks like video capture, event coding, video editing, and preparing reports.

• Tech Savviness: Being with new technology and the ability to work with third-party APIs, sensors, wearables, and video analysis software like Hudl Sportscode and Dartfish.

To expand your understanding of Performance Analysis, Shane Liyanage’s guide on Sports Grad offers valuable insights on what it takes to excel in this field and work for a Grand Slam Winner

Data Scientists are the Fortune-Tellers

Data scientists use algorithms and statistical models to predict the likelihood of future outcomes, differentiating themselves from data analysts. In addition to possessing the skills of data analysts, data scientists excel in the following areas:

• Applied Statistics: A solid grasp of applied statistics is fundamental to developing, evaluating, and interpreting machine learning models.

• Unstructured Data: The ability to handle diverse data types in native formats, including social media comments, sensor data, images, videos, and more.

• Machine Learning: Leveraging sophisticated algorithms to identify patterns, trends, and relationships within data which help solve business use cases like Fan segmentation, delivering personalized fan experiences, ticket deals, offers on merchandise, etc.

• AI: Exploring cutting-edge AI techniques and technologies to push the boundaries of product innovation like automated highlights, curating content based on preferred teams and athletes, near to real-time betting odds and more.

• AI for Performance analysis: Computer vision and deep learning techniques are the two disciplines of AI that is revolutionizing performance analysis in the world of sports. With the ability to process and interpret visual data from images and videos, these technologies enable coaches, athletes, and analysts to gain valuable insights into athletic performance. In sports such as soccer, basketball, or tennis, computer vision algorithms can automatically track player movements, detect key events, and extract relevant statistics from video footage. This allows coaches to analyze player positioning, tactical patterns, and game dynamics, leading to more informed decision-making during training sessions and matches. Check out these amazing startups driving innovation in this space

Swing Vision https://swing.tennis/

Fulltrack AI👇

By pursuing a career in data engineering, data analysis, or data science, you have an incredible opportunity to make a mark in the sports industry. Embrace your passion for sports, hone your analytical skills, and embark on a rewarding journey where you can shape the future of sports through data-driven decision-making and innovation.

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