What Can Sports Teach Us About BI/AI?

Back in the 90s, a Major League Baseball team sent a rejection letter to a fan and statistician asking to come work for them. He wanted to use data to win games, and he knew how to do it. So why did the Reds reject him? No other teams were using statisticians; no one valued the data. Since then, baseball has increased the amount of data they’re tracking from 100 bytes to 7TB per game.  

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Just as sports teams have evolved and now use that data to optimize player performance and win games, businesses can use similar, highly actionable insights to gain a competitive edge in efficiency, strategy, and talent management.  

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Whether you’re a business owner, manager, or business analyst, nothing is more important than winning new business. After that, you need to get clear on expanding your business and your share of the market. How do you compare to other similar businesses? How do you keep winning?

Sports can offer a blueprint for modern BI by demonstrating how to move beyond basic reporting to real-time, predictive decision-making.

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In this blog, we’re going to explore what sports can teach us about business analytics, and how to use BI/AI to enhance your game.

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What do sports teach us about analytics?

  1. Decision Making:  
    1. Data-backed decision-making powers success no matter what field you work in.  
    2. In sports, it enables coaches to refine lineups and executives to allocate business capital with precision.  
    3. Both business and sports involve systematic processes of data collection, analysis, communication of insights – followed by execution of decisions, requiring strong collaboration between analysts, technologists, and leaders.
  1. Advanced Analytics:
    1. ‍Both utilize large and extensive datasets, ranging from player tracking, to sales, marketing, stadium operations, and social media.
  1. Performance Optimization:  
    1. In sports, it's about player efficiency and team success; in business, it's about operational efficiency, customer satisfaction, and revenue growth. Teams are even using AI to predict and optimize player health and avoid injuries.  
    2. Businesses can also get creative with predictive analytics in optimizing their performance.
  1. Organizational Overview:
    1. To achieve a complete organizational view, both methods focus on breaking down silos and bringing together disparate data sources.
  1. Key Performance Indicators:
    1. Both leverage KPIs to measure success and drive continuous improvement

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Side-by-Side Sports Analytics vs. Business Analytics

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Sports Analytics Business Analytics
Descriptive to Predictive Descriptive to Predictive
Teams don't just look at box scores (descriptive); they use biomechanics along with play analysis to improve player performance (diagnostic), player tracking, and historical data to predict injury risks and future performance (predictive), and to determine optimal in-game strategies (prescriptive). Businesses can use data in a similar way. Looking at last quarter’s sales figures offers a descriptive view, but it’s possible to use market data along with performance data to assess market fit (diagnostic), to foresee future market trends (predictive), and assess employee performance and skillsets to meet those needs (prescriptive).
Finding Undervalued Assets Finding Undervalued Assets
Moneyball showed that focusing on non-traditional, overlooked metrics (for example, on-base percentage vs. batting average) can identify undervalued talent, allowing smaller teams to compete with wealthier ones. Organizations should scrutinize their Key Performance Indicators (KPIs) to ensure they are measuring what actually drives value, not just what is easy to measure. Are you tracking everything you upsell during negotiations with customers? What are your metrics around consultants’ “land and expand” deals and how are they making those deals?
Real-Time Adjustment Real-Time Adjustment
In sports, feedback is immediate; coaches know in real time if a defensive strategy is working, allowing them to adjust during a game. Businesses should build systems that allow for instantaneous, or near-instantaneous, analysis of data to adjust strategies in real time, such as dynamic pricing or supply chain management.
Data-Driven Culture Data-Driven Culture
Players are beginning to analyze their own performance data (for example, pitchers studying their spin rates) to improve their skills. The Cincinnati Reds have an app on their phone that allows them to see their biomechanics data about 30 minutes after the game and have created a culture of applying changes based on that data. Companies should foster "analytical amateurs" by making data accessible to front-line employees, not just the technical team, encouraging everyone to use data to improve their daily work.
New Data Types New Data Types
The use of cameras (such as Statcast or KinaTrax), radar, and GPS trackers on athletes provides a wealth of granular data that, when combined with video, uncovers hidden inefficiencies. AI, for example, can analyze pitch tipping and, although baseball does not currently allow data to be sent to the dugout during the game, teams can use that information to better respond to a pitcher’s signals the next time. Companies can tap into high-volume data sources, such as computer vision for foot traffic in retail or IoT sensors in manufacturing, to gain deeper operational insights.

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Using Sports as a Model for Uncovering Untapped Opportunities

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Whether you are a team owner, coach or just a fan, in the world of sports, nothing is more important than who won the game. Once you've determined that, the next most important thing is to determine who they’re playing next and how those teams and players compare to each other, considering both their overall stats and how they’ve performed against each other previously.

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Returning to the Major League team in the 90s: Don’t be the person who sends a rejection letter because you don’t yet know how to use the data. Instead, be the person who discovers underutilized opportunities and gains the market advantage.  

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