January 30, 2023

Sign Up for Our Blog, Resources, and Tips!


  • Please prove you are human by selecting the Star.
  • This field is for validation purposes and should be left unchanged.

Taking Football to the Next Level

We’re in the last few weeks of the NFL football season, with the final two teams heading to the Super Bowl. As the most watched sporting event in the US, the Super Bowl will be watched by around 99 million people. Attendance at the big game brings in anywhere upwards of $66.5 million in revenue to the NFL. Advertising also plays a huge role – the average 30-second commercial costs $5.6 million earning a total of $545 million in revenue last year. This is the time of year when football attracts viewers, new and longstanding.

In our household, football has been a family affair for many years. From watching our favorite college football team together on Saturdays, our children helping their dad coach football at our school and gathering on our couch on Sunday afternoons to watch our favorite NFL teams play. As I have watched football over the past few decades, I can’t help but notice how far the sport has come in collecting and using data to analyze individual and team performance to drive decision-making. While watching a recent playoff game, the Next Gen Stats for the game or a player were displayed at least 2-3 times per quarter. For example, when examining how long a player is on the field Next Gen Stats uses this information to detect the route the player will take on the field which is then analyzed to determine the team’s win probability.

Data-driven Football

At each football game cameras are used to gather roughly 1.4 million data points on the field. That means 10 data points are collected each second for every player. These data points include the distance a player traveled, how fast they moved, and the amount of time spent on the field. Tracking devices and sensors, such as heart rate monitors, diet monitors, and sleep trackers are utilized to further data collection on football players off the field. This data is primarily used to track the performance of players and even aid in training routines and help to prevent injuries. During training sessions, players have wearable technology, in their helmets and shoulder pads, that measures their workload and fatigue to gauge a player’s fitness level. Coaches and trainers can use this data to help prevent players from overworking their muscles. These are good examples of leading measures – measures that are collected before or during games, and whose performance can have an impact on the outcome of a game. For example, a leading measure during a game such as the number of times a quarterback is hurried in the pocket may have an effect on the number of complete passes, number of interceptions, number of sacks, and ultimately on the team’s ability to score points.

Outside of games and practices, football teams have also begun using data to scout and sign players to best prepare their tactical advantage for the season ahead. Next Gen Stats has created a model used during training to predict a prospect’s chances of success in the NFL. Players are examined based on 3 key data points: athleticism, size, and college production metrics. The athleticism score is based on measurable drills during training, speed, agility, etc. The size score is how big a player is relative to other players in the same position. Their production score is how productive a player was in college based on their on-field performance. These results are analyzed and turned into a score from 50 – 99, measuring the potential success of each individual player.  As teams are constantly looking to seek out the ‘next big thing’, they are now doing so by implementing these analytical approaches.

The development of data collection in football is just getting started. Data collection in the NFL began in 2015 when all football stadiums were equipped with RFID scanners. In 2016 the balls used during the game were tagged with scanners to collect information such as how many miles per hour a pass is thrown. In a few years, what is currently “state of the art” will be the norm. As technology becomes more innovative, and the need for more advanced applications of data collection and analysis arise, we can expect to see it integrated in the NFL.

Lessons from the NFL

For many organizations, data-driven decision-making is not new. However, like the NFL, the collection and use of data to drive decisions is a journey and will change and mature as the ability to collect and analyze data improves.

In the NFL, we can likely boil down any team’s goal into a simple statement – win the Super Bowl. This singular focus drives all other actions and decisions, including what data to collect, and how to utilize the data to increase a team’s chances at achieving that goal. Organizations can take a similar approach by asking ‘what is our singular (or few) goal or focus?’ Based on the answer(s), an organization should adjust and improve its data collection and analysis strategies. As an example, a hospital system can collect granular data on what providers a patient sees, lab and test results ordered and their results, diagnoses given, medications prescribed, and more (assuming the patient stays within the hospital system for care interventions). If a hospital system has a goal to better serve the needs of their community, they may want to focus data collection and analysis on common or emerging diagnoses found in their patient population. This information could help determine what services to provide more or less of. Alternatively, having a clear definition of a problem or performance gap can also guide an organization’s need for data collection and analysis. For a hospital system, a problem might include long wait times for patients to see a specialist they were referred to. In this case, data collection may include wait times to be scheduled with specialists, which specialists have the longest wait times, whether patients are rescheduling or canceling appointments, and more.

With the multitude of data being collected in our organizations, or by our favorite sport (here’s an interesting infographic on how much data is collected in a day), it is vital to align what data we collect, how we collect it, and how we analyze it to make decisions to a singular (or few) primary goals or key problems being addressed.

The next time you watch a sporting event, take note of what data is being collected and how it is being used, you may be able to take a play from their playbook.