Introduction
Welcome to our new blog post! Today, we are excited to share a new project requirement with you, titled “Analyzing Performance in Professional Basketball”.
In this project, our aim is to delve deep into the world of professional basketball, utilizing data and analytics to understand player performance, team dynamics, and game strategies. We believe that by analyzing these aspects, we can gain valuable insights that could potentially revolutionize the way we view and understand the game.
In the upcoming sections of this blog post, we will discuss our approach to solving this project requirement. We will walk you through our thought process, the methodologies we plan to employ, and the tools we will be using. Our goal is to provide a comprehensive solution that is both effective and efficient.
Finally, we will showcase the output of our analysis, including visualizations, key findings, and interpretations of the data. We hope that this will not only demonstrate the effectiveness of our approach but also provide interesting and valuable insights into professional basketball.
Project Requirements:
DOMAIN: Sports
CONTEXT
Company X manages the men's top professional basketball division of the American league system. The dataset contains information on all the teams that have participated in all the past tournaments. It has data about how many baskets each team scored, conceded, how many times they came within the first 2 positions, how many tournaments they have qualified, their best position in the past, etc.
DATA DESCRIPTION
Basketball.csv - The dataset contains information on all the teams so far participated in all the past tournaments.
DATA DICTIONARY:
Team: Team’s name
Tournament: Number of played tournaments.
Score: Team’s score so far.
PlayedGames: Games played by the team so far.
WonGames: Games won by the team so far.
DrawnGames: Games drawn by the team so far.
LostGames: Games lost by the team so far.
BasketScored: Basket scored by the team so far.
BasketGiven: Basket scored against the team so far.
TournamentChampion: How many times the team was a champion of the tournaments so far.
Runner-up: How many times the team was a runners-up of the tournaments so far.
TeamLaunch: Year the team was launched on professional basketball.
HighestPositionHeld: Highest position held by the team amongst all the tournaments played.
PROJECT OBJECTIVE
Company’s management wants to invest in proposals on managing some of the best teams in the league. The analytics department has been assigned with a task of creating a report on the performance shown by the teams. Some of the older teams are already in contract with competitors. Hence Company X wants to understand which teams they can approach which will be a deal win for them.
STEPS AND TASK
Read the dataset, clean the data and prepare final dataset to be used for analysis.
Perform detailed statistical analysis and EDA using univariate, bivariate and multivariate EDA techniques to get data-driven insights on recommending which teams they can approach which will be a deal win for them. Also as a data and statistics expert you have to develop a detailed performance report using this data.
Hint: Use statistical techniques and visualization techniques to come up with useful metrics and reporting. Find out the best-performing team, oldest team, team with the highest goals, team with the lowest performance etc. and many more. These are just random examples.
Please use your best analytical approach to build this report. You can mix match columns to create new ones which can be used for better analysis. Create your own features if required. Be highly experimental and analytical here to find hidden patterns. Use graphical interactive libraries to enable you to publish interactive plots in python.
Please include any improvements or suggestions to the association management on quality, quantity, variety, velocity, veracity etc. on the data points collected by the association to perform a better data analysis in the future. At least 1 suggestion for each point.
Solution Approach:
In this project, we were tasked with analyzing the performance of teams in a professional basketball league to assist Company X in identifying potential investment opportunities. Here's a breakdown of our approach:
Data Preparation:
We began by loading the dataset and examining its structure.
Utilizing pandas, we cleaned the data by handling missing values and converting relevant columns to numeric data types.
Additionally, we extracted the launch year of each team to better understand their tenure in professional basketball.
Statistical Analysis and Exploratory Data Analysis (EDA):
To gain insights into team performance, we conducted detailed statistical analysis and EDA using various techniques.
Employing both univariate and bivariate analysis, we explored distributions and relationships among different variables.
We visualized key metrics such as tournament participation, scores, wins, losses, and championships won by teams over the years.
Data Visualization:
Leveraging libraries like seaborn and matplotlib in Python, we created insightful visualizations to represent the data effectively.
Visualizations included histograms, bar plots, line plots, and count plots to highlight trends, distributions, and comparisons across teams.
Key Findings:
We identified the top-performing teams based on various criteria such as the number of matches won, highest scores, and winning percentages.
Furthermore, we highlighted the oldest teams in the league and teams with the highest and lowest performance.
Our analysis also included insights into runner-up teams and the distribution of team launches over the years.
Recommendations and Future Improvements:
Based on our analysis, we recommended approaching the top-performing teams for potential investment opportunities.
We suggested improvements for data quality, quantity, velocity, variety, and veracity to enhance future data analysis efforts.
Recommendations included capturing additional player-related data and maintaining accurate records of matches to facilitate comprehensive analysis.
Output :
Here are some outputs of the above project.
Count team launch in year
Top 10 Teams most number of times matches won
Top 10 Teams most number of times matches Lost
who played most of times they made highest score
In our project on analyzing performance in professional basketball has provided valuable insights into team dynamics and player proficiency. Through meticulous data preparation, detailed statistical analysis, and exploratory data visualization, we gained a comprehensive understanding of various performance metrics.
We identified the top-performing teams based on matches won, highlighted the oldest teams in the league, and examined patterns of success and struggle among different teams. Moreover, our analysis shed light on key aspects such as highest scores, matches lost, and consistent performers.
If you require any assistance with the project discussed in this blog, or if you find yourself in need of similar support for other projects, please don't hesitate to reach out to us. Our team can be contacted at any time via email at contact@codersarts.com.