Welcome to the Baseball Machine Learning Workbench

The Baseball Machine Learning Workbench is an interactive web application. Explore various analytics, decision intelligence & Machine Intelligence techniques using historical baseball data.

Prediction breakdown
Variable Response
Prediction Matrix
Available Scenarios:
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What-If Analysis - Rules Engine This scenario showcases how a simple rules engine can be used to attempt to predict baseball Hall Of Fame Induction. No Machine Intelligence is used, rather a simple rule:
If sum of career HRs >= 500 then Hall of Fame Induction is true (else Hall of Fame Induction is false).

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What-If Analysis - Single Model This scenario showcases how a Machine Intelligence model can be used to attempt to predict baseball Hall Of Fame Induction. Machine Intelligence is used to classify the batter baseball data.
The key difference over the rules engine approach is that a probability is returned; allowing a decision to be made on a probability threshhold and other statistical metrics.

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What-If Analysis - Multiple Models This scenario showcases how multiple Machine Intelligence models can be used to attempt to predict appearing on a Hall Of Fame Ballot & Hall Of Fame Induction. Machine Intelligence is used to classify the batter baseball data.
The multiple models implementation showcases progression to Hall of Fame Induction. First, the player needs to be considered on being on the Hall of Fame Ballot then considered for Hall of Fame Induction. This can be used to aid the decision maker in providing multiple supporting conclusions provided by machine learning models (experts).