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).

What-If Analysis - Single Model This scenario showcases how a Machine Intelligence model can be used to predict baseball Hall Of Fame Induction. Machine Intelligence is used to build statistical signals over batter baseball statistics.
The key difference over the rules engine approach is that a probability is returned; allowing a decision to be executed using probabilistic decision threshold.

What-If Analysis - Multiple Models This scenario showcases how multiple Machine Intelligence models can be used to predict Hall Of Fame BallotPresence & Hall Of Fame Induction. Machine Intelligence is used to build statistical signals over batter baseball statistics.
The multiple models implementation showcases breaking down the to Hall of Fame Induction into a process. 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, by providing multiple supporting conclusions provided by Machine Intelligence (experts).

What-If Analysis - Agentic Analysis This scenario uses multiple AI agents to assess Hall of Fame Ballot Presence and Induction using player statistics, machine learning predictions, and retrieved professional commentary. Quantitative evidence comes from the selected player’s batting statistics and probabilities calculated by multiple machine learning models. Agent Q combines the agents’ assessments into probability estimates and sensitivity ranges.