Omorodion's Data Analysis of FC Porto's Goal Scoring Performance
# Omorodion's Data Analysis of FC Porto's Goal Scoring Performance
## Introduction
The goal scoring performance is one of the most critical metrics in football analysis. It directly reflects a team's ability to control possession and create chances for their opponents. In this article, we will analyze FC Porto's goal-scoring performance using data analysis techniques.
## Dataset Overview
FC Porto has been the reigning champions of Portuguese football since 2014. The club has a strong tradition of winning domestic competitions but struggles with maintaining high goalscoring success across all competitions. This dataset includes data on every match played by FC Porto between 2015 and 2022.
## Data Preprocessing
To begin our analysis, we need to preprocess the dataset. We will handle missing values, encode categorical variables, and normalize numerical features. Here’s how we can do it:
### Missing Values Handling
We will fill missing values with the mean or median of the respective column based on the frequency of occurrence.
### Categorical Variables Encoding
For categorical variables like player positions (forward, defender), teams, and seasons, we will use one-hot encoding to convert them into binary columns indicating presence or absence of each category.
### Normalization
Numerical features like goals scored per game and minutes played will be normalized so that they fall within a reasonable range.
## Feature Engineering
Next, we will engineer new features that could help us understand goalscoring better. For instance, we might look at the number of shots taken by each goalkeeper during matches. If a goalkeeper saves many shots, it suggests they have good vision and may not be easily cornered.
## Model Selection
To predict goals scored by FC Porto, we will consider several models: linear regression, decision trees, random forests, and gradient boosting machines. Each model has its strengths and weaknesses, so we will evaluate different combinations to find the best fit.
## Model Evaluation
After selecting the optimal model, we will perform evaluation metrics such as accuracy, precision, recall, and F1 score to assess the model's performance.
## Conclusion
In conclusion, FC Porto's goal scoring performance has shown some signs of improvement over time, especially in recent years. However, there are still areas where the team needs to improve. By analyzing the data, identifying key factors influencing goalscoring, and employing appropriate statistical methods, we can gain valuable insights into why these performances vary.
This analysis provides a foundation for future discussions about improving FC Porto's goal-scoring strategy.