Predictive data analytics for contract renewals: a decision support tool for managerial decision-making

Purpose Predictive analytics and artificial intelligence are perceived as significant drivers to improve organizational performance and managerial decision-making. Hiring employees and contract renewals are instances of managerial decision-making problems that can incur high financial costs and long-term impacts on organizational performance. The primary goal of this study is to identify the Major League Baseball (MLB) free agents who are likely to receive a contract. Design/methodology/approach This study used the design science research paradigm and the cognitive analytics management (CAM) theory to develop the research framework. A dataset on MLB's free agents between 2013 and 2017 was collected. A decision support tool was built using artificial neural networks. Findings There are clear links between a player's statistical performance and the decision of the player to sign a new offered contract. Age, Wins above Replacement and the team on which a player last played are the most significant factors in determining if a player signs a new contract. Originality/value This paper applied analytical modeling to personnel decision-making using the design science paradigm and guided by CAM as the kernel theory. The study employed machine learning techniques, producing a model that predicts the probability of free agents signing a new contract. Also, a web-based tool was developed to help decision-makers in baseball front offices so they can determine which available free agents to offer contracts.

Automated summary

Predictive analytics and artificial intelligence are crucial for improving organizational performance and managerial decision-making. This study focused on identifying MLB free agents likely to receive a contract, using a design science research paradigm and CAM theory to develop a framework. The research found that a player's statistical performance and factors like age, Wins above Replacement, and last team played for are significant in predicting contract signings.