As software continues to become not only a function, but a focal point of nearly all major industries across the globe, it’s clear the delineation between tech and other sectors will only continue to grow thin. FinTech could someday just become finance, medtech could one day be referred to simply as the medical field, and edtech could become inseparable from education itself. Until then though, we’ll continue living in an exciting time of transition, where what we know as ‘tech jobs’ are no longer reserved solely for the well-funded startups, Metas, and Googles of the world. 

One of the industries rapidly hiring for tech roles such as these is sports. From soccer to baseball to basketball, big data and analytics have taken the sports world by storm in recent years. And with Super Bowl season among us, we wanted to take a moment to focus attention on football in particular. Here’s how data is taking over the NFL, and how you can get involved in the profession. 

Table of Contents

The Evolution of Data’s Role In Football

The Roles Analytics Play, On and Off The Field

Creating A Talent Pipeline Through The AWS Big Data Bowl

Which Data and Analytics Roles Is The NFL Hiring For Right Now?

From Hobbyism To In-House: The Evolution of Data’s Role In Football

After winning a national collegiate championship this year as head coach of the Michigan University Wolverines, future hall of famer Jim Harbaugh announced that he would be making his return to coach in the NFL for the Los Angeles Chargers. Having taken the San Francisco 49ers to the Super Bowl in 2013, it’s been over nine years since Harbaugh’s last head coaching stint in pro football. In a recent interview with Colin Cowherd, when asked what has changed the most about the NFL since then, Harbaugh was clear: “The analytics piece, the study, and the reports have grown. Even 10 or 15 years ago when the coach was watching the tape putting together the game plan…it’s been aided so much by analytics regarding what coaches should watch. I’m not only talking about whether or not to go for it on fourth down or go for a two-point conversion. I’m talking the tendencies of players and play callers…We’re probably just a few years away from AI being a key part of football as well.”

But what started this shift, and how has it gained this much steam so quickly? Most people in and around the league give this credit to Brian Burke and his website, Advanced Football Analytics, which dates back to the mid-2000s. The site was a platform that helped pioneer the introduction of quantitative analysis into the sport. In an interview with Forbes, Super Bowl-winning data analyst for the Philadelphia Eagles, Ryan Paganetti, stated that “If Tom Brady is the G.O.A.T. (‘Greatest of All Time’) of quarterbacks, then Brian Burke is the G.O.A.T. of football analytics.” Since then, the snowball effect has been profound, spawning companies like Pro Football Focus, recently valued at $160 million, which boasts 20 million weekly users who rely on its advanced analytics and data. 

“I’m not only talking about whether or not to go for it on fourth down or go for a two-point conversion. I’m talking the tendencies of players and play callers…We’re probably just a few years away from AI being a key part of football as well.”

Coach jim harbaugh

Fast forward to today, and analytics have continued to become embedded into the sport – both inside organizations for executives and in the viewing experience for fans. The best example of this is the partnership between Amazon Web Services (AWS) and the NFL. Together, they form Next Gen Stats, an initiative aiming to enhance all areas of the league through advanced analytics and machine learning. Since their collaboration started in 2017, the impact has been immediate. From 2018 to 2022, helmets that were redesigned to prevent player injury based on analytics have reached almost 100% adoption throughout the NFL, which has led to a 25% decrease in concussions compared to 2015 to 2017. This early success has led to a full embrace of analytics into the league (the NFL uses 114 total AWS services today) through in-house hiring of various data-related roles for teams, along with many other areas as described below. 

The Roles Analytics Play, On and Off The Field

The NFL is currently using data science and analytics in a handful of key ways.

Player movement and injury prevention

The NFL and AWS employ player tracking technology, such as RFID chips (radio-frequency identification) in players’ equipment, to gather real-time data on their movements during games. This includes information on speed, acceleration, deceleration, and directional changes. Analysts synthesize this player tracking data to identify patterns and risk factors associated with injuries. Machine learning models can also be developed to predict the likelihood of injuries based on factors such as fatigue, biomechanics, and historical injury data. This all helps practitioners make informed decisions on helmet and padding design, potential rule changes, and more. 

Teams also use analytics to monitor players’ physical exertion and workload during practices and games. This information assists in implementing load management strategies to reduce the risk of injuries and optimize player performance over the course of a season.

Developing the NFL schedule

Creating the NFL schedule involves accommodating seemingly countless factors, including team travel, stadium availability, and broadcast requirements. Advanced scheduling algorithms powered by AWS use historical data, team preferences, and logistical constraints to generate a balanced and fair schedule. Analytics also play a role in ensuring teams have adequate rest between games, minimizing fatigue, and enhancing overall player well-being. The final schedule is designed to optimize performance by considering contingencies like short weeks, travel distances, when teams should have their ‘bye week’ (one week per season in which teams don’t play a game), and consecutive away games.

In-game situations 

Coaches and analysts use real-time data to guide decisions in their play calling and overall strategy during games, which includes analyzing opponents’ tendencies, player performance metrics, and situational factors. Statistical models influence critical in-game decisions for the coaching staff, like whether to go for it on fourth down or attempt a two-point conversion, by assessing the probability of success in various scenarios. 

The NFL Draft 

The use of machine learning and advanced analytics doesn’t stop when the season ends. Teams leverage analytics in the scouting process to evaluate potential draft picks. This involves analyzing college player statistics, performance metrics, and game footage to measure players’ strengths, weaknesses, and overall suitability for the team. These models aid teams in making more informed decisions during the draft. Analytics also influence draft-day strategies, including potential trade scenarios and player selections.

Creating A Talent Pipeline Through The AWS Big Data Bowl

The AWS Big Data Bowl is an annual competition organized by the NFL in collaboration with Amazon Web Services. The competition encourages data enthusiasts, analysts, and data scientists to apply their skills to solve real-world challenges in football analytics. The AWS Big Data Bowl provides participants with access to a large dataset of NFL player tracking data, covering the movements and actions of players during games and much more. Teams from all around the world then present their findings to a panel of experts, which includes representatives from the NFL and AWS. The winning solutions are selected based on the creativity, accuracy, and practicality of the analyses or models. 

To date, more than 50 Big Data Bowl participants have been hired in data and analytics roles in sports. “The Big Data Bowl has proven to be a strong pipeline for people aspiring to work in football analytics,” says Mike Lopez, NFL Senior Director, Football Data and Analytics. “The football data and analytics landscape continues to evolve and our partnership with Next Gen Stats, AWS, and the data science community positions us to provide the tools for innovative ideas, metrics, and research to enhance the game.”

Which Data and Analytics Roles Is The NFL Hiring For Right Now?

  • Manager of Business Intelligence: This person analyzes business data, including fan engagement metrics, ticket sales, and marketing data to inform strategic decisions for the organization.
  • Director of Research: This person analyzes player performance data, game statistics, and other relevant data sets to derive insights and develop statistical models to predict player performance, team outcomes, and trends.
  • Next Gen Stats Researcher: This person helps develop storylines using Next Gen Stats data to support various platforms at NFL Media, including NFL Network,, and the NFL’s social media platforms.
  • Football Analytics Specialist: This role focuses specifically on football-related analytics, such as play calling, strategy optimization, and in-game decision-making.
  • Machine Learning Engineers: This role works in developing machine learning models for player performance prediction, injury risk assessment, and other relevant applications.
  • Scouting Analytics Coordinator: This person works to integrate data analytics into the scouting process to identify and evaluate potential draft picks and free agents.

Ready to start your data journey?

The integration of data science and analytics into the world of football, particularly in the NFL, marks a transformative period for the sport. From the evolution of analytics shaping game strategies to the growing influence of artificial intelligence, the impact is undeniable. The intersection of sports and tech roles is no longer on the horizon – it’s happening now, and the NFL is at the forefront of this all coming together. If you’re a current or aspiring tech professional who has a passion for football, the best of both worlds is now within reach with this exciting new career path. For industry-vetted data science courses and Nanodegree programs, visit Udacity’s School of Data Science.

Dakota Nunley
Dakota Nunley
Content Strategy Manager at Udacity