Overview

This dataset contains data about NCAA Basketball games, teams, and players. Game data covers play-by-play and box scores back to 2009, as well as final scores back to 1996. Additional data about wins and losses goes back to the 1894-5 season in some teams' cases.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.github_repos.[TABLENAME]. Fork this kernel to get started to learn how to safely manage analyzing large BigQuery datasets.

Acknowledgements

Sportradar: Copyright Sportradar LLC. Access to data is intended solely for internal research and testing purposes, and is not to be used for any business or commercial purpose. Data are not to be exploited in any manner without express approval from Sportradar.

NCAA®: Copyright National Collegiate Athletic Association. Access to data is provided solely for internal research and testing purposes, and may not be used for any business or commercial purpose. Data are not to be exploited in any manner without express approval from the National Collegiate Athletic Association.

Context

Fantasy basketball is a simple game. You select a team and fill out a roster. Each player has a price and you have a budget constraint that you should consider while building your team. You succeed or fail based on how well your players perform. Fantasy sport websites uses their own pricing algorithm and they mostly don’t tell people what their pricing algorithm looks like. In this case study, you will try to explore fantasy basketball data and the player pricing algorithm used for a fantasy basketball website.

Acknowledgements: Invent Analytics for providing data

Update 06-08-2022: The data now includes 2021 season.

Update 02-08-2021: The data now includes 2020 season and metrics for 2019 have been updated.

Update 08-03-2020: The data now includes 2017, 2018 and 2019 seasons. Keep in mind that metrics like gp, pts, reb, etc. are not complete for 2019 season, as it is ongoing at the time of upload.

Context

As a life-long fan of basketball I always wanted to combine my enthusiasm for the sport with passion for analytics 🏀📊. So, I utilized the NBA Stats API to pull together this data set. I hope it will prove to be as interesting to work with for you as it has been for me!

Content

The data set contains over two decades of data on each player who has been part of an NBA teams' roster. It captures demographic variables such as age, height, weight and place of birth, biographical details like the team played for, draft year and round. In addition, it has basic box score statistics such as games played, average number of points, rebounds, assists, etc.

The pull initially contained 52 rows of missing data. The gaps have been manually filled using data from Basketball Reference. I am not aware of any other data quality issues.

Analysis Ideas

The data set can be used to explore how age/height/weight tendencies have changed over time due to changes in game philosophy and player development strategies. Also, it could be interesting to see how geographically diverse the NBA is and how oversees talents have influenced it. A longitudinal study on players' career arches can also be performed.

Here are a few use cases for this project:

1. "Sports Analysis": BlackDiamond_Press model can be used in sports analytics to identify and analyze player movement, identify player positions and speeds in motorbike races or other sports involving motorbikes, and track/manage ball movement in basketball games.

2. "Safety Monitoring": The model can be utilized in public safety or traffic surveillance systems, tracking individuals and motorbike movements in the city to help maintain law and order or to analyze traffic patterns.

3. "Personal Fitness Tracker": This model can be implemented in personal fitness apps where the users perform different types of activities like playing basketball. The app can identify their actions, give feedback, and help users improve their performance.

4. "Gaming and Animation": It can be used for developing realistic games or animations where understanding the movements of a person or a motorbike is crucial for providing a real-world experience.

5. "Advertisement Analytics": Businesses could uses the BlackDiamond_Press model to analyze the impact of their outdoor advertising campaigns. It can identify how many people or motorbike riders come across their billboards, helping them accurately understand their reach.

Abstract: Campeones tells the story of the professional basketball coach Marco Montes and his team "Los Amigos" becoming vice-champions of the Spanish Paralympic National Cup. First reluctant to train the Amigos, Marco gets more and more attached to them and manages to transform the untrained and mismatched "bunch" into a successful team. In return, through his work with the Amigos, Marco can overcome his marital problems and his fear of starting a family with his wife, Sonia. Details: The movie starts with a basketball game between the Movistar Estudiantes and the Tenerife Iberostar. Marco, the co-trainer of the Movistar Estudiantes, breaks off a fight with the main trainer, Francisco Carrascosa, over the team's strategy and aggressively pushes him, which leads to a public scandal and him being fired. Frustrated, Marco gets drunk in a bar and drives home in his car. Being under the influence, he crashes into a police car and gets arrested. At the end of his trial, the judge sentences him to 18 months in jail or 3 months of community service at the "Los Amigos Association", a sports club for people with mental disabilities. Since he does not want to go to prison, he reluctantly chooses community service. On his first day, he meets with the manager of the Association, Julio, and the members of the basketball team: Fabián, Sergio, Jesus, Juanma, Paquito, Benito, Román, Manuel and Marín. After the first training session, Marco complains to Julio that the Amigos cannot even pass the ball to each other and that the whole project is just impossible. He states that his job is to "train normal players" and that the Amigos are "neither players nor normal". After some weeks of training, Julio calls Marco excitedly and tells him that the Amigos qualified for the national cup. In one of the training sessions, Marco notices Román training individually and throwing balls at the basket. Marco tells him that he seems to be "the only one who can play" and that he wants him to play in the national cup since he "doesn't want to embarrass himself". Soon after this conversation, Román leaves the team. When Marco sees Benito leave with a scooter, he calls out in disbelief. In his opinion, Benito should not drive because he could kill himself or someone else. When he starts belittling Benito's mental capacities, Julio steps up and states that, in contrast to Marco, Benito has never had an accident in his life. He explains to Marco how the members of the Amigos make their living and pass their days. He mentions the challenges they have to overcome on a daily basis and the areas where they thrive. At their first game, Benito is missing. The others tell Marco that Benito's boss is making fun of him at work and does not let him go to the game. Before the second game, Julio calls Marco and tells him that he has found a transfer who could replace Román. The player's name is "Collantes". Marco soon realizes that Collantes is actually a rather tiny woman "with a big mouth". After having won the game, the Amigos take the bus to go home. They are very excited because of their victory, which unnerves the other passengers and leads to them getting kicked off the bus. After the bus incident, Marco, who currently stays at his mother's house due to marital issues, heads to see his wife, Sonia. He tells her that he wants to quit training the Amigos because he "can't take care of 30-year-olds who behave like 6-year-olds". Sonia angrily replies that he is just getting cold feet and that, being their coach, he has to protect them. After the fight, Sonia borrows her colleague's caravan and becomes the Amigos' driver and co-trainer. With Sonia on board, the Amigos start to have a winning streak. Román and Carrascosa secretly watch one of the Amigo's games and seem to be impressed by the success of the team and Marco's coaching skills. When Marco notices Román in the audience, he apologizes to him about what he had told him in their last conversation and invites him to come back to the game. Román accepts this invitation. Soon after the conversation with Román, Marco asks Sonia to get back together with him. She tells him that she would love to but that she wants to start a family. Marco responds hesitantly and answers that this "is a really tough decision". Also, he doesn't want to have a disabled child. Sonia storms off in frustration. Marín, who overheard their conversation, tells Marco that he also does not want to have a child-like "them". If he could choose, he would also prefer a healthy child. What he would like, however, would be a father like Marco. He thanks Marco for everything he has done for the Amigos and leaves. Shortly after the successful game, Julio tells Marco that the Amigos cannot participate in the final game on the Canary Islands because the club cannot afford the flight. However, Marco has a plan. He and Sonia dress up as police officers and pay Benito's boss a visit. They pretend to have evidence against him, which proves that he is bullying and mistreating Benito. When the boss proposes a deal, they negotiate that he has to become the new sponsor of the Amigos. On the way to their final game, Julio tells Marco that Román used to be a Paralympic champion. However, he had to give back his gold medal because his coach had put up a team composed of almost only people without disabilities. Ever since this fraud, Román had never trusted a coach again. After a very narrow defeat in the final game, Marco seems to be disappointed. The team members, on the other hand, celebrate being the vice-champion. When Marco realizes this, he changes his attitude and starts celebrating with them. They say that it is even better to be the vice champions because they still have a goal that they can work on. Happily, Marco tells Sonia that he finally feels ready to have children. Soon after the game, Marco gets called by Carrascosa, who asks him to become his co-trainer of the Spanish national baseball team. When Marco tells Julio that he wants to take the job and will stop training the Amigos, he doesn't have the heart to say goodbye to his team. He mumbles an excuse and quickly walks off onto the street. When he turns around with tears in his eyes, he realizes that the Amigos have followed him. They say goodbye emotionally and tell each other what they have learned from the time they spent together. The movie ends with them joyfully doing their battle cry one last time.

Decisions often reflect implicit biases. Ethnic, racial, and gender traits are associated with stereotypes that may influence the decision-making process. Previous research shows that referees’ decisions in men’s professional sports are often biased in favor of racial and nationalistic in-groups. This study examined if similar biases exist in women’s professional sports. Additionally, this study analyzed the potential influence of the gender composition of referee teams on rapid decisions. We gathered data on referee foul calls in women’s professional basketball in Spain, 2014–2019 and defined important decisions (fifth fouls) and stressful situations (one-possession matches). The main finding is that out-groups based on racial (i.e., Black players) and nationalistic (i.e., foreign players) criteria did not differ in number of foul calls received. In stressful situations, foreign players actually received fewer fouls than Spanish players. Similarly, there was no evidence of bias due to the gender composition of referee teams: foul calls did not differ between all-male and mixed teams. Implications for race and nationality as dynamic social constructs within ethnocentric and social identity theories are discussed.

Abstract Basketball performance analysis using technical indicators dissociated from the moment they occurred in the game seems to no longer respond to emerging issues of the game as it does not identify the periods when a team’s offensive efficiency has increased or decreased. The aim was to characterize and compare the technical indicators in the positive and negative periods and in the whole game of winning and losing teams in men’s professional basketball. Fourteen games of professional men’s teams of the “Novo Basquete Brasil” Championship in the regular 2011/2012 season were filmed and analyzed. The Kolmogorov-Smirnov test was used to verify data normality. The independent T test was used for variables with normal distribution and the Mann-Whitney test for variables that did not present normal distribution, in order to compare teams’ performance. Analysis in the whole game showed that winning teams had significantly higher averages in successful 3-point field goals but in the positive periods, they showed higher averages for successful free throws, successful layups, defensive rebounds and defensive fouls, and in negative periods, losing teams made more defensive and offensive fouls. The teams’ performance in the whole game may not elucidate the determinant indicators for building the difference in the scoreboard. It is suggested that coaches should identify the periods of best and worst teams’ performance in the game and the indicators involved, preparing teams to overcome the negative periods and obtain more positive periods in the game.

Context

This data set contains combined on-court performance data for NBA players in the 2016-2017 season, alongside salary, Twitter engagement, and Wikipedia traffic data.

Further information can be found in a series of articles for IBM Developerworks: "Explore valuation and attendance using data science and machine learning" and "Exploring the individual NBA players".

A talk about this dataset has slides from March, 2018, Strata:

https://www.slideshare.net/noahgift/social-power-andinfluenceinthenba-89807740?qid=3f9f835a-f3d7-4174-8a8c-c97f9c82e614&v=&b=&from_search=1

Further reading on this dataset is in the book Pragmatic AI, in Chapter 6 or full book, Pragmatic AI: An introduction to Cloud-based Machine Learning and watch lesson 9 in Essential Machine Learning and AI with Python and Jupyter Notebook

Followup Items

• You can watch a breakdown of using cluster analysis on the Pragmatic AI YouTube channel

• Learn to deploy a Kaggle project into a production Machine Learning sklearn + flask + container by reading Python for Devops: Learn Ruthlessly Effective Automation, Chapter 14: MLOps and Machine learning engineering

• Use social media to predict a winning season with this notebook: https://github.com/noahgift/core-stats-datascience/blob/master/Lesson2_7_Trends_Supervized_Learning.ipynb

• Learn to use the cloud for data analysis.

Acknowledgement

Data sources include ESPN, Basketball-Reference, Twitter, Five-ThirtyEight, and Wikipedia. The source code for this dataset (in Python and R) can be found on GitHub. Links to more writing can be found at noahgift.com.

Inspiration

• Do NBA fans know more about who the best players are, or do owners?
• What is the true worth of the social media presence of athletes in the NBA?

An Adeps centre is a sports establishment managed by the General Administration of Sport, an administrative entity that is part of the Wallonia-Brussels Federation (FWB). Adeps centres are infrastructures dedicated to the promotion of physical activity and sport to all audiences, including young people, adults, and the elderly, as well as people with disabilities. They are accessible to all via internships, sports courses, infrastructure rentals by individuals or organizations (schools, sports clubs, associations, ...) for which specific educational programs can be organized. Each centre has its own specific facilities such as sports fields (football, tennis, basketball), swimming pools, gyms, track and field. Centres may also have facilities to accommodate participants in sports activities or places to eat. These facilities are constantly evolving. Indeed, redevelopment projects are often carried out in the centres to be able to offer citizens new or refurbished infrastructure.

This is a dataset of classified for apartments for rent in USA from various rental listing agency platforms. The dataset contains both 10,000 or 100,000 rental entries and 22 columns. The data contains missing values but has been cleaned in the way that column price and square_feet never is empty but the dataset is saved as it was created.

Potential Machine Learning and Data Science Applications: 1. Clustering: To discover new features. 2. Classification: Based on the category of classified rentals 3. Regression: for the squares feet or price column. 4. Recommendation System 5. Geo Data Analysis

Provide information id = unique identifier of apartment category = category of classified title = title text of apartment body = body text of apartment amenities = like AC, basketball,cable, gym, internet access, pool, refrigerator etc. bathrooms = number of bathrooms bedrooms = number of bedrooms currency = price in current fee = fee has_photo = photo of apartment pets_allowed = what pets are allowed dogs/cats etc. price = rental price of an apartment price_display = price converted into a display for the reader price_type = price in USD square_feet = size of the apartment address = where the apartment is located cityname = where the apartment is located state = where the apartment is located latitude = where the apartment is located longitude = where the apartment is located source = origin of classified time = when classified was created bout each attribute in your data set.