Wager Mage
Wager Mage
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Data Acquisition with Web Scraping in Python The web scraping script (with Chrome WebDriver) was applied on the FIFA Men's Ranking page to retrieve national team points and ranks between 1992 and 2021.
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Read More »Data Acquisition with Web Scraping in Python Python, being a general-purpose programming language, is frequently used for general-purpose tasks such as web scraping. One of the most popular tools for such data scraping jobs is Selenium. Selenium is an open-source automation tool for web browser control and is used to automate repetitive actions such as button clicks, form filling, text extraction, and page scrolls. The web scraping script (with Chrome WebDriver) was applied on the FIFA Men’s Ranking page to retrieve national team points and ranks between 1992 and 2021. The extracted data was exported as a CSV file for data analysis. Screenshot of first five rows of extracted FIFA rankings data | Image by author Data Visualization with Bar Chart Race in R Similar to Python, R is an open-source programming language that serves a myriad of functions. However, it is commonly used for statistical analysis and creating graphics. The latter is a significant strength of R, as it can generate top-quality graphs which are highly customizable. In this segment, we demonstrate the use of several R packages to create a dynamic visualization. FIFA World Rankings Bar Chart Race | Image by author The figure above is a bar chart race animation, and it shows how the top 10 FIFA rankings have evolved over the last three decades. The countries were ranked by aggregating their yearly average points. The data visualization was generated in two steps. First, a static bar plot was created using the popular ggplot2 package (loaded within tidyverse), which allows a high degree of customization by breaking the graph into different components. Many packages are now available to complement ggplot2 plots. In this case, we customized the plot by adding flag images using the ggimage package. Second, the static plot was animated using gganimate. Specifically, the transition_time function controls the bar chart plot to change year by year with just an additional line of code! Regression Analyses with Python and R Both Python and R are highly effective tools for statistical analysis. To demonstrate this, we showcase regression modeling in Python and R to answer specific football questions. (i) Relationship between FIFA rankings and monetary values of national teams (Linear Regression in Python) We wanted to determine how a national team’s FIFA points are affected by the average monetary value of the players in the squad. The data for the squad lineups (based on recent tournaments like UEFA Euro 2020, Copa America 2021, etc.) and corresponding player market values (in British pounds) were obtained from the public football site Transfermarkt.co.uk (accurate as of 12 Aug 2021). We calculated the monetary value of each national team by summing the market value of every player. Because every squad may have a different player count, the average monetary value (i.e. total value divided by team size) was used for analysis. Before modeling, it is a good practice to visualize the data. In particular, we want to ensure that our independent variable (average monetary value) follows a normal distribution, an assumption that needs to be satisfied for linear regression. With the seaborn library, we can create a histogram (with KDE) to show the distribution of the average monetary value. Histogram with KDE | Image by author The plot reveals a significant right skew since several teams have monetary values much higher than the rest. The solution is to apply log transformation to the average monetary value. Upon doing so, we get a plot that is much closer to normal distribution. Histogram with KDE (after log transformation) | Image by author With our independent variable transformed successfully, we can proceed to regression analysis. The most intuitive baseline model to start with is linear regression. Numerous Python packages allow us to perform linear regression, and one of the most popular is the linear_model module of scikit-learn. After providing the LinearRegression() function with the dependent and independent variables, we obtained the following metrics: Linear regression metrics | Image by author The R² (coefficient of determination) score of 0.746 means that the model accounts for 74.6% of the variability in FIFA points. This is a pretty decent result, given that our model has only one independent variable! It implies that the average monetary value of the national team has a relatively strong positive correlation with its FIFA points. Lastly, a regression plot was created with seaborn to visualize the linear regression model fit: Regression plot in Python | Image by author (ii) Relationship between FIFA rankings and monetary values of national leagues (Linear Regression in R) We were also interested in the relationship between FIFA points and the monetary value of countries’ tier 1 football leagues. Data for the leagues’ monetary value was similarly taken from Transfermarkt.co.uk. Given that the number of participating clubs differs across leagues, the monetary value of national leagues was normalized as the value per club in British pounds. Similar to the earlier regression analysis, the distribution of national league monetary values is right-skewed, and thus a log transformation was applied. As R is commonly utilized for statistical analysis, linear modeling is already included in the basic packages, and no additional libraries need to be imported. The lm function fits the linear model to the data by simply specifying the formula in the format Y ~ X. The results revealed that the R² was 0.47, indicating that the monetary value of national leagues accounts for less than half the variability in countries’ FIFA points. This relationship can be visualized in the scatterplot below: Regression plot in R | Image by author We observe that the data points are further away from the linear regression line than the Python scatterplot on national teams’ monetary values. Collectively, we can interpret from both analyses that the national team’s monetary value better predicts a country’s FIFA points than the value of their national leagues. Photo by Chaos Soccer Gear on Unsplash
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Read More »Summary Both Python and R are excellent programming languages for data analysis, and each has its rightful place in the data science toolkit. If you are focused on statistical modeling or generating graph visualizations from your data, R is a more straightforward option. On the other hand, Python is a more suitable choice if your project entails general-purpose programming tasks or integration with other applications. In conclusion, it is futile to debate whether Python or R is the better language. The ideal tool depends on the use case, the questions you intend to answer, and the culture of your team and industry.
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