Will Artificial Intelligence Replace the Front Office in Professional Sports?

March 15, 2023 marked the official start of the new National Football League season. It also meant the beginning of the free agency period, when teams make deals for players no longer under contract with their former teams; each new contract can mean literally millions of dollars of a team’s budget.

So getting the recruitment right is crucial. Like any business, NFL executives and the leaders of other professional sports teams must make decisions about how best to allocate their limited budgets, placing informed bets on the ROI they will get from resources (the players, in this case), including in relation to expected performance (on and off the field), future injuries and other factors.

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

While free agency and other recruitment mechanisms have been around for decades, As decisions about which players to make is changing rapidly. In particular, the application of AI-powered technologies to massive sports datasets is improving the ability of front offices to make decisions about which players to recruit, develop, bench or trade. And it will change how all professional sports work, permanently.

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

While this new technology is definitely augmenting human decision-making, we don’t see it replacing general management teams in the foreseeable future in sports or any other business.

Revolutionary predictive power

Among a large and growing number of AI-powered sports-focused offerings, some are intended to aid team decision-makers with injury predictions and athlete longevity. Knowing the likelihood of injury within a specific time frame has a big impact on recruiting, as teams would naturally target players who are expected to stay injury-free the longest. Industry executives have always had an experiential intuition for factors that go to hurt, like time and mileage in the field. Sometimes these predictions are true, but often not.

The difference now is that AI can back up some conventional wisdom in the NFL, for example, a wide receiver over the age of 30 is more prone to injuries and other challenges, for example, but it can also provide much more specific estimates the likelihood of injury or diminished performance, and what that means for a particular player’s availability and how much it could cost the team. One company, Probility AI, claims 96% accuracy in predicting which players will miss out next season. Executives can use these findings to go from I think this is probably a big factor to I know this is a big factor, and can estimate the impact and cost with unprecedented confidence.

Insights generated by AI go far beyond those that exist or are supported by intuition. For example, Probility AI trained its injury prediction models on data from specific NFL teams, along with other public and private data sources, to understand the impact of factors like where a particular player went to college, the combinations of heads and assistant coaches they played under, and resulting practice demands and workload. While these nascent insights warrant further research, they show just how deep AI can go in its predictive analytics.

As a result, instead of general managers trying to secure the absolute best wide receiver, they can find the best receiver for their team, based on artificial intelligence predictions of future injuries and performance. Since players typically have different career lengths and expected performance outcomes with different coaches, field conditions or teammates, this creates an officiating situation where the market value of the player varies depending on which team he plays for player.

Several NFL teams are implementing AI technologies from Probility AI and other sources, with good reason: Not doing so would put them at a disadvantage against their AI-equipped peers. Of course, such models are also used in other sports such as football and basketball to generate value and across all business sectors to improve operations including making informed decisions, increasing productivity and better serving customers.

Augmentation, not replacement

So as AI gains predictive capabilities in all key dimensions of sports injuries and trading times, will others replace the front office?

In short, no. For now, think of AI as increasing human decision making. It won’t replace executives, but it will help them make better decisions, especially in areas where human error and bias are more likely, such as basing recruiting largely on intuition and doing what has worked before. Where the Moneyball movement of the past 20 years has used player stats much more rigorously and systematically, AI uses deeper learning to make even better predictions about performance.

With accurate player availability predictions for all active players, decision-making is greatly improved around three dimensions:

  • Risk management: If a productive wide-receiver is likely to be injured, for example, a team could invest more in talented backups, to minimize the decline in team performance during the injury.
  • Training and targeted interventions: If the AI ​​suggests a player is prone to injury, teams can target that player with customized training, nutrition, or other regimens to reduce the likelihood of injury. Alternatively, a team could choose to reduce a player’s workload while also reducing risk.
  • Personnel Decisions: By identifying factors predicting injuries or other unavailability, teams can select, trade, or otherwise acquire players they believe are more likely to be available throughout the season. Additionally, teams may choose to trade players for whom injury appears likely.

Experienced executives will also integrate injury prediction into financial decision-making. That is, AI not only generates predictions about player availability, but it can feed those predictions into a financial decision engine, allowing team leaders to create granular metrics on expected productivity per dollar spent. For example, a running back who is expected to play only 50% of games in a given year becomes, functionally, twice as expensive as one of similar cost who may play every game. By considering the price paid per accomplishment (yards gained, tackles made, runs scored, more), teams can allocate their dollars extremely efficiently, optimizing productivity for every dollar spent.

However, technology alone is not enough. While the software can analyze player engine and resource allocation, sports executives’ judgment and risk tolerance must ultimately choose between inevitable trade-offs and dictate the decisions made. We share more about this in the last section.

However, AI is an absolute game changer in professional sports and is replacing informal or even statistics-based decision-making as the engine of a complete system powered by big data and unprecedented predictive power.

It’s easy to see how better predictions generated by AI would have a huge impact on any business. A close analogy here would be to anticipate when worker performance in labour-intensive industries such as construction may suffer, or when large equipment such as that which powers manufacturing plants or refineries may malfunction or fail, and take preventative measures before a costly accident. The approach would apply to any business with outdated assets.

More broadly, forecasting demand for everything from clothing to corn would enable business leaders to make better decisions about production, including in relation to the supply chain and other areas. Other AI-powered algorithms could make predictions about the competition. The list goes on, and AI has already been applied in these and other ways across industries, helping to explain why AI startups received nearly $1.4 billion in funding in 2022.

Don’t go out of bounds

Of course there are limitations to using predictive AI, further reinforcing the idea of ​​augment over replacement.

With regards to NFL injury prediction, for example, while new technology can drive decisions about recruiting, trades, and how much to pay a particular player, the coaching team must think strategically about the dynamic of the entire team. The AI ​​might tell you it’s time to replace an injury-prone running back with a player of a certain profile, but an executive will have to think about how best to integrate the new recruit into the team. The total risk, after all, is distributed among all players and their interactions. Again, AI is getting better at understanding the big picture of teams and its implications, starting with sports with smaller starting teams, like hockey, which puts no more than six players on the ice at a time. .

Also, it’s important to understand that AI-powered bidding doesn’t provide a definitive answer, but makes a prediction with a confidence interval around it. That range will shrink as the technology improves, but there will always be some slack relative to prediction and it is, again, where human judgment is key.

In the end, AI is definitely a game changer for sports, giving front offices and coaches unprecedented predictive power to make a growing range of decisions with big implications for performance and returns, providing players with insights to extend their careers and keep more players in the game, which is getting fans excited. But it’s still a rising story, one where leaders, using new technologies to inform their experience-based intuition, must make strategic choices the best they can and maintain accountability for what happens on the ground and on the budget.

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