應用混合多準則決策方法建構中華職棒大聯盟球員薪資預測模式之研究

職業棒球是觀賞式運動之一，須能吸引觀眾看球，以達成球隊永續經營的目標。球員是職棒球隊人力資源管理最重要的一環，如何激勵球員認真表現，是職棒球隊重要的課題。根據期望理論，球員預期努力可以有效地獲得報酬時，就會有努力的動機，而薪資是眾多報酬中最受到球員重視的議題之一。為能了解球員績效對球員薪資之關係，本研究提出四種職棒球員薪資預測模型。本研究以2018年於中華職棒大聯盟登錄至少3年的球員為研究對象，首先利用熵分析法與灰關聯分析法求取權重，搭配理想類似度偏好順序評估法與灰關聯分析法加以排序的四種模型，計算分析中華職棒大聯盟球員2014年至2018年績效值。利用2014年至2017年球員績效透過灰預測分析法預測2018年績效值，並透過McNemar test檢定研究中所提四種模型之適切性。本研究結果發現﹕(1).影響球員薪資技術準則權重會因為職務不同產生差異；(2).中華職棒大聯盟所屬球隊調整球員薪資時，並無有效的預測機制可參考；(3).熵灰關聯與灰關聯兩種模型所產生之績效值，能較精確地預測職棒球員新年度之績效值。本論文研究結果可提供中華職棒大聯盟各球隊訂定球員薪資的參考。

關鍵字

熵分析法；理想類似度偏好順序評估法；灰關聯分析法；灰預測分析法

並列摘要

Professional baseball is one of the sports for watching and appreciation. It should attract viewers in order to achieve the goal of sustainability. Players are the most important part in terms of human resources management. As a result, how to encourage players to do their best to perform is a vital issue for professional baseball teams. Based on the expectation theory, when people can expect the reward via the efforts they have made, their motivation for hard working will enhance. Of the various rewards, salary is the most important concern for the players. In order to realize the relationship between players’ performance effect and their salary, this study proposed four types of salary-predicting models for professional baseball players. The subjects of the study were the players who had registered in Chinese Professional Baseball League (hereafter, CPBL) for at least three years based on the year of 2018. First of all, the weight was acquired via Entropy and Grey Relational Analysis. Then TOPSIS and Grey Relational Analysis were employed to derive the four models which were sequenced. These four models were applied to conduct the analysis of the CPBL players’ performance value from 2014 to 2018, through the use of Grey Prediction on players’ performance effect produced from 2014 to 2017 to predict players’ performance value in 2018. Additionally, the McNemar test was used to examine the appropriateness of the four models proposed by this study. The findings of the study are: (1) The weight influencing players’ salary varies from players’ positions; (2) There is no effective prediction method to base on for the teams in CPBL when adjusting players’ salary; (3) The performance value gained by Entropy and Grey Relational Analysis can better predict players’ performance value for the coming year. The results of the study can be used for reference by each team in CPBL when deciding their players’ salary.