A system evaluation of NBA rookie contract execution efficiency with stacked Autoencoder and hybrid DEA

Most labor contract evaluations rely on performance evaluations by human resource management, which is time-consuming and costly. However, there has been little research into quantitative contract evaluations. This paper embedded a Stacked Autoencoder into a weighted two-stage data envelopment analysis model to evaluate NBA rookie seasonal contracts in an attempt to quantitatively assess contract execution efficiency. It was found that the model was able to effectively evaluate the NBA rookie contracts and provide guidance to the coach regarding their on-court performances. The NBA rookie contract execution analyses also found that performance and therefore contract fulfilment was possibly affected by time allocation problems. Finally, a dynamic and comprehensive contract evaluation system that has significant possible commercial value was constructed to assist the player, coach and manager make timely decisions, which may be a breakthrough in objective human resource management performance evaluation systems.