Fantasy sports have come a long way since their humble beginnings as pen-and-paper leagues among friends. In today’s world, artificial intelligence (AI) is the new MVP, revolutionizing how players draft, trade and manage their fantasy teams. While AI brings undeniable benefits, it also comes with its fair share of challenges. Yogonet brought this topic to us in their article, “How AI and data analytics are revolutionizing fantasy sports.”

AI’s ability to analyze massive amounts of data in real-time has transformed how fantasy sports players approach the game. Machine learning algorithms can parse player stats, injury reports, weather conditions and even historical trends to provide recommendations tailored to a specific league’s scoring system.

Fantasy sports platforms now offer personalized insights, thanks to AI. Tools like player comparisons, trade analyzers and predictive models allow users to understand the strengths and weaknesses of their team better.

While AI provides valuable insights, some players might rely too heavily on it, losing the “human touch” that makes fantasy sports enjoyable. The thrill of making a gut-based decision or unearthing a sleeper pick could fade when everything is AI-optimized.

Many advanced AI tools are locked behind paywalls, creating a gap between players who can afford premium subscriptions and those who can’t. This could lead to a more unequal playing field, with wealthier participants dominating leagues.

AI’s impact on fantasy sports is undeniable, offering tools that can make the game more engaging and competitive. However, it’s essential to strike a balance. Fantasy sports have always thrived on the unpredictability of real-world events and the creativity of their players. While AI can enhance the experience, it’s crucial to preserve the human element that makes the game so addictive in the first place.

The biggest challenge is that most organizations have little knowledge on how AI systems make decisions and how to interpret AI and machine learning results. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and it potential biases.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, uniquely positioned to help you in your AI journey.