Salaries of professional players are usually determined prior to the execution of the responsibilities assigned by the organizations
and are often based on the expected future performance of these players as derived from their past achievement. The study first
identifies criteria that would affect players’ salaries through literature reviews and then utilizes grey relational analysis (GRA) and
grey prediction model to calculate weights of salary impact criteria, players’ annual performance index, and salary prediction for
the coming year. The performance data of players from the Chinese Professional Baseball League (CPBL) are used in this study.
The results are as follows: (i) CPBL teams do refer to players’ past performance records and future performance prediction when
deciding on their salaries and (ii) future performance prediction must be made using at least a 3-year data set. The proposed
prediction model is able to effectively provide relevant and useful information to the CPBL teams’ management during players’
salary adjustment.
