The past decade has shown a dizzying pace of technological advances and subsequent technological obsolescence. In 2010 consumers adopted the revolutionary iPhone 4 which first debuted FaceTime; today you can get an iPhone 4 for under $20. In just ten years, cutting-edge tech can transition from ‘behavior altering’ to valued roughly the same as lunch for two at a fast-food restaurant. It’s no wonder the multi-billion dollar sports industry has sent analytics rocketing forward at breakneck speed before they too are obsolete. Tech advances are being accelerated faster and faster in large part thanks to Artificial Intelligence (AI), and sports analytics are no exception. Sports analytics firms, such as LeansAI, are employing AI based algorithms to forecast the outcomes of sporting events with impressive results. LeansAI’s product is simple – predict which way a game leans with high precision. For example, the LeansAI algorithm uses tens of thousands of nodes to calculate the New York Mets have a 59.41% probability of winning over the San Francisco Giants, and the game will yield on average 7.31 runs scored.
“Over the past few years, we’ve been developing a proprietary, AI algorithm that takes mountains of data and boils it down to a few useful numbers,” said Mr. Chase, a Data Consultant at LeansAI. “The algorithm runs 24/7 using up-to-the-minute data on everything from historical stats to injury reports to weather forecasts. We employ these calculations using predictive analytics and AI models to generate precise probabilities of certain events happening in a game. Many human behaviors, especially those with well defined boundaries as we find in sports, follow statistical and mathematical undercurrents with amazing repeatability. AI is providing us insights we’ve never had before into sports trends and allowing us to make subsequent predictions that were non-intuitive just a few years ago.”
These types of precise probabilities have proved valuable to a variety of clients, including professional sports teams, sports bettors who want to incorporate the AI into their strategies, and sports fans who want to see how AI values their favorite teams.“What’s even more impressive,” said Mr. Chase, “is the algorithm’s use of reinforcement machine learning, or teaching the algorithm to learn from its own mistakes. The system can improve its performance over time by seeing how accurate its past predictions were factoring those results against future predictions. After a while the model starts training itself.”
Most professional sports teams employ analytics in coordination with their scouting departments, an idea from the early 2000’s thanks to the ‘Moneyball’ concept of Bill James and sabermetrics in baseball. This field has trickled into the other sports in recent years, and niche firms like LeansAI have carved their place. For those curious to see an AI sports algorithms in action, LeansAI publishes their algorithms’ past performance at www.leans.ai/past-leans. To date, AI shows promise in sports analytics over traditional methods and will only continue to improve in depth and insight as these models become more advanced.
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Company Name: LeansAI
Contact Person: Remi Handler
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Country: United States
Website: http://www.leans.ai/
