UNDERSTANDING LA LIGA: ARE MATCH PERFORMANCES AND PLAYER MARKET VALUE RELATED?

Football- one of the most popular sports worldwide, has emerged as a huge centre of business. Through this research study, I aim to understand the relationship between match performances of footballers and their respective values in the European Transfer Market. This paper explores this particular trend in the latest season of La Liga 2019/20 (Spanish Division one football). This study also focuses on understanding whether players of foreign nationalities have been understood to have a higher value compared to the home-grown players of the league. Correlational Matrix Plot for Spanish and Foreign players- Match Ratings v/s Market Value.


ISSN: 2320-5407
Int. J. Adv. Res. 9(01), 12-21 13 constitutes a significant variable which enhances a player's annual valuation. Whereas, (Dimitropoulos, 2016) studied the player acquisition trends of the 'Big 5' leagues-the impact of indigenous home-grown players, nonindigenous home-grown players and foreign players within the respective leagues.
Due to this certain commercialisation of the sport, there was an increased involvement of foreign strategic investors in countries like Germany, France, and England (Marc Rohde, 2017). Therefore, player recruitment had become a serious business. Constrained models were used to give a real-time analysis, adjusted to various budgets (Giovanni Pantuso, 2019). ANOVA, Regression analysis were utilized in other models to find the player's maximum economic value (Jose Luis Felipe, 2020

Type of Research Design
Since the data has been in a quantitative format, I have chosen to go ahead with a Causal-Comparative Research Design to study the desired. It is a type of a descriptive non-experimental research which is used to explain the reasons of existing differences between two or more data groups( nationality, match performance), which then is compared with an dependent variable (Market Value).
To meet the requirements of statistical assumptions, methods such as p-test, ANOVA, and correlation is undertaken.

Data: Sources:
The data has been collected and curated from these following public databases: Transfer Markt, WhoScored, and Garter.
The market data is collected from Transfer Markt. The site uses its own non-disclosed algorithm to estimate the market value of a particular player. The values used in the study are confirmed to be true post the end of the 19/20 season.
The performance statistics have been gathered from WhoScored. Additionally, WhoScored provides a performance rating for all the players, using OPTA's statistics which is updated during each game. The rating variable is scaled from 0-10.
Finally, the personal characteristics of each player (i.e. Nationality, Age, Position, etc.) has been accessed from Garter in a similar fashion.

Variables:
For the benefit of the study, the entire set of 250 players has been divided into three segments on the basis of their position-Goalkeepers (50), Attackers (105) and Defenders (105. Keeping that in mind, there are several types of variables utilized. These are: Independent Variables: 1. Player Name, Age-Utilized to mark each response for easy identification. 2. Essential Attributes (Minutes Played, Successful Passes/ 90 Minutes) -These attributes are essential for every footballer irrespective of their position. Hence, they have been marked for every footballer present in the dataset. 3. Positional Attributes-There are specific skill sets required for each position. For example, a keeper is judged on his ability to conduct saves, whereas an attacker has been viewed to keep his goal scoring high to be deemed good.
Flow Chart classifying the positional attributes utilised.

Interconnected Variables:
Clubs-Clubs play a smaller role in deciding the market value of a player. It acts as a small influencer. Players playing in bigger clubs tend to have more market value compared to the rest. To study this, all 20 clubs divided into four tiers with respect to their final league positions.

Number of Observations:-
The entire data distribution consists of 250 La Ligafootballers, divided into three separate sections with respect to their playing position.
The data in that respect consists of 40 goalkeepers, 105 attackers and defenders each.

Cross Sectional Study:
The entire research is based upon a cross-sectional study of 250 main players representing their teams over the period of one season. The playing statistics according to their position is noted down, and then correlation is conducted in order to understand the role match performance plays in boosting or downgrading one's value in the European Transfer Market.

Time Period :
The study considers the latest complete season of La Liga (Spanish Football League), i.e. 2019/20 season. This season covers just above a whole year, from 11 th June to 19 th July; with a total of 380 matches being played.

Normality Checking:
For a big set of data in this field of research, the null hypothesis is assumed as the set in not normal. Therefore, through Minitab I have been able to use the Anderson-Darling Test to prove that the sets taken are in a normal distribution. For the same, P-value needs to be more than 0.05.

Defenders Data:
Defenders' set of data for tackles and interceptions.

Goalkeeper Data:
Goalkeepers' set of data for saves and clearances.

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Attacker Data: Attackers' set of data for goals scored.

Transfer Market Value:
Entire Normal distribution of the transfer market values of players, with box plot below.
In conclusion, the Null Hypothesis disproved and we can go ahead with the data analysis.

Tools used for data analysis with justification:
Minitab has been utilized as a data analysis tool. I have personally found it to be user-friendly, and effective in nature. It also provides all the necessary operations to conduct graphs, statistical tests and complicated functions like Regression, ANOVA, control charts, etc.
For simple mathematical functions and collecting the data, Microsoft Excel has also been utilized. The sole reason being, the familiarity behind it.

Research Hypothesis :
Therefore, since null hypothesis has been disproved; we can go ahead with formulating a research hypothesis. A good match performance by the La Liga footballers positively influences their market value. The second hypothesis is that footballers from foreign countries tend to have a higher market value compared to the same of the Spanish players. H1: Match Performance directly affects the players' market value H2: Match Performances of Spanish footballers influence their market value more, when compared to the same for foreign players.