ENTERPRISE AI INSIGHTS
Are Coaches Open to AI Tools? An Initial Investigation in Baseball and Softball
This paper presents an initial study to gauge the openness of baseball and softball coaches to using AI tools to help evaluate and coach pitchers. While more work remains, findings suggest that coaches are generally open to adopting AI tools and can easily envision their utility. Potential challenges and suggestions were identified, all of which will guide the future development of AI tools.
Executive Impact: Transforming Pitcher Evaluation
Pitcher performance lacks standardized, quantifiable metrics, hindering precise evaluation and coaching. AI-generated metrics offer a solution to fill this void, enabling objective insights for player development and strategic decision-making. This study explores the readiness of coaches to adopt such transformative tools.
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Deep Analysis & Enterprise Applications
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Understanding Pitcher Accuracy
Participants identified velocity, movement, accuracy, and deception as key factors for pitcher effectiveness. Accuracy was defined as the ability to hit an intended location, acknowledging an 'acceptable miss'. A crucial insight highlighted that 'one degree of variation by the pitcher is 7 1/2 inches of variation by the time it gets to home plate,' underscoring the sensitivity of pitching precision.
Current Evaluation Challenges
Current methods primarily rely on strike rate, which is deemed unreliable due to factors like pitcher intention, batter swings, and umpire calls. There is no standardized metric for pitcher accuracy, leading to time-intensive manual data collection and delayed feedback. Coaches also struggle to translate data into actionable mechanical adjustments, often relying solely on their assessment.
Leveraging Tech for Decisions
Coaches currently use motion sensors, radar guns, and video recordings. Pitch recommendations are influenced by pitcher arsenal, real-time performance, and situational contexts (e.g., batter-pitcher matchups). AI tools could provide a more objective decision-making framework, helping confirm if a pitcher's 'best pitch' is truly their most effective in a given context.
Relevance & Usefulness of AI Metric
A quantifiable AI accuracy metric was perceived as 'awesome' and 'of high value', offering objective comparisons between pitchers. It was seen as extremely useful across all contexts: understanding performance, coaching & analysis, and scouting. The metric could help identify strike zone effectiveness, correlate with ERA/strike-to-ball ratios, and establish training roadmaps. Coaches recommend the metric be granular (by pitch type, location, pressure) and graphically presented.
Openness & Expectations for AI Tools
Coaches expressed general openness to AI tools that provide tailored feedback, contingent on accuracy, reliability, and clear understanding of how the tool works. Expected feedback includes specific strike zone effectiveness, optimal pitch sequencing, and insights into mechanical adjustments correlated with accuracy trends. Ease of setup, integration with existing tech, and data handling were identified as crucial factors for adoption.
7.5 inches
of pitch variation from 1 degree of error (4.1.1)
Enterprise Process Flow: Thematic Analysis Steps
Familiarizing with Data
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Generating Initial Codes
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Searching for Themes
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Reviewing Themes
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Defining & Naming Themes
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Reporting Findings
| Aspect | Current Methods | AI Tool Potential |
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| Metric Reliability |
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| Feedback Timing & Actionability |
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| Evaluation Scope & Depth |
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Voice of the Professional
"I would love to get my hands on whatever you guys are creating. I think there's a lot of promise in augmenting or confirming. There's a lot of gut instinct in pitch calling or working with pitchers, like “I think this'll work," but being able to quantify some of those things, I think would help significantly."
— Baseball/Softball Professional
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Your AI Implementation Roadmap
Our structured approach ensures a seamless transition and maximizes your ROI from day one.
01 Initial Assessment & Strategy
Understand current practices and goals, identifying areas where AI can make the most impact for pitcher evaluation and coaching.
02 Custom AI Model Development
Tailor AI models to specific team needs, ensuring accuracy and reliability for pitcher evaluation and feedback generation.
03 Integration & Pilot Program
Seamlessly integrate AI tools with existing systems, starting with a pilot to gather feedback from coaches and players.
04 Training & Rollout
Comprehensive training for coaches and staff, followed by a full rollout across the organization for widespread adoption.
05 Continuous Optimization
Regular monitoring and refinement of AI tools to adapt to evolving needs and maximize performance and utility over time.
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