Latest Projects
Hierarchical Clustering for TOP 5 Leagues Using xG and xGA Metrics
This project aims to categorize teams across the major five leagues by analyzing their Expected Goals (xG) versus Expected Goals Against (xGA) metrics. The goal is to identify similarities and connections between teams, offering insights into their statistical tendencies. By clustering teams, the project seeks to anticipate the expected statistical performance when two teams from different or similar clusters face off. Subsequently, the data is visualized using Tableau for better comprehension and analysis.
Cloud-Enabled Insights: Transforming Match Data into Actionable Reports
This project collected football event data from Borussia Dortmund vs Stuttgart match in the Bundesliga using Python's BeautifulSoup library to scrape semi-structured JSON-formatted data from whoscored.com. The data was cleaned and transformed for accuracy. A cloud database instance was created using SQL and Python, integrating the structured data into Supabase. This setup enabled the generation of detailed game reports, providing stakeholders with valuable insights and statistics from the dataset.
Unveiling Guardiola's FC Barcelona Passing Network Dynamics
Analyzed football event data from the Statsbombpy API, focusing on the UEFA Champions League Final 2011 match between FC Barcelona and Manchester United. Using mlpsoccer and matplotlib libraries, the project visualized FC Barcelona's passing network, creating a dynamic graph displaying player associations and their strengths through varying line thickness. This visualization, rooted in Graph Theory and Network Science, unveiled player connections and strategic dynamics in football.
Predictive xG Betting Model
By harnessing web scraping techniques, this project collected detailed xG data from understat.com. Leveraging the power of the scipy library and the Poisson distribution, a sophisticated predictive model was crafted. This model not only estimates the likelihood of each game in the TOP 6 leagues having over 2.5 goals but also provides comprehensive predictions for various match outcomes. The project's scope encompasses API extraction and automation, ensuring a robust and comprehensive approach to data retrieval and predictive analysis in sports betting scenarios.
Automated Pre-Game Reports using Understat and Fotmob Data
This project automated the generation of PDF pre-game reports for upcoming matchweeks across the top 5 leagues. Utilizing stats scraped from understat.com and fotmob.com, the reports encompass advanced Expected Goals (xG) data, xG Difference, and xGA Difference. The system rapidly creates reports for all matches within a matchday, showcasing high scalability. These reports serve as inputs for intelligent pre-game analysis and betting strategies, offering quick insights into upcoming games.
Prospect Prodigies: Identifying Future CDM for FC Barcelona
This project creates detailed scouting cards for four potential replacements for Sergio Busquets in FC Barcelona's critical Central Defensive Midfielder (CDM) position. It combines statistical analysis and precise data cleaning to produce comprehensive PDF reports. The process involves using statistical methodologies to assess players' CDM suitability and advanced web scraping from FBRef.com for football-specific data. These enriched scouting reports offer vital insights for informed decision-making in player scouting and recruitment strategies.
Past Projects
Rolling xG Plot: Analyzing FC Barcelona Performance
This project uses Python, Pandas, and Matplotlib to create a rolling plot of FC Barcelona's expected goals (xG) based on data from the 2021-2022 and 2022-2023 seasons, with the last game being Barcelona vs Cadiz at the Camp Nou. The plot includes a line that separates Xavi's numbers from Koeman's and showcases the team's xG performance over time.
The xG/xGA Model: Predicting Over 2.5 Goals
This Model employs xG and xGA data, along with a Poisson distribution, to generate predictions on the likelihood of upcoming La Liga soccer matches having over 2.5 goals. The use of advanced statistical techniques allows for a more accurate assessment of goal-scoring potential, providing valuable insights for sports bettors and fans alike.
Movie Recommendation System
The user inputs a movie, and the program utilizes natural language toolkit and feature extraction to analyze patterns in the data. The program performs extensive data cleaning to ensure accurate recommendations. As a result, the program provides 5 movie recommendations that are similar in nature to the user's input movie.
Automated Inventory Management System
Data pipeline that facilitates the tracking of inventory and updates it based on incoming data. The system leverages several software tools and technologies to perform these tasks, including Python, Pandas, and xlwings libraries, and a macro-enabled Excel workbook. The system can save time and reduce errors associated with manual data entry.
Automated Inventory Analysis Report
Program developed to automate the process of identifying products that are low in quantity. The program analyzes data from the inventory sheet, and based on pre-defined numerical parameters, outputs a report in Word format listing the products that fall below the specified thresholds. The program uses Pandas and the python-docx libraries.
Netflix Data Analysis Project
The account data was requested from Netflix.com and a review was conducted on the trends and favorite movies/TV shows for each user. Prior to analyzing the data, extensive cleaning and manipulation were performed to ensure its accuracy and relevance, where users were defined by location or device type. Through this process, valuable insights were gained into the preferences of Netflix users and trends.
Car Dealership SQL Sales Data Exploration
This project aimed to leverage data querying and exploration techniques in SQL to uncover key insights from auto sales dealership data. The primary goal was to identify the most efficient salesperson within the dealership. To achieve this objective, a comprehensive analysis of the available data was conducted, focusing on various performance metrics.
Commissions, Brand Bonus & Production Bonus Calculation System
Developed a Python-based automated system that calculates commissions, brand bonuses, and production bonuses for a Peruvian logistis & transportation company. The system uses input and parameters to provide accurate and efficient calculations, resulting in cost savings and increased productivity.
Tableau Dashboards
Through Tableau dashboards, we can gain a holistic view of various datasets, including COVID-19 project data from around the world and Latin America, sales data from stores, Netflix account data, industry consumer data (such as movie preferences and pet ownership), and more. Additionally, we can also incorporate football data, such as the World Cup and La Liga matches, to gain valuable insights into the sport.
S. Peña Statistical Report
Delve into the analysis of Sergio Peña's performance during the 20-21 season in the Dutch first division. The analysis highlihts the importance of the correct interpretation of machine & deep learning advanced metrics and models. The study contains descriptive and predictive analysis.
xG and xGA Betting Model
Python program designed to provide support in Soccer betting strategies. It analyzes the statistics of the different teams and outputs a betting recommendation based on parameters and conditions for each game. A great portion of it involves Data Cleaning and Manipulation.
Understat Web Scrapping Project
In this project the "Real Madrid vs FC Barcelona - Season 2021-2022" game data is extracted from understat.com leveraging Beautiful Soup's web scrapping capabilities to later use the data to create a visualization (xG FlowChart) .
Expected Goals Barca Descriptive Analysis
In this project, the Expected Goals statistics of FC Barcelona under Xavi is analyzed and compared to the club's stats under the previous manager: Ronald Koeman. The study shows a huge improvement in both performance and results.
Renato Tapia Defensive Analysis
Quick comparative statistical project that select three strengths (Tackles, Interceptions and Blocks) of Renato Tapia's defensive performance over the season and compare them to those of the selected TOP defensive midfielders in La Liga 2020-2021 .
Calories & Macros Fitness Tracking System
The program outputs the total Calories needed for the goal (Caloric Surplus, Caloric Deficit, or Body Recomposition), along with the amount of macronutrients (Protein, Fat, and Carbs) needed per day, based on the Total Energetic Expenditure.