Decision Analysis and Trees in Python — The Case of the Oakland A’s

Just recently, the owner of the Oakland Athletics baseball team, John Fischer, announced that the team had purchased close to 50 acres of land in Las Vegas, Nevada. [1] This puts the future of Oakland’s last remaining professional sports team in jeopardy. In the last 5 years, Oakland has seen the Golden State Warriors (NBA) and Las Vegas Raiders (NFL) depart for newer shinier stadiums in other cities (although, Golden State just went across the Bay Bridge to San Francsico). While the decision-making process in the Oakland A’s front office remains a mystery to me, data science and decision analysis in tandem can reveal a great deal about John Fischer’s motives to move to Las Vegas. Decision analysis is important for all data scientists to understand because it is the bridge between the highly technical work of probability and statistical models and business decisions. Understanding how business decisions are made can help frame our work and the presentation of our findings to non-technical audiences as we provide actionable recommendations and findings. The Institute for Operations Research and Management Science (INFORMS) even has an entire society dedicated to Decision Analysis. Additionally, machine learning can help generalize the results of decision analysis by unlocking insight from probabilistic sensitivity analyses. After initially constructing a model dissecting the Oakland vs. Las Vegas scenarios using decision analysis, we will use machine learning to mine for patterns that could help reveal actionable recommendations for the A’s should the circumstances of the decision change. <a href="https://towardsdatascience.com/decision-analysis-and-trees-in-python-the-case-of-the-oakland-as-786d746cdfb2">Click Here</a>