Este DataFrame contém estatísticas detalhadas sobre jogadores de futebol, organizadas em 33 colunas. Inclui informações básicas, como o nome do jogador, time e posição, além de métricas de desempenho, como gols, assistências, minutos jogados e passes completados.

Algumas colunas-chave incluem:

Jogador e Time: Identificam o atleta e seu clube. Minutos (Min.): Minutos jogados por cada jogador. Gols e Assistências: Mostram as contribuições ofensivas através de gols e assistências. xG e xAG: Métricas avançadas como "gols esperados" e "assistências esperadas", indicando a probabilidade de uma jogada resultar em gol. Cmp%: Percentual de passes completados, uma métrica importante para avaliar a precisão de passes dos jogadores. O DataFrame é ideal para análise estatística e insights sobre o desempenho dos jogadores ao longo de várias partidas. Como o conjunto de dados é continuamente atualizado, o número total de registros aumentará com o tempo.

Este conjunto de dados é baseado nas estatísticas dos jogadores do Campeonato Brasileiro, proporcionando uma visão detalhada do desempenho dos atletas nas competições nacionais.|

This DataFrame contains detailed statistics on football players, organized into 33 columns. It includes basic information such as the player's name, team, and position, along with performance metrics like goals, assists, minutes played, and completed passes.

Some key columns include:

Player and Team: Identifies the athlete and their club. Minutes (Min.): Minutes played by each player. Goals and Assists: Shows offensive contributions through goals and assists. xG and xAG: Advanced metrics like "expected goals" and "expected assists," indicating the probability of a play resulting in a goal. Cmp%: Pass completion percentage, an important metric to assess players' passing accuracy. The DataFrame is ideal for statistical analysis and insights into player performance over multiple matches. Since the dataset is continuously updated, the total number of records will increase over time.

This dataset is based on the statistics of players from the Campeonato Brasileiro, providing a detailed view of athlete performance in national competitions.

Analysis of ‘Campeonato Brasileiro de futebol’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/adaoduque/campeonato-brasileiro-de-futebol on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Campeonato Brasileiro de Futebol

18 anos de campeonato brasileiro de futebol

Conteúdo

No total 7645 partidas de 2003 à 2021

Github do projeto

https://github.com/adaoduque/Brasileirao_Dataset

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Paulo Pilotti Duarte

Released under MIT

Contents

I always want to do my best, and since I started at College I wanted to know more about Data Science. What's the best way to know about an area than going deep in the same? That's why I choose some friends to start a project in Data area.

We are doing the basic, just to get started and aprove our knowledge, so we decided to pick a theme that we like. And in Brazil everybody likes soccer! So we are picking the datas from the championship table since the 2009's one. In the table have basicily all the data os the championship and the team like, victory, losses, draws, number of goals that the team made and have took. We will do some data visualization and try to get some insights and do some graphics.

I am grateful to all the content that some friendly guys provides at internet, that's the best way to help who are getting started at this tech area at all. And the same way this guys are helping me I will try the help the most people I can, motivating, with content or whatever he needs!

I am searching for knowleadge so help me do a good project. I need your help to khow the path I have to go, to khow the next step in my project. So I hope we can help eachother.

JP. :)

Brazilian Soccer Data

This dataset consists of collecting the history and current data of all the most important competitions that Brazilian teams compete, the principal competitions are:

• Brasileirão(Brazilian soccer league)
• Libertatodes(Principal South america Competition)
• Sudamericana(South American secondary competition)
• Copa do Brasil(Brazilian Cup)

Next Steps: - structure the collection of the games of the sudamericana - Gather data from the main state championships(SP, RJ, MG, RS) - Gather more data from these championships, such as match statistics

Any questions or suggestions are welcome, feel free to collaborate on the github repository

Update [09/Dec/2024]

Uploaded match data for Brasileirão 2024

Update [07/Dec/2023]

Uploaded match data for Brasileirão 2023

Update [14/Nov/2022]

Uploaded match data for Brasileirão 2022

Update [12/Dec/2021]

Uploaded match data for Brasileirão 2021

General info

This dataset contains the matches from 2003-2024 of the Brazilian Championship A-Series (BCAS). I stress the fact that the dataset is validated, i.e., the matches produce the final ranking ipsis literis The main file is the matches-2003-2024.txt with self-explanatory columns (header). The other files are complementary to this one and the other with official rankings (ranking-2003-2024.txt).

Note for the 2020 Season

All matches starting in January/2021 were modified to January/2020 (and subsequent months in 2021) so my scripts will keep functioning without any other tweaking around. This was necessary because of COVID-19. This is important ONLY for studies where the DATES of the matches do matter.

ResearchGate paper

A more comprehensive study may be accessed on ResearchGate, which used Markov Chains for predicting Top 4 and Bottom 4 teams per season.

I stress the fact that the data has been thoroughly validated against official rankings and all exceptions that have happened during each season (detailed in the paper above, with some useful longitudinal statistics on scores).

All files (e.g. Perl scripts) are in GitHub as well.

Brazilian States

Every team belongs to a state in the federation (totalling 27). In the file I list the team's name followed by its state (after a '/' symbol).

AC: Acre AL: Alagoas AP: Amapá AM: Amazonas BA: Bahia CE: Ceará DF: Distrito Federal ES: Espírito Santo GO: Goiás MA: Maranhão MT: Mato Grosso MS: Mato Grosso do Sul MG: Minas Gerais PA: Pará PB: Paraíba PR: Paraná PE: Pernambuco PI: Piauí RJ: Rio de Janeiro RN: Rio Grande do Norte RS: Rio Grande do Sul RO: Rondônia RR: Roraima SC: Santa Catarina SP: São Paulo SE: Sergipe TO: Tocantins