Growing up in the Netherlands, American football was largely a foreign concept to me. My version of football was The Beautiful Game, or as most Americans know it, soccer. Football, futbol, soccer, or whatever else you call it, will always be something I’m deeply passionate about, especially my hometown team, Ajax.
When I joined Amazon and Seattle became my new home, I began to see how my colleagues shared this same level of passion for American football – and particularly the fervent fans known as “The 12s” of the local team, the Seattle Seahawks. As I started to better understand this version of football, it was easy for me to get excited about the game as well as what was happening behind the scenes. Coaching staffs, decision makers, and even the announcers are using data to make real-time decisions, each team constantly working to gain even a fraction of an advantage over its opponent.
One of the things that I find most interesting about football is how the evolution of technology is having an impact on its progression. In my opinion, the Seahawks are one of the best examples of this, where they have been at the forefront in adopting new technology, like machine learning (ML), Internet of Things (IoT), and serverless architecture, to make improvements from player safety to performance on the field.
But all of these technologies start with data. That’s why last year the Seahawks selected AWS as its official cloud partner and have worked with AWS to build a data lake, a centralized repository that allows organizations to store, govern, discover, and share all of their structured and unstructured data at any scale.
The Seahawks adopted a serverless architecture, with solutions like Amazon S3, AWS Lambda, AWS Fargate, AWS Step Functions, and AWS Glue, to build their data lake and ingestion pipeline. Their data originates from a variety of on-premises and third party data sources, such as NFL Next Gen Stats (powered by AWS), Pro Football Focus (also powered by AWS), player telemetry data from IoT sensors, and tagged plays based on third-party applications.
With this data lake, the Seahawks hope to improve talent evaluation and acquisition, player health and recovery times, and game planning. Let’s look a little closer at these three areas, how technology can fuel improvements, and the architecture behind the Seahawks’ data lake.
Historically, football teams evaluated potential players by having scouts go to as many games as possible and poring over videotape. Obviously, this approach has limitations – for example, the evaluation might not be objective, and it’s very time consuming.
That’s why the Seahawks supplement traditional scouting with AWS analytics for player evaluation. First they collect data about the player, like the size of the school the player comes from, the position they play, and the roles they’ve played within that college team’s style of play. Then they take data about the Seahawks, like their own style of play, the current players on the team, and more, and use an ML model to evaluate whether or not the player is a good fit.
During the draft, the Seahawks might have ranked a player higher than other teams using this analysis, allowing them to trade down during the draft and still select the right player for the team while acquiring more draft picks. This type of modeling has also helped during free agency where the Seahawks have signed a player that creates the most value for the team, in turn making the biggest impact without over-spending against the NFL’s salary cap.
In addition to having the right data to make these important decisions, the Seahawks have a dashboard that makes the information easy to understand and take action on. Figure 1 gives an example of the kind of analytics dashboard used by the Seahawks in preparation for the 2020 draft.
Nothing is more important to the Seahawks than the health and safety of their players.
