Abstract
For popular sports brands such as Nike, Adidas, and Puma, value often depends upon the performance of star athletes and the success of professional leagues. These leagues and players are watched closely by many around the world, and exposure to a brand may ultimately cause someone to buy a product. This can be explored statistically, and the interconnectedness of brands, athletes, and the sport of basketball are covered in this chapter. Specifically, data about the NBA and Google Ngrams data are explored in relation to the stock price of these various sports brands. This is done through both statistical analysis and machine learning models. Ultimately, it was concluded that these factors do influence the stock price of Nike, Adidas, and Puma. This conclusion is supported by the machine learning models where this diverse dataset was utilized to accurately predict the stock price of sports brands.
TopBackground
Literature Review
There have been numerous studies that have covered a similar topic area to that of this chapter in the past, whether it be exploring the value of a sports brand or even just the popularity of Michael Jordan. The following studies are not used a basis of this chapter, but rather a springboard for developing the overall topic. Studies are grouped by topic in the below paragraphs.
Effects of Players on Brands
The first study to be examined is a thesis paper written by Paul Andrew Maddock II. This paper covers the economic impact on a brand that an NBA player can have. Regressions were calculated by this author, and both negative and positive impacts on a brand’s value could be seen in his results (Maddock II, 2018). Another study focused on a similar topic area as the previous one. It focused on the negative impacts an athlete has on a brand. It even mentioned that this impact can transfer over to other brands too (Shintaro Sato, 2018). Superstar players can also have major effects on the success of their team financially. This study focused on the effects that a superstar player can have on the attendance of games (both home and away), and ultimately concluded that the effect is real (Humphreys, 2019).
Miscellaneous Sports Brand Studies
The next study focused on the impacts of a brand’s involvement in grassroots basketball. It further explores why these brands are involved in grassroots sports and even the benefits that they reap from it (Keefe, 2011). Brand loyalty is another factor that ultimately decides the value of a brand. This study explored this through firsthand data collection, and ultimately determined that being loyal to one brand will affect that person’s decision to buy a pair of basketball shoes (de Silva, Madhushani, & Jayalath, 2020). Business strategy and models can also affect the value of a brand. This paper explores just that and goes into detail with Nike and Adidas (Ali Mahdi, Abbas, Mazar, & George, 2015).
Michael Jordan and The Effects of Star Athletes
Another paper explores Michael Jordan’s decision to return to the NBA from minor league baseball. It explores the effects that decisions like this have on brands that are not directly involved which is significant (Lynette Knowles Mathur, 1997). The final paper to be covered explores what they coin as “The Jordan Effect”. This revolves around Michael Jordan’s effects on brands just by what he does in life. Any decision he makes can spill over to a brand, and this is something to keep in mind when determining value (Johnson, 1998). Another study focused on the rise and fall of prolific players. They ran various models on this topic and did so for multiple sports as well. While this paper did not cover sports brands directly, the rise and fall of a player can have effects on a brand as evidenced by previous papers referenced in this section (Yupin Yang, 2011).
Key Terms in this Chapter
Scikit Learn: Open-source Python library that offers predictive models and analysis metrics.
National Basketball Association (NBA): The premier professional basketball league in not only the United States, but also in the world.
Michael Jordan: Michael Jordan is argued by many to be the greatest basketball player of all time and he carries a heavy influence on anything that he is involved in.
Total Brand Value: Total brand value references a calculation that is as follows: å (Closing Stock Price * Volume of shares). All individual brand values are summed to reach this number.
Correlation: A connection or relationship that is seen between two numbers.
Predictive Modeling: A mathematical process that seeks to predict future events or outcomes by analyzing patterns that are likely to forecast future results (Carew, n.d.).
Regression: A statistical method used in finance, investing, and other disciplines that attempts to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables (known as independent variables) (Beers, 2021).
Google Ngrams: Tool offered by Google that contains frequency data of a term in literature.
Stock Price: The value of a company on the stock market at a specific point in time.
Multi-Layer Perception (MLP): MLP is a supervised machine learning model that can be compared to a logistic regression. One of the main differences is that the MLPClassifier has a non-linear hidden layer between the input and output layers. The attributes of this model make it good for both classification and regression problems. (Neural network models (supervised), n.d.). See Figure 7 for a conceptual example.