Monte carlo simulation sports

Massachusetts Institute of Technology

Technology Licensing Office

  • Disclose & Protect Your Intellectual Property
    • Submit Your Disclosure
    • Disclosure FAQs
    • Share Research Materials
      • Incoming MTA Requests
    • Forms that Protect IP Rights
  • Learn About Intellectual Property
    • Technology Transfer Overview
    • Technology Transfer Process
    • Ownership
      • Inventions and Proprietary Information Agreement (IPIA)
      • Waiver of Ownership
      • IPIA Obligations for Inventors
      • IPIA FAQs
    • Patenting an Invention
    • Conduct Your Own Patent Search
    • Software and Open Source Licensing
    • Commercializing an Invention
    • MIT IP Policies
    • Lincoln Laboratory
    • Information for Students
    • Licensing MIT’s Intellectual Property
  • Explore MIT Technologies
    • View Technologies
      • Ready to Sign Technologies
    • Sponsoring Research
  • Use of MIT’s Name & Trademark
    • Using MIT’s Name
    • License MIT’s Trademark
  • Resources
    • Licensing to Start-ups
    • Payment FAQs for Licensees
    • News & Events
    • Educational Resources
      • TLO Seminar Series
  • Engage with the TLO
    • About the TLO
    • COVID-19
      • COVID-19 Technology Access Framework
    • Our Team
    • Career Opportunities
      • Internship Program
      • Marketing Internship
    • Contact Us
    • TLO Organization Chart
    • TLO Data
      • TLO Statistics
  • Disclose & Protect Your Intellectual Property
    • Submit Your Disclosure
    • Disclosure FAQs
    • Share Research Materials
      • Incoming MTA Requests
    • Forms that Protect IP Rights
  • Learn About Intellectual Property
    • Technology Transfer Overview
    • Technology Transfer Process
    • Ownership
    • Patenting an Invention
    • Conduct Your Own Patent Search
    • Software and Open Source Licensing
    • Commercializing an Invention
    • MIT IP Policies
    • Lincoln Laboratory
    • Information for Students
    • Licensing MIT’s Intellectual Property
  • Explore MIT Technologies
    • View Technologies
    • Sponsoring Research
  • Use of MIT’s Name & Trademark
    • Using MIT’s Name
    • License MIT’s Trademark
  • Resources
    • Licensing to Start-ups
    • Payment FAQs for Licensees
    • News & Events
    • Educational Resources
  • Engage with the TLO
    • About the TLO
    • COVID-19
    • Our Team
    • Career Opportunities
    • Contact Us
    • TLO Organization Chart
    • TLO Data

Monte Carlo Baseball Simulator

Primary tabs

Applications

The Authors have developed a baseball simulator designed to simulate Major League Baseball games based on players’ statistics. By tweaking the settings of the simulator, the user can test different strategies to determine which strategies will lead to the most run production and therefore the most long-term success.

Problem Addressed

A predictive model of baseball games based on strategy simulations can be an interesting and useful tool for athletes, coaches and educators. There are a few Monte Carlo simulation packages that exist for the purpose of modeling baseball strategy; however, many of these simulations are exceedingly complex and do not provide source code. The Authors’ copyrighted software uses a comparatively very simple approach but faithfully reproduces observed outcomes in MLB games.

Technical Description

This program runs using MATLAB™. The simulator generates probabilities of given outcomes of certain plays during the course of a baseball game based off true MLB statistics. It then generates random occurrences of each play, thereby simulating what would happen in a real game. The simulator first checks if there was a hit recorded on a play, then either moves the runners up or records an out. After the simulator logs three outs in a given inning, it resets the bases and continues to simulate. The simulator performs this nine times to represent nine innings, then resets the count to zero and begins to simulate a new game. After many games, the predictions will converge to their true expected value at a rate of the square root of the number of games simulated.

One Reply to “Monte carlo simulation sports”

Comments are closed.