Lecture Notes: Forrester Technopolitics with Ari Kaplan

Introduction

• Speaker: Mike Waltieri, Forrester Principal Analyst
• Guest: Ari Kaplan, President of AriBall, involved in Major League Baseball using predictive analytics.

Overview of AriBall

• Purpose: Use analytics to predict economic impacts and performance of baseball players.
• Areas of Focus:
  • Above the Field: Forecast economic impacts and future performance, assess risks like "dollar on the muscle."
  • On the Field: Work with players and coaches using analytics to find strengths and weaknesses.

Predictive Analytics in Baseball

• Adoption by Teams:
  • Varies by team due to different cultures and personalities.
  • Teams aim to maximize investments on and off the field.
• Data Sources:
  • Team Collected Data: Proprietary information collected by teams.
  • Third-Party Vendors: Provide detailed pitch data, including pitcher's hand position, ball spin, and movement.
• Data Utilization:
  • Analyze patterns in player behavior.
  • Develop game plans based on data insights.

Recent Developments and Future

• Pitching and Hitting Analytics:
  • Teams examining pitching command and hitting mechanics.
• Data Collection:
  • Increasing use of camera setups in fields to collect comprehensive game data.
  • Potential use of sensors for more detailed analytics.

Importance of Data and Technology

• Quantifying Player Performance:
  • Objective metrics to compare players, e.g., throw speed, positioning.
• Role of Data Scientists:
  • Recognized as key contributors to team success due to their analytical capabilities.

Conclusion

• Predictive analytics play a crucial role in modern baseball.
• Data scientists and computer scientists are becoming increasingly important in team staff.

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End of Notes