The National Football League (NFL) in its quest for better means of evaluating athlete stats and rating players, has embraced the use of AI. Now, the NFL lines up both scout insights and predictive algorithms to gain deeper insights into player performance. Still, it doesn’t end there.
There are many more ways American football harnesses the power of artificial intelligence besides simply rating players. In this article, we will delve into how the NFL does this. Let’s go!
History Of Artificial Intelligence Applications In The NFL
Before looking into the various ways AI comes in handy on the field, let’s look at how long American Football has been using algorithms. Starting from the early 1990s, the NFL has been using algorithm simulations to evaluate game strategies and player statistics. Over time, this has evolved into a wider range of purposes as can be seen below.
Ways Artificial Intelligence Helps The NFL Rate Players And Improve The Game
Allows For Objective Analysis
In conventional settings, players get their evaluations done based on the subjective insights obtained from scouts, analysts, and coaches. But although these opinions are invaluable, they may sometimes get biased and are often limited. Hence, the adoption of artificial intelligence technology by American football makes it possible to circumvent this issue.
Artificial intelligence introduces a new approach to rating players based on calculated data and this makes player analysis much more objective. The insights derived this way transcends from just a gut feeling to data-based rating.
Collection And Analysis Of Data
During games and practices, a huge amount of data regarding players’ performances can be garnered. And this is where artificial intelligence comes in to make things easier and faster. It collects stats ranging from the player’s speed, movements, acceleration, distance covered, and a lot more.
This is done by imbuing sensors into the player’s equipment and in the stadiums. It then leads to an unimpeded stream of data needed for player evaluation. Artificial intelligence then uses algorithms to sort through these statistics to figure out patterns that mere human scrutiny may overlook.
Predictive Analytics And Performance Stats
Beyond simple stats like speed or distance covered or even touchdowns scored, AI evaluation goes on to more complex figures. It delves into other statistics that help give a better idea of a player’s contribution to the team. And it varies depending on the position the athlete plays.
For instance, for quarterbacks, this includes analyzing specifics such as defensive formation performances, speedy decision-making, as well as pass accuracy. Also, for defensive players, this could mean measuring their tackling effectiveness, reaction time, and coverage skills. And running backs may have their own evaluation based on evasive tactics, vision, and many others.
But all of this is based on past metrics and the use of artificial intelligence doesn’t end there. It goes on to offer predictive analysis of the various ways players may react in certain situations. This comes in handy when determining the right strategy to use in-game based on previous evaluations of player strengths and weaknesses.
Prevention Of Injuries And Determining Overall Player Health
Outside player performance on the field, algorithm-driven analysis is also integral to preventing injuries and determination of player health. By taking into consideration psychological data and player movements, it detects any unusual behavior that may pose a chance of injury.
And with the information provided, teams can change their training regimen and game tactics to lower the risk of injury. This presents a proactive stance to ensure the safety of players. It also helps make sure the best players are not stopped from playing by otherwise avoidable injuries.
Future Drafts
Picking players for future drafts, in particular, is one crucial way American football teams apply the use of algorithms. Just as in other sports, having the right athletes on the team could make all the difference between the winners and losers of the championship. But it is no easy task to come by.
By tracking player physics, past performance, and personality characteristics, AI determines those players who are the right fit for a team. It evaluates historical stats to identify certain patterns consistent with exceptional athletes. Coaches and scouts can then make informed decisions based on these insights.
Challenges And The Human Touch
The use of artificial intelligence in American football has yielded great results but it doesn’t mean there are no challenges to it. For instance, there is no particular privacy policy that restricts the potential misuse of data collected. Also, as is common to most technical tools, a little glitch or faulty sensor could result in false inferences.
However, this is where the human touch comes in. It is best to remember that AI is only a tool that helps make data-based decisions and not a way to replace the human element. So by combining human expertise with the information obtained from algorithms, you get an effective hybrid approach to the game.
Conclusion
The AI-based approach of rating players brings a more in-depth understanding of player stats and the game in general. As technology keeps advancing, we expect to see more ways algorithms would come in handy in the NFL.
