Will AI Replace the Front Office in Pro Sports?


On March 15, 2023, the new season of the national football team officially begins.It also means starting free agency periodwhen a team makes a trade for a player who is no longer under contract with the former team; each new contract can mean millions of dollars in the team’s budget.

So getting it right is crucial. As with any business, NFL executives and the leaders of other professional sports teams must make decisions about how best to spend their limited budgets on the return on investment they will get from assets, in this case players. Make smart bets on the odds, including return on investment (on and off the field) relative to expected performance, future injuries and other factors.

But what if, this year, AI could tell us how many games a player has left in his career, how many points they will score next season, or whether they will suffer a major injury in the near future?

While free agency and other recruiting mechanisms have existed for decades, how Decisions about players making are changing rapidly. Specifically, applying AI-based techniques to massive sports data sets is enhancing the front office’s ability to make decisions about players—who to recruit, develop, bench, or trade. It will permanently change the way all professional sports operate.

But will artificial intelligence replace the front office of sports teams anytime soon?

While this new technology will certainly enhance human decision-making, we don’t see it replacing general management teams in sports or other industries in the near future.

Game-changing predictive capabilities

Some of the growing number of AI-powered sports-focused products aim to help team decision makers predict player injuries and longevity. Knowing the likelihood of injury in a given time frame has a big impact on recruiting, as teams will naturally target players who are expected to remain injury-free for a longer period of time. Industry executives have always had some experience-based intuition about factors that contribute to injury, such as time and field “mileage.” Sometimes these predictions are correct, but often they are not.

The difference now is that AI can support some of the conventional wisdom — for example, in the NFL, wide receivers over the age of 30 are more prone to injuries and other challenges — but can also provide more specific estimates of the likelihood of injury or decline in performance , and what that means for the availability of specific players, and what that might cost teams. a company, Probabilistic AI, claiming to be 96 percent accurate in predicting which players will miss games next season. Executives can use these results to move from “I think this might be a significant factor” to “I know this is a significant factor and can estimate its impact and cost with unprecedented confidence.”

AI-generated insights go far beyond what’s available or supported by intuition. For example, Probility AI trained its injury prediction model on data from a specific NFL team, as well as other public and private data sources, to understand the impact of factors such as where a particular player went to college, the mix of head and assistant coaches they held, and more. The practice and workload demands that come with it. While these nascent insights warrant further study, they demonstrate the depth of AI in predictive analytics.

So instead of trying to secure the best wide receiver overall, the general manager can find the best receiver for their team, Predictions of future injuries and performance based on artificial intelligence. Since players often have different predicted career lengths and performance outcomes, as do coaches, field conditions, or teammates, this creates an arbitrage situation where a player’s market value varies depending on the team the player plays for.

Multiple NFL teams are deploying AI technology from Probility AI and other sources, and for good reason: Failure to do so puts them at a disadvantage compared to their AI-equipped counterparts.Of course, such models are also used in other sports, such as football and basketball Create value, and across business units Strengthening activities Including informing decision making, improving productivity and better serving customers.

enhance, not replace

So, as AI gains predictive power in key aspects of the sport — injuries, trade timing, etc. — will it replace the front office?

In short, no.Now, think of AI as Increase people’s decisions. It won’t replace executives, but it will help them make better decisions, especially in areas that are more prone to human error and bias, such as hiring primarily on gut instinct and doing “what worked before.”Where magic ball Over the past 20 years, AI has been using player statistics in a more rigorous and systematic way, using deeper learning to better predict performance.

With accurate player availability predictions for all active players, decision making is significantly improved in three ways:

  • Risk Management: For example, if an efficient wide receiver is likely to be injured, the team may invest more in a talented backup to minimize the decline in team performance during the injury.
  • Training and targeted interventions: If the AI ​​indicates that a player is prone to injury, teams can tailor training, nutrition or other protocols to that player to reduce the likelihood of injury. Alternatively, teams may choose to reduce player workloads while mitigating risk.
  • Personnel decisions: By identifying factors that predict injuries or other unavailability, teams can draft, trade or otherwise acquire players they believe are more likely to be available throughout the season. Additionally, teams may choose to trade players who may be injured.

Savvy executives also incorporate injury forecasting into financial decisions. That is, AI can not only generate predictions about player availability, but feed those predictions into a financial decision engine, enabling team leaders to create granular metrics for expected productivity per dollar spent. For example, a running back who is expected to play in only 50% of games in a given year is functionally twice as expensive as a running back who can play in every game for a similar fee. By considering the price paid for each result (yards gained, tackles scored, points scored, etc.), teams can allocate their dollars in the most efficient way, optimizing productivity per dollar spent.

However, technology alone is not enough. While software can analyze player engines and resource allocation, the judgment and risk tolerance of sports executives ultimately must choose among the inevitable trade-offs and dictate the decisions made. We shared more about that in the previous section.

Still, AI is definitely a game-changer for professional sports, replacing informal and even statistical-based decision-making as the engine of comprehensive systems fueled by big data and unprecedented predictive power.

It’s easy to see how better forecasts generated by AI could have a huge impact on any business. An analogy here is predicting when worker performance in a labor-intensive industry like construction will suffer, or when large pieces of equipment powering a manufacturing plant or refinery will malfunction or fail, and act before a costly event occurs. Precaution. This approach works for any business with aging resources.

More broadly, predicting demand for anything from apparel to corn will allow business leaders to make better production decisions, including those related to supply chains and other areas. Other AI-based algorithms can predict competition.The list goes on, and AI is already being used in these and other ways in various fields, which helps explain why AI startups are gaining Nearly $1.4 billion in funding 2022.

don’t cross the line

Of course, there are limitations to using predictive AI, which further reinforces the idea of ​​augmentation versus replacement.

For example, when it comes to predicting NFL injuries, while new technologies can guide decisions about recruiting, trading, and how much to pay specific players, coaching staff must strategically consider team-wide dynamics. An AI might tell you it’s time to replace an injury-prone running back with a player with a given profile, but executives must consider how best to integrate the new recruit into the team. After all, the total risk is spread across all participants and their interactions.In this regard, AI is also getting better at understanding the big picture of teams and their impact, starting with sports with smaller starting teams such as hockeywhich places no more than 6 players on the ice at a given time.

Also, it’s important to understand that AI-based products aren’t providing a definitive “answer,” but predictions with confidence intervals around it. As technology improves, this interval will shrink, but there will always be some looseness associated with predictions, and this is where human judgment is crucial.

In the end, AI will undoubtedly be a game-changer in sports, empowering management and coaches with unprecedented predictive power, allowing them to make more and more decisions that have a significant impact on performance and returns, and providing players with insights to extend their Fans are excited to see their careers and keep more players playing. But it’s still an enhanced story where leaders use new technologies to inform their experience-based intuitions, must make strategic decisions to the best of their ability, and remain accountable for what’s happening on the field and on their balance sheets.


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