Fuzzy Financial Analysis

This project investigates how salary and performance of offensive players in Major League Baseball are linked. We believe this is an interesting problem because it is traditionally believed that professional athletes play with hopes of earning a high salary, yet it often seems a batter’s performance is not linked to their salary (Jensen). Therefore, it seems as if the link between a player’s performance and their salary is different than their true performance. Performing a statistical analysis of this conundrum will give us great insight as to if it is accurate to say that performance changes salary drastically. Studies that prior statisticians have done differ from this study because their studies focus on salary and team performance rather than on the performance of individual players (Jane). Our study focuses on salary and individual performance in the current season. While there is extensive data on both game performances in the MLB and salaries, we can contribute to the statistical community by comparing how salaries are affected by different performance indicators for randomly selected individual players. Essentially, our hypothesis is an examination into how a batter 's game performance affects salary. We expect that the better a player’s statistics are, the higher their salary will be.

Baseball has always been a game of numbers. Fans of the game have grown up being able to recite them by heart; Ted Williams’.406 batting average, Joe DiMaggio’s 56 game hitting streak, Babe Ruth’s 714 home runs. These numbers hold a special place in the history of the game. Statistics such as batting average, wins, home runs, and runs batted in have always been there to tell us who the best players are. Your favorite player has a .300 batting average? He’s an all-star. He hit 40 home runs and batted in 120 runs? That’s a Most Valuable Player Award candidate. Your favorite team’s best pitcher won 20 games? He’s a Cy Young Award contender. These statistics have been used to evaluate player performance

MLB teams are finding new innovative ways to use analytics, one way is to use the statistics for a more effective way to evaluate free agents. “Astros employed an analysis based on the TrackMan system to acquire an unaccomplished pitcher called Collin McHugh, because of his fast-spinning curveball” (“Every Step They”). “They then told him to throw that pitch far more often during the next season, and he blossomed into a star” (“Every Step They”). General Managers and coaches can use these analytics to scout areas they have never scouted before. Analytics gives teams more resources and more assistance to evaluate players. “For example, by comparing the number of strikes called for a catcher in relation to every other catcher in the league, the data can illustrate how good any one catcher is at getting umpires to call strikes” (Nadler). “Additionally, general managers and scouts can use

For example, the authors write that multiple teams beat there preseason predictions by “double digits”. The use of these numbers and logic in the blog shows that the purpose of the blog was the authors attempt to convince the reader through statistics to convince that a growing randomness in teams’ performance in the MLB is occurring due to the increase in skilled young players. Since the preseason predictions were way off, the authors show just how off the predictions were for all the teams in the league. Also, the advanced formulas used in the blog also boost the authors’ ethos in that their complexity means that the authors have a high understanding of both math and baseball. This terminology also helps their ethos in that the reader is going to trust what they are saying. The article also includes links to other blogs and articles about the projections further establishing their ethos. The audience for the blog is people that pay attention and follow professional baseball as to anybody else the numbers and facts would not mean anything to them. The blog is successful in using logos and ethos as rhetorical appeals and using the conventions of the blog genre to prove to audience that the preseason predictions have become progressively unreliable in a way that the audience finds easy to understand and interesting to

In baseball a common factor for determining the quality of a player is batting average. (# of hits/# of bats) This statistic helps to show a player's ability to successfully make contact with the ball, which leads to RBI’s (runs batted

In sports stats, you can’t decide the best players on a team by how they played in a shorter time |

Price is defined as “The value that will purchase a finite quality, weight or other measure of a good or service” (Business Dictonary). When growing up your parents always said, this is too much money so you wouldn’t be able to get that candy bar or video game because the price of the product was too high. Whether this be because of high price the person that made this product had to out some research into the idea of how much they should sell this product for, how much profitability am I making at the end of the day after all deductions are taken out. The price is what set’s your product apart but a high price mean’s that you need to market the product very well to get people to buy it and build a quality product to get raving reviews. At Starbuck’s they always advertise giving you incentives and low prices. Summer time they do Ice Blended hour, which from 3 pm to 5 pm they offer their ice blended

Batting average was the norm adopted by other baseball teams. But training for Oakland was focused on the player’s ability to obtain on-base scoring. The team relied more on selecting players by their on-base percentages. According to Sabermetrics model, teams always win with players having attained high on-base percentages.

In the former 'Runs Created' model of judging the value of the player, too much emphasis was placed upon batting averages and stolen bases versus the Sabermetrics approach which used an equation computing the value of a player based on walks, steals, and doubles. On-base percentage versus the traditional metric of a 'batting average' was used to determine hitter effectiveness. Sabermetrics also suggested that certain tried and true techniques such as sacrificial bunting were less effective than originally presumed. A great deal of the analysis supported the notion that succeeding in baseball was

Statistics, specifically how they measure and control data, as well as help us learn from it, has and always will be a part of baseball (“What is statistics”). Stats are very, very useful for many purposes in the sport, and can help keep some analysis simplistic. The most effective use of stats in baseball is the comparison between teammates within the same season. Within these parameters, the numbers come from very similar environments, as the teammates are playing against the same teams in the same ballparks at the same time. There are no outside factors potentially skewing the

My study proposes to examine the New York Times sports pages between 1997 and 2017 as a way of testing some ideas about the nature of the changes in the discourse about baseball as that discourse has evolved over the last 20 years. Although these ideas did not necessarily take hold in professional baseball circles until the 21st century, outsiders like Bill James have been promoting non-traditional baseball statistics as more accurate ways of describing the game since the 1970s, while in the 1990s Baseball Prospectus, a publication which debuted the PECOTA predictive baseball model developed by eventual data celebrity Nate Silver, began to spread these ideas to increasingly wide groups of baseball fans. Today, these ideas have widespread popularity, and the yearly Bill James Baseball Abstracts and Baseball Prospectus anthologies both have high circulations, while websites like FanGraphs, which approach baseball journalism from a statistical point of view, have significant daily readership (among them, yours truly).

Competition within the industry as well as market supply and demand conditions set the price of products sold.

Analytical data includes different psychological aspects of a player’s mentality. Stopwatches record times, tape measures calculate distances and heights, plus video gives scouts and coaches a visual look at player performance. However, for all their usefulness, they do little more than categorize athletes based on a series of numbers.

The success of a business depends on its ability to remain profitable over the long term, while being able to pay all its financial obligations and earning above average returns for its shareholders. This is made possible if the business is able to maximize on available opportunities and very efficiently and effectively use the resources it has to create maximum value for all involved stakeholders. One way the performance of a company can be measured on critical areas such as profitability, its ability to stay solvent, the amount of debt exposure and the effectiveness in resource utilization, is performing financial analysis where a set of ratios provides a snapshot of company performance

Price, which is one of the most important elements of the marketing mix, can be difficult to get right. Pricing too high, or low, can negatively impact on customer satisfaction and revenue. Adopting a pricing strategy is necessary to achieve desired sales objectives (Chan & Wong 2005).