This dataset opens the door to the intricacies of the 2023 NBA season, offering a profound understanding of the art of scoring in professional basketball. Within its comprehensive analysis, it showcases the remarkable prowess of 3 players LeBron James, James Harden, and Stephen Curry—true icons of the sport. Delve deep into the strategic brilliance that defines these players' shooting trends, performance metrics, and unwavering precision on the court. Whether you're a passionate basketball enthusiast or a data-driven analyst, this dataset provides a unique and invaluable window into the mastery of these legendary athletes and the ever-evolving game of basketball.

Key Features

How to use this dataset

Data Scientists and Analysts: Employ advanced statistical analysis to uncover hidden patterns and insights in the shooting trends of LeBron James, James Harden, and Stephen Curry.

Basketball Researchers and Analysts: Evaluate the impact of shooting techniques and performance on game outcomes.

NBA Coaches and Officials: Utilize the dataset to study the strengths and weaknesses of individual players, enabling more targeted coaching and defensive strategies.

Sports Journalists and Commentators: Access detailed statistics to enhance game commentary and provide viewers with deeper insights into player performance.

Basketball Enthusiasts and Fans: Gain a new perspective on the game by exploring the shooting trends and performance of their favorite players.

Analysis of ‘NBA Players Career Duration’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sveneschlbeck/nba-players-career-duration on 28 January 2022.

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

Context

In terms of competitiveness, work ethics and training mentality, few leagues worldwide are as hard as the National Basketball Association. If a Rookie (new player) is successful or not depends on many variables - especially on his performance in the first season. Sometimes, it is possible to use statistics about such players to predict wheter they will last 5 years in the NBA or not.

Content

The tabular data contains 22 columns, all regarding a player's performance records such as e.g. the number of 3 Points made.

Analysis

Take a look at the notebook "nba-players" to get started on how to transform, analyse or visualize the data. Interesting questions to answer might be: - Statistics about NBA Rookies (Percentage of Goal types, Number of played Games, etc.) - Statistics about NBA Games/Seasons (Average Rookie Performance, etc.) - Machine Learning models predicting a Player's Career Duration of more than 5 years (binary) or the probability therefore (Proba Prediction)

Data Source

https://data.world/exercises/logistic-regression-exercise-1

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

This is an image dataset for all NBA Players from the 2019-20 season. There are about 50 - 80 images for each player and its sorted folder-wise to make labelling easier. I have not filtered out all photos so there may be some photos that may not belong to the player.

All these images were scraped from Google images.

Here are a few use cases for this project:

1. Sports Analytics: Analysts can use the "Team_Maximes" model to collect real-time data during basketball games. This could involve tracking player movement, identifying possession changes, checking violation of some game rules, and making statistics on the success rate of teams from different shooting zones.

2. Media Broadcasting: TV broadcasters and Sports networks could use this model to enhance viewers' experience with real-time graphics, game statistics, player tracking, and to predict next moves. Additionally, it can be used in automatic gathering of game highlights.

3. Sports Betting Platforms: The firms can use the model as a tool to provide live data inputs that are critical to making betting decisions such as current scores, player statistics, and timing left.

4. Virtual Reality Training: Software developers could use this model to provide real-world, statistical data-driven scenarios for VR training programs for basketball players. This would allow players to practice against different simulated match scenarios aided by real-time data.

5. Crowd Management: Given the visual perspective, the model can help in strategic crowd management in live games, optimizing security by providing potential insights on crowd distribution and movement patterns.

Current and updated dataset of NBA statistics since the 1981-1982 season!

All standard statistics like Assists Per Game, Minutes Per Game, etc. are present as well as advanced statistics like Player Efficiency Rating (PER), Value Over Replacement Player (VORP), Win Share, and more!

This dataset was created with the intent to build a Machine Learning Model to predict the NBA MVP (Complete project is in Code section) and was web scraped from https://www.basketball-reference.com.

Feel free to let me know if there are any statistics or player information that isn't present that you think should be added!

For more details on how some statistics are calculated, please see the https://www.basketball-reference.com/about/glossary.html

Here are a few use cases for this project:

1. Sports Analytics: Track and record player movements, ball possession, and shot patterns during basketball games. This data can be used by coaches to drive strategy, assess performance, and develop individual player skills.

2. Automated Game Highlights: Generate video highlights by detecting exciting moments, such as epic passes, great defensive plays, or successful shots in basketball games. Sports broadcasters and social media enthusiasts can then use these clips to create engaging content for fans.

3. Virtual Reality Training: Incorporate the "Basket Ball Tracking" model into virtual reality training systems that mimic real-world situations to help athletes and teams improve their overall gameplay, decision-making, and situational awareness in basketball.

4. Injury Prevention and Detection: Utilize the tracked data to identify improper posture, forceful impacts between players, or unusual ball-person interactions in basketball games. The information could be used to flag potential injury risks and encourage safer playing practices.

5. Real-Time Fan Engagement: Create interactive apps or live features that allow fans to track their favorite players, stay updated on key moments, and even participate in trivia or prediction challenges based on the tracked information. This would enhance the spectator experience for basketball fans, whether they are watching the game live or online.

This paper examines the impact of lagged performance on free agent contracts for players in the National Basketball Association. The main approach of the paper is twofold. The first piece investigates how past performance affects future performance in the two seasons after contract year and compares it to the impact previous performance has on contract terms for free agent players. The second piece investigates the rationality of free agent contracts in their entirety by comparing the impact of lagged performance on total accumulated production and total dollar value paid. The goal is to determine if performance prior to contract year is underweighted in contract decision-making relative to its predictive power of future performance. There is evidence that performance in years prior to contract year is overlooked in contract determination decisions by NBA general managers, and there is mild evidence that performance data two years prior to contract year are underweighted given their predictive power of future performance.

Content

In this dataset, you will find over 400 players who were selected to the NBA all-star teams from the years 2000-2016.

Acknowledgements

This dataset comes from https://data.world/gmoney/nba-all-stars-2000-2016.

Context

• The National Basketball Association (NBA) is an American men's professional basketball league.
• It is composed of 30 teams (29 in the United States and 1 in Canada) and is one of the four major professional sports leagues in the United States and Canada.
• It is the premier men's professional basketball league in the world.
• The league was founded in New York City on June 6, 1946, as the Basketball Association of America (BAA).
• It changed its name to the National Basketball Association on August 3, 1949, after merging with the competing National Basketball League (NBL).
• The NBA's regular season runs from October to April, with each team playing 82 games.

• The league's playoff tournament extends into June. As of 2020, NBA players are the world's best paid athletes by average annual salary per player.

• The NBA is an active member of USA Basketball (USAB), which is recognized by the FIBA (International Basketball Federation) as the national governing body for basketball in the United States.

• The league's several international as well as individual team offices are directed out of its head offices in Midtown Manhattan, while its NBA Entertainment and NBA TV studios are directed out of offices located in Secaucus, New Jersey.

The NBA is the third wealthiest professional sport league after the National Football League (NFL) and Major League Baseball (MLB) by revenue.

Content

1. GM, GP; GS: games played; games started 2.PTS: points 3.FGM, FGA, FG%: field goals made, attempted and percentage 4.FTM, FTA, FT%: free throws made, attempted and percentage 5.3FGM, 3FGA, 3FG%: three-point field goals made, attempted and percentage 6.REB, OREB, DREB: rebounds, offensive rebounds, defensive rebounds 7.AST: assists 8.STL: steals 9.BLK: blocks 10.TO: turnovers 11.EFF: efficiency: NBA's efficiency rating: (PTS + REB + AST + STL + BLK − ((FGA − FGM) + (FTA − FTM) + TO)) 12.PF: personal fouls 13.MIN: minutes 14.AST/TO: assist to turnover ratio 15.PER: Player Efficiency Rating: John Hollinger's Player Efficiency Rating 16.PIR: Performance Index Rating: Euro league's and Euro cup's Performance Index Rating: (Points + Rebounds + Assists + 17.Steals + Blocks + Fouls Drawn) − (Missed Field Goals + Missed Free Throws + Turnovers + Shots Rejected + Fouls Committed)

Inspiration

This is one of the most common questions of everyone's life that how are these basketball players such rich and how are they getting paid, and even the stats about how everything is done on a team.

Context

I had a hard time finding larges amount of box scores for the NBA. I decided to scrap them off NBA_Reference.com since 2012 til the current day. I only included players who had played at least a few games rather than just a game. A few players with only a few games had webpage glitches on NBA_references end which made it impossible to download.

Content

There are two data sets provided below, the first being healthy players and other being injured games. Injured games have all 0s for stats while healthy players have the corresponding attributes filled in. There are about 1/4 a million rows in total.

Acknowledgements

I am a student from the University of Massachusetts Dartmouth in the Data Science program.

Inspiration

I was using the data to try and predict the injuries in the NBA. Please feel free to use this dataset to do anything with.

Introduction

I've been really interested in plotting and visualizing different NBA trends throughout this Thanksgiving break. Recently, I have been wanting to fact-check a common axiom I hear around the NBA during draft season: the notion that *older* draft prospects tend to have have *lower* upside. This is such a widespread belief that it can be heard on all levels, from NBA fan discussion on r/nba, to media draft analysis, to even GMs speaking about their draft choices.

For this visualization, I calculated the age of every lottery pick in the NBA draft from 1995 - 2015. I started at 1995 since this was the first modern "prep-to-pro" year with Kevin Garnett jumping from high school to the NBA. I ended at 2015 since I don't think we can develop an accurate read on the career trajectory of draft picks chosen after 2015 yet.

For each age range, I plotted a boxplot to visualize the distribution of the players' career PER, WS/48, BPM, and VORP. Let me know if you prefer to see another stat included here - I just went with the ones that Basketball Reference had publicly available.

Data

Here is the link to my plot

Key Results and Conclusions

Minimal differences among 18-21 year old prospects

It seems that differences in "upside" among 18-21 year old prospects are largely contrived by our brain's intuition, since there do not appear to be any significant difference in performance or success in the NBA for 18-19 year olds when compared to 19-20 and 20-21 year olds. Although VORP shows that the best of the best players since 1995 have been those drafted at age 18-19, the variation in distribution of BPM, WS/48, and career PER data is much lower.

Thus, we should be a lot more careful when assigning more favorable grades to extremely young prospects because they don't seem to have markedly better careers when compared to their slightly older counterparts. (Example: The data shows that 20.8 year old Donovan Mitchell would not have any different upside than 18.9 year old Kevin Knox)

Lower Extreme values for 22+ year old prospects

Interestingly, it looks like the median production is not really affected by the age of the prospect selected at all. However, there are some clear differences in the extremes.

The collective distribution of 22 and 23 year old lottery prospects shows that they tend to have much lower upper quartiles and extreme values, thus the best-case scenarios for these types of players is not as exciting. Although this difference is not as pronounced for 18-21 year olds, there is a huge drop off in the upper extreme values when moving from the 21-22 year old range to the 22-23 range.

Contrary to many other contexts, the NBA draft is a lot more about the outliers than it is about the median selection - each team is gambling on their pick becoming a future Tim Duncan or Dirk Nowitzki, and a successful draft would mean finding a franchise player-level talent. Therefore, our final conclusion is that although there are minimal differences in upside when comparing prospects in the 18-21 age range, 22+ year old prospects tend to have markedly lower ceilings than their younger peers.

​

Acknowledgements/Notes

• Data was scraped from basketball reference (player pages, draft pages, advanced stats pages) as well as wikipedia (specific dates of each draft for age calculation). Scraping was done using beautiful soup.
• Figures were processed using numpy/pandas and visualized in matplotlib.
• Sample sizes for each age range:

A group often shares a common goal and accomplishes a task that is difficult to complete alone by distributing roles. In such coordination, the non-verbal behavior among three or more members complicates the explanation of the mechanism due to complex and dynamic interactions. In cognitive science, a crucial role is indicated: to intervene moderately with others and adjust the whole balance without interrupting their main smooth interactions, using an experimental task. The findings suggest that resilient helping actions in the third role support coordination. These actions are similar to off-ball movements in team sports, which involve an on-ball player and have recently been the focus of sports science because their characteristics are not represented in common statistical data, such as a shooting success rate. Hence, a new perspective for discussing coordination has emerged, as existing theories, such as synchronization—where movements between players are spontaneously matched and organized—cannot explain the mentioned role. However, there is a lack of investigation and discussion regarding whether these findings are applicable to real-world activities. Therefore, this study applied the experimental findings to the field of sports. We developed a 3-on-3 basketball game in which the offensive role of intervention decision and adjustment is key for winning and introduced it to the practice of a female university team as a pilot study. Participants repeatedly engaged in the mini-game, and the playing was compared before and after receiving tips for this role. Consequently, in the bins of the relatively large distance between the participant required to the relevant role and each defensive player, the frequencies after receiving these tips were significantly higher. Furthermore, the winning rate on the offensive team improved temporarily; however, the effects were not maintained. These suggest that spacing skill, which maintains reasonable distances from the other players, creates favorable situations for coordination. This study may bridge the gap between controlled experiments and real-world applications and make an educational contribution; it may recommend practice design for the acquisition of spacing skills related to the crucial role.