The baseball game has been a source of entertainment for several years. It dates back to the 18th century or earlier in England. The game has introduced a culture, which is the relationship the fans had with the competition. However, doubts have been voiced about mispricing in the baseball labor market. The pricing anomaly had been prevalent for a number of years mainly before 2003. The issue was introduced into the limelight by Michael Lewis in the 2003 book named Moneyball.

The book analyzed the period when the anomalies persisted and the solutions to them. Nevertheless, there is a reason to believe they also existed during the Moneyball period and after the publication of the book. Consequently, there has been a positive impact on some teams who implemented his concept. Economically, the wages and productivity are positively correlated especially in competitive markets. This was achieved by introducing the pricing theory, whereby the personnel is compensated according to their contribution. He questioned whether that pricing concept was being implemented. The author’s argument generated discussion from both the baseball association and the economists. The concept was very essential, which lead to the Oakland Athletics hiring a team to investigate the claims made by Lewis. In reference to the concept, in 2003, the wages paid to the hitters were not positively correlated with their particular effort to enhance the status of the team. Despite the resistance to the idea of the baseball fraternity, numerous alterations, based on the suggestions of Lewis, were made.

Recently, age economists, baseball management groups, and scholars have explored the concept of use of relevant economic and statistical tools. Teams have invested in the research to investigate the aspect of mispricing in the baseball labor market. With the help of economic and statistic analysis, alterations have been made for the improvement of the pricing deviation. The research has uncovered information that was previously interpreted in the absence of accurate facts. Therefore, concepts have been revised in relation to the players, such as measurement of the batting skills and the factors that lead to a win. The main characteristic of the concept is the batting average. It is the ratio of hits to the total at-bats. Relatively, the slugging percentage is also considered. Slugging percentage is the total bases divided by at bats. However, the two rates are not inclusive of all the activities that are considered independent variables in a baseball game.

Independent variables cover activities like sacrifices and walks, which should be integrated as at-bats. One of the most vital components of the batting skills is elimination of the possibility of making an out. Therefore, not integrating the walks creates a relevant deviation from the accurate data. In contrast, the on-base percentage accommodates the walks. On-base percentage is the fragment of plate actualization where the player gets to the base through a hit or a walk. However, the baseball fraternity has made efforts to incorporate both the slugging percentage and the on-base percentage to forge a single statistical tool. This statistical tool is aimed at protecting the positive impact of two percentages. With regard to Money ball concept, the capacity to get to the base was not incorporated in the baseball labor market.

On-base percentage has been estimated to be a strong indicator of the batter’s contribution to the game won by a particular team. There has been a high correlation between runs achieved and the linear equation of the on-base and slugging percentage. As a result of statistical analysis, it has been investigated that on-base percentages contribute more to winning a game than the slugging percentage. Accordingly, in constructing the linear relationship between the two percentages, more relevance is given to the on-base percentage.

In reference to the team sheet, the index is constructed by multiplying the on-base percentage by 2. Its theoretical explanation seeks to explain that the on-base percentage affects the game twice as much as the slugging percentage. However, it is important to note that the twice concept is an assumption based on the idea that the on-base percentage affects the score of the game more in comparison to the slugging percentage. The index is constructed by adding the slugging percentage and the on-base percentage, which is multiplied by two. The index is then multiplied by 100 for easier analysis. By summing up the on-base percentage and slugging percentage, it introduces a tool that incorporates the two percentages.

The teams’ revenue is affected mostly by the attendance at the games during a particular session and the ticket price. In the team sheet, the total ticket revenue column is the product of the total attendance in the regular session and the average ticket price. It is relevant to note that the data is of the regular session. In Chart 1, it outlines the relationship between the total ticket revenue and the index; 100*[2*On-base Percentage + Slugging percentage]. From the linear equation, y = 1017353.76x – 358070101.32, it is notable that the total ticket revenue is affected by the index. It is the relationship of the on-base and slugging percentage combined. The slope is negative as shown by the (–) sign in the equation. A 1 unit increase in the x variable, the on-base and slugging percentage, leads to a 35807010.32 units decrease in the total percentage revenue, with all the other factors held constant. However, it should be noted that the outlier was deleted so that can be an explanation for the negative correlation.

In Chart 2, the information presents the relationship between the winning percentages and the index. The linear equation applied in this linear relationship is y = 0067x – 0.1761. The index is the independent variable while the winning percentage is the dependent variable. According to the equation, a 1 unit change in the on-base and slugging percentage results in a 0.1761unit decrease in the winning percentage. This concludes that winning percentage is affected negatively by the on-base and slugging percentage.

In Chart 3, it displays the relationship between the total ticket revenue and the winning percentage. Their linear relationship is explained by the equation y = -56710554x + 99262157. Whereby, the winning percentage is the independent variable and the total ticket revenue is the dependent variable. A 1 unit change in the winning percentage leads to 99262157 increases in the total ticket revenue. The two variables are positively correlated. The equation supports the idea that an increase in winning percentages leads to an increase in the total ticket revenue. Nonetheless, it is vital to note that tickets are not the sole source of revenue for the teams. Other sources include refreshments, accessories, packing, and programs.

Analyzing the baseball players’ wages, it is vitally important to investigate the labor market. From 2000 to 2004, the average wage for non-pitchers varied from $2.56million to $3.32million. The wages for the home run hitters were from $3million to $4million averagely. So, the hitters earned the highest salaries and got more income. This situation can be explained by the concentration on hitting by the baseball fraternity. Acknowledgment of the on-base percentage and slugging percentage is the proper compensation for the baseball labor market. This is attainable by encouraging the relatively younger players to negotiate for a higher wage.

The Lewis’ claim critics have also analyzed the situation. For example, they tried to support the argument why the on-base percentage has been omitted. First, there has been a possibility that the game’s audience prefer the slugging, which has the big impact on the game. The baseball fraternity seeks to invest in the sluggers. Second, there has not been any tangible evidence to prove that Oakland A’s was as a result of accurate pricing. It is evident that the team’s management invested in players with a high on-base percentage and were successful in the walks. This explanation was in relation to the argument that excellent hitters increased the walks, projecting the on-base percentage and emphasizing the hitters. The ream invested in hitters who were not experienced in slugging and cut down on the salaries in such a way. It was an economic concept of minimizing costs and maximizing the profits. The idea of the on-base percentage was also implemented in coaching. For instance, the coaches taught the tact of disciplined hitting rather than bad pitches.

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The owners of the team believed that walks in contrast to homeruns would lead to winning a game. During this period, the movement of players was rampant from one team to another. However, the concept of the on-base percentage ensured the team would maximize their profits without necessarily ‘buying’ expensive players. They implemented the idea by investing in the on-base players. They paid them lower salary, yet increased the team’s productivity. Most members of the league were interested in cutting costs and increasing the wins. The low costs concentrated on the on-base percentage and improvement was noted from 1999. As for the Oakland Athletics, the revenues increased and the attendance escalated. Consequently, the average ticket price increased, affecting the revenue base. Their game performance also improved, leading to an increased demand among the fans. After the success of Oakland Athletics, other teams were also interested in re-evaluating their personnel. It was effective only by minimizing the costs. The teams introduced programs whose aim was to duplicate the process implemented by the Oakland A’s.

The impact of the on-base percentage and slugging percentage is positive. It is important to note that the on-base percentage was undervalued in the baseball labor market. For statistical purposes, it is also worth noting that the players’ wages differ in relation to the season. This heterogeneity should be incorporated while carrying out the research. In conclusion, use of statistical data in baseball decisions is fundamental for the games.