Purpose Statement
This study aims at showing the relationship between the performance and salary of a player in the Japanese Baseball League. Furthermore, it will try and predict the salaries of the players based on their daily field performance. In this, the dependent variable will be the salary earned by a player annually which is determined by independent variables such as Batting Average (BA), Stolen Base (SB), experience level of the player (ELP) and the on base slugging percentage (OSP). The on base slugging percentage is expected to be the most significant independent variable since it is a comprehensive variable that includes factors like slugging, time used in considerations, position, getting on base and being untangled on a mean of a hundred (Tanner, 2012, 17) . So, in order to indicate the performance of an individual player the on base slugging percentage (OSP) has to be included since it’s the key independent variable. Therefore, the player’s salary annually will be modelled as follows:
Where: P.A.P= Player’s annual Pay
B.A= Batting Average
OSP= the on base slugging percentage
SB= Stolen Base
ELP= Experience level of the Player
Definition of Variables
In this case the player’s annual pay is the dependent variable that is measured at the scale level of measurement. Therefore, it is important for the general player payroll data to be obtained so as to be able to accomplish the aims set by this study. According to study carried out by Lutze, Gani & Woodley which researched on the relationship between the productivity of a player and their contracts’ length, the on base slugging percentage seemed to be a powerful statistic that evaluated the performance or productivity of the baseball player (Lutze, Gani &Woodley, 2011). It added the two measures of productivity together, slugging percentage, and the on base percentage and is re known for its accurate measure of productivity(Tanner, 2012, 52). Bates, Maechler & Walker conducted a study looking at the mean annual salary for players in different positions (Bates, Maechler & Walker, 2016). The developed two models with one of them the average yearly salary using regression analysis on data collected from the plate appearances and on the base slugging percentage. This study concluded that the performance metrics based on the two variables used were influential in the prediction of a player’s salary (Urdan, 2015, 111). OSP is the primary independent variable since it has strong positive correlation with the performance and the salary of the player.
Additionally, salaries are also related to the zeal and willingness of the fans when it comes to paying for their entrance charges (Averbukh, Brown & Chase, 2015). Likewise, owners willingly pay for a player when they note that they are going to generate for them more revenue. These players are meant to generate capital and profits for their bosses just like any employee of a given company (Tanner, 2012, 61). This is why Japanese baseball coaches and managers trade young players for veterans with proven track records (Sæther & Solberg, 2015).This trade off eliminates long-term payroll flexibility and older and more experienced players come with their fans base who can support the teams in the long run (Urdan, 2015, 116). This is the main reason why club managers are demotivated in training new and young talents for the long run but are rather motivated to produce results for short term results.
Data Source and Collection
This research is based on the data collected from the Baseball reference forum database available for viewership and download at Baseball-reference.com. This database contains all the information needed such as the pitching, fielding and hitting statistics for all the major baseball leagues Japanese Baseball league included. Also contained in the database is the season by season information on players’ productivity and performance, salaries and several other variables that are useful in the structuring of the analysis done in this project. Furthermore, the site also hubs data from other leagues that can be used in analysis, historical information of the league and more useful data on the pacific league.
The database was created by Sean Forman, who has begun working on the website working on his PhD dissertation in applied mathematics at the University of IOWA. This website started its operations in April 2000, after its launch earlier on that year on February. What started as an educational expedition has now grown tremendously to one of the most sought out baseball information site world widely (Japanese NPB Stats and History, 2018). In addition to information collected from the baseball reference site other data sources will be used such as baseball-almanac.com so as to diversify the data used in the research.
After the extraction of the data, it will be later imported to SPSS for further analysis. In this analysis the research compares the data to get the F-ratio of the information provided. Similarly, the analysis looks at the degree of freedom of the data and the Post Hoc test will be conducted to tell more on the difference between the variables after finding the significant F.
Moreover, in this study, the following positions will be considered: Batters, pitchers and other fielding positions that hit including their hitting positions. However, the batter players were irrelevant for the research since fielding statistics were not used. The statistics that will be used for pitchers and batters include:
Earned Run Mean (ERM) – The average of earned runs that a pitcher gave up per nine pitched innings.
Wins- The number of games won by the pitchers.
Losses- The number of games lost by the pitchers
Strike Outs- the number of players that struck out by the pitchers
Hits- The total of number of home runs, singles, doubles and triples earned by a batter.
Home runs- the number of balls hit by the batter out of the field.
Batting Average- Number of hits over the number of bats.
Run Batted in- The players who have scored a run from a hit by the batter.
Additional factors that were considered included the number of years that a player has been playing in the Japanese Baseball league, the teams they have played for and their personal history in those teams.
Averbukh, M., Brown, S., & Chase, B. (2015). Baseball Pay and Performance.
Bates, D. M., Maechler, M., Bolker, B., & Walker, S. (2016). lme4: mixed-effects modelling with R. 2010. URL: http://lme4. R-forge. R-project. Org/book [8 April 2015].
 Japanese NPB Stats and History. (2018). Retrieved from https://www.baseball-reference.com/register/npb-stats.shtml
Lutze, P., Woodley, J., & Gani, R. (2011). An innovative synthesis methodology for process intensification. Technical University of Denmark
Sæther, S. A., & Solberg, H. A. (2015). Talent development in football: are young talents given time to blossom? Sport, Business and Management: An International Journal5(5), 493-506.
Tanner, D. (2012). Using statistics to make educational decisions (pp.1-1500. Los Angeles, LA [etc.]: SAGE.
Urdan, T. (2015). Statistics in plain English, (4th ed., pp. 15-269). [Place of publication not identified]: Routledge.