Force Energy: Growing the Brand

Kae Jonishi, the protagonist in the case, is a marketing analyst at Force Energy (Force). Her company has advertised with Major League Baseball (MLB) teams in the past and has gained moderate amounts of exposure from these marketing campaigns. However, Jonishi’s manager wants to increase the company’s exposure to MLB fans. One way to do so is to sponsor teams that are likely to advance into the MLB post-season, or playoffs. The top performing teams during the MLB regular season advance into the playoffs and increase their fan base, which results in greater marketing exposure for advertisers.<br><br>Jonishi has access to a data set that contains aggregate team statistics for offence and pitching performance during all MLB seasons from 1995 to 2019. With adequate knowledge of both baseball and data science, Jonishi begins by analyzing and visualizing the data. She then builds a machine learning model that can predict whether a team will make the playoffs. Using this model, Jonishi can input future offence and defence performance predictions to determine if the team would have advanced to the playoffs in previous years.

This case is suitable for introductory data science courses or modules at the undergraduate or graduate level. After completion of this case, students will be able to accomplish the following objectives:<br><br><ul><li>Learn how to load and clean up data to perform a preliminary analysis.</li><li>Learn how to analyze and visualize data.</li><li>Using the R or Python programming languages, become familiar with building machine learning models such as decision trees, random forests, logistic regression, and support vector machines.</li><li>Learn how to cluster data and resolve implications related to using clusters.</li></ul>

• visualization
• machine learning
• data anayltics
• random forest