A survey conducted in January 2025 in the United States revealed that over half of NBA fans were Caucasian. Meanwhile, 20.7 percent were Hispanic.

When surveyed in January 2025, it was found that the age group with the highest share of NBA fans was the 35 to 49-year-old demographic. In total, 26.2 percent of respondents in this age bracket were fans of the world's leading basketball league. Meanwhile, 7.2 percent of 13 to 17-year-olds were NBA fans.

A January 2025 survey in the United States revealed that over 60 percent of NBA fans who attended or watched games were male. Meanwhile, just under 40 percent of NBA fans were female.

An average of **** million viewers tuned in to watch NBA regular season games across ABC, ESPN and TNT in the 2024/25 season. This marked a slight decline in the number of viewers from the previous season.

NBA Rookies Performance Statistics and Minutes Played: 1980-2016

Tracking Basketball Prodigies' Growth and Achievements

By Gabe Salzer [source]

About this dataset

This dataset contains essential performance statistics for NBA rookies from 1980-2016. Here you can find minute per game stats, points scored, field goals made and attempted, three-pointers made and attempted, free throws made and attempted (with the respective percentages for each), offensive rebounds, defensive rebounds, assists, steals blocks turnovers efficiency rating and Hall of Fame induction year. It is organized in descending order by minutes played per game as well as draft year. This Kaggle dataset is an excellent resource for basketball analysts to gain a better understanding of how rookies have evolved over the years—from their stats to how they were inducted into the Hall of Fame. With its great detail on individual players' performance data this dataset allows you to compare their performances against different eras in NBA history along with overall trends in rookie statistics. Compare rookies drafted far apart or those that played together- whatever your goal may be!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset is perfect for providing insight into the performance of NBA rookies over an extended period of time. The data covers rookie stats from 1980 to 2016 and includes statistics such as points scored, field goals made, free throw percentage, offensive rebounds, defensive rebounds and assists. It also provides the name of each rookie along with the year they were drafted and their Hall of Fame class.

This data set is useful for researching how rookies’ stats have changed over time in order to compare different eras or identify trends in player performance. It can also be used to evaluate players by comparing their stats against those of other players or previous years’ stats.

In order to use this dataset effectively, a few tips are helpful:

• Consider using Field Goal Percentage (FG%), Three Point Percentage (3P%) and Free Throw Percentage (FT%) to measure a player’s efficiency beyond just points scored or field goals made/attempted (FGM/FGA).

• Lookout for anomalies such as low efficiency ratings despite high minutes played as this could indicate that either a player has not had enough playing time in order for their statistics to reach what would be per game average when playing more minutes or that they simply did not play well over that short period with limited opportunities.

• Try different visualizations with the data such as histograms, line graphs and scatter plots because each may offer different insights into varied aspects of the data set like comparison between individual years vs aggregate trends over multiple years etc.

  Lastly it is important keep in mind whether you're dealing with cumulative totals over multiple seasons versus looking at individual season averages or per game numbers when attempting analysis on these sets!

Research Ideas

• Evaluating the performance of historical NBA rookies over time and how this can help inform future draft picks in the NBA.
• Analysing the relative importance of certain performance stats, such as three-point percentage, to overall success and Hall of Fame induction from 1980-2016.
• Comparing rookie seasons across different years to identify common trends in terms of statistical contributions and development over time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: NBA Rookies by Year_Hall of Fame Class.csv | Column name | Description | |:-----------------------|:------------------------------------------------------------------| | Name | The name of...

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

NBA Stats: Post Season 2023/2024🏀

Welcome to the NBA Stats dataset for the post season 2023/2024! As an avid fan of basketball and sports analysis, I created this dataset to provide a comprehensive overview of player performance in the NBA during this exciting postseason.

The dataset comprises six sub-directories: - team estimated metrics - team games - team players dashboard - team players on/off details - team players on/off ratings - team season ranks by stats

The sub-directories contains CSV files of all team's estimated metrics, all stats for every game that each team played, stats for players on every team, rankings for each team's players on and off court, total stats for each team's players on and off court, and team's stats for season rankings.

Data for this dataset was collected from the official NBA website (https://www.nba.com/) using the NBA API library(https://github.com/swar/nba_api). The dataset is intended for sports enthusiasts, data analysts, and anyone interested in exploring and analyzing NBA player statistics for the 2023/2024 season.

My passion for basketball and sports analytics inspired me to compile this dataset. I believe it can be a valuable resource for researchers, analysts, and basketball enthusiasts who wish to delve deeper into the performance trends and metrics of NBA players during this exciting season.

NBA Statistics Repository

Welcome to the NBA Statistics Repository for teams and players. This repository contains a rich and diverse dataset spanning from 1996 to 2023, drawn from NBA game statistics. It's ideal for data analysts, basketball fans, researchers, and anyone interested in the detailed numbers behind the sport.

Repo Structure

This repository contains a series of CSV files detailing the performances of teams and players from 1996 to 2023. A list of these files is provided below:

1. player_index.csv: An index of all players with general information.
2. player_stats_advanced_po.csv and player_stats_advanced_rs.csv: Advanced statistics for players during playoffs (po) and regular season (rs).
3. player_stats_defense_po.csv and player_stats_defense_rs.csv: Defensive statistics for players during the playoffs and regular season.
4. player_stats_misc_po.csv and player_stats_misc_rs.csv: Miscellaneous player statistics for the playoffs and regular season.
5. player_stats_scoring_po.csv and player_stats_scoring_rs.csv: Scoring statistics for players during the playoffs and regular season.
6. player_stats_traditional_po.csv and player_stats_traditionnal_rs.csv: Traditional player statistics during the playoffs and regular season.
7. player_stats_usage_po.csv and player_stats_usage_rs.csv: Player usage statistics during the playoffs and regular season.
8. team_stats_advanced_po.csv and team_stats_advanced_rs.csv: Advanced team statistics during the playoffs and regular season.
9. team_stats_defense_po.csv and team_stats_defense_rs.csv: Defensive team statistics during the playoffs and regular season.
10. team_stats_four_factors_po.csv and team_stats_four_factors_rs.csv: Four factors team statistics during the playoffs and regular season.
11. team_stats_misc_po.csv and team_stats_misc_rs.csv: Miscellaneous team statistics during the playoffs and regular season.
12. team_stats_opponent_po.csv and team_stats_opponent_rs.csv: Team opponent statistics during the playoffs and regular season.
13. team_stats_scoring_po.csv and team_stats_scoring_rs.csv: Scoring team statistics during the playoffs and regular season.
14. team_stats_traditional_po.csv and team_stats_traditional_rs.csv: Traditional team statistics during the playoffs and regular season.

How to Use this Data

To use this data, simply clone this repository and use a software capable of reading CSV files, such as Excel, R, Python (with pandas), etc.

Contributions

Contributions to this repo are welcome. If you have additional data to add or corrections to make, please feel free to open a pull request.

License

These data are released under the MIT License. See the LICENSE file for more information.

NBA Player Statistics Dataset (Regular Season & Playoffs)

Overview

This dataset contains detailed NBA player statistics for both the regular season and playoffs, including per-game performance metrics and advanced analytics such as Player Efficiency Rating (PER). The dataset is useful for basketball analytics, machine learning projects, and statistical research on player performance.

Dataset Features

• Basic Information

  • Player: Name of the player
  • Age: Player's age in the season
  • Team: Team abbreviation
  • Pos: Position played (e.g., PG, SG, SF, PF, C)
  • Season Type: Indicates whether stats are from Regular Season or Playoffs

• Per-Game Statistics

  • G: Games played
  • GS: Games started
  • MP: Minutes played per game
  • FG, FGA, FG%: Field goals made, attempted, and percentage
  • 3P, 3PA, 3P%: Three-pointers made, attempted, and percentage
  • 2P, 2PA, 2P%: Two-pointers made, attempted, and percentage
  • FT, FTA, FT%: Free throws made, attempted, and percentage
  • ORB, DRB, TRB: Offensive, defensive, and total rebounds per game
  • AST: Assists per game
  • STL: Steals per game
  • BLK: Blocks per game
  • TOV: Turnovers per game
  • PF: Personal fouls per game
  • PTS: Points per game

• Advanced Metrics

  • PER: Player Efficiency Rating, a metric that measures per-minute performance while adjusting for pace

Usage

This dataset is ideal for:
✅ Basketball analytics (player comparisons, efficiency analysis)
✅ Machine learning projects (predicting player performance, clustering player roles)
✅ Data visualization (trends in player stats, team comparisons)

NBA Game-Level Team Statistics, updated nightly.

This dataset contains comprehensive NBA game-level team statistics scraped from NBA.com, including raw scraped data and processed data from 2006 until today (certain statistical categories were not recorded before 2006). These are team level statistics and does not include player-level data. Data includes all the 'advanced statistics' categories on the NBA.com website (traditional, advanced, scoring, four-factors, and misc).

This data was compiled to support ML projects that predict game winners.

Column Descriptions

See data_dictionary.csv for complete column descriptions - this file contains detailed information about every column in every CSV file, including data types and descriptions.

Data Files

Raw Scraped Data (cumulative_scraped/)

• games_traditional.csv: Traditional box score stats (points, rebounds, assists, etc.)
• games_advanced.csv: Advanced metrics (offensive/defensive rating, pace, etc.)
• games_four-factors.csv: Four Factors of basketball (shooting, turnovers, rebounding, free throws)
• games_misc.csv: Miscellaneous statistics
• games_scoring.csv: Detailed scoring breakdown

Processed Data

• processed/games_boxscores.csv: Combined game-level statistics (one row per game with team statistics for home and visitor teams)
• processed/teams_boxscores.csv: Combined statistics for each team (two rows per game, 1 for each team)
• processed/column_mapping.json: JSON file mapping column names across different data sources

Update Frequency

Dataset is updated nightly with the latest NBA game results during the season.

Source

Data scraped from NBA.com official statistics.

Related Project

GitHub: https://github.com/cmunch1/nba_analysis_project

Here are a few use cases for this project:

1. Real-Time Game Analysis: The NBA-Player-Detector can be used by coaches or analysts to track player movements, interactions between players, and ball possession in real-time. This could provide valuable insights for decision-making during games and fine-tuning of strategies.

2. Enhanced Sports Broadcasting: Broadcast companies can use the model to automatically detect and highlight players on the screen during a live broadcast. It can help viewers follow the game more closely, especially in identifying less known players, and enhance the overall viewing experience.

3. Player Training and Evaluation: The NBA Player Detector can be used to analyze the performance of individual players during training sessions or competitive games. It could help trainers identify areas where a player could use improvement, such as shooting or passing skills.

4. Sports Betting and Predictions: Bettors or prediction companies can use real-time or historical data from the model to predict player or team performance. Such insights may influence betting odds or decision-making in fantasy sports.

5. Fan Engagement and Interaction: Sports apps can integrate the computer vision model for interactive features, such as allowing fans to click on a player during a live game stream to view their statistics or history. This could significantly enhance fan engagement and satisfaction.

The dataset contains data for each of the players who have interacted with the NBA during a specific period of time (last season) and collects all the accumulated statistics. In addition, it summarizes the performance of each player through the rest of the data by means of the player efficiency rating (PER) variable, a metric that takes into account all the data extracted from a player.

Current and updated dataset of NBA Playoff statistics since the 1949-1950 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 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!

If you want the regular season statistics check out my other data set.

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

The database contains several datasets and files with NBA statistical data spanning four seasons (2015-2016 to 2018-2019). These datasets were procured from the Basketball Reference database (https://www.basketball-reference.com/), a publicly accessible source of NBA data.

The main file, `dat.cleaned.csv`, includes the Win/Loss records for all thirty NBA teams, along with box scores and advanced statistics. The data captured over the four seasons correspond to about 4,920 regular-season games. A distinguishing feature of this dataset is the repeated measurements per player within a team across the seasons. However, it's important to note that these repeated measurements are not independent, necessitating the use of hierarchical modelling to properly handle the data.

Two sets of additional text files (`per_2017.txt`, `per_2018.txt`, `rpm_2017.txt`, `rpm_2018.txt`) provide specific metrics for player performance. The 'PER' files contain the Athlete Efficiency Rating (PER) for the years 2017 and 2018. The 'RPM' files contain the ESPN-developed score called Real Plus-Minus (RPM) for the same years.

However, potential biases or limitations within the datasets should be acknowledged. For instance, the Basketball Reference website might not include data from some matches or may exclude certain variables, potentially affecting the quality and accuracy of the dataset.

NBA Player Statistics (2000-2009)

This dataset contains the regular season statistics of NBA players from the 2000-2001 season to the 2009-2010 season. The data is presented in separate CSV files for each season. I take this datasets from https://www.nba.com/stats/players/traditional?Season=2000-01&SeasonType=Regular+Season

Features:

Player: Name of the player.

Team: Team the player played for.

GP: Games Played

MIN: Minutes Played

PTS: Points

FGM: Fields Goals Made

FGA: Fields Goals Attempted

FG%: Field Goals Percentage

3PM: 3 Point Field Goals Made

3PA: 3 Point Field Goals Attempted

3P%: 3 Point Field Goals Percentage

FTM: Free Throws Made

FTA: Free Throws Attempted

FT%: Free Throws Percentage

OREB: Offensive Rebounds

DREB: Defensive Rebounds

REB: Rebounds

AST: Assists

STL: Steals

BLK: Blocks

TOV: Turnovers

EFF: Average efficiency rating per game. (PTS + REB + AST + STL + BLK − Missed FG − Missed FT - TO) / GP

Potential Use Cases:

Performance Analysis: Analyze the performance of players over multiple seasons based on various statistics such as points per game, rebounds, assists, etc. Identify top-performing players in different categories. Team Comparison: Compare the performance of different teams over the years. Analyze which teams have been consistently strong or have shown improvement over time. Player Development: Study the development of individual players over their careers. Analyze how their statistics have changed over time and identify factors contributing to their improvement. Predictive Modeling: Build predictive models to forecast player performance or team outcomes based on historical data. Use machine learning algorithms to predict future trends or outcomes. Fantasy Sports: Use the data for fantasy sports analysis and team selection. Create algorithms to optimize fantasy team selection based on player statistics and performance trends. The dataset provides a comprehensive view of player performance in the NBA during the specified period, making it valuable for various analytical and predictive purposes.

This dataset contains detailed statistics for NBA games, focusing on player performance and team metrics. It includes various features such as shooting percentages, rebounds, assists, and defensive stats. The dataset is designed to help predict the outcome of NBA games and analyze player efficiency based on their in-game performance.

Columns: - PLAYER_NAME: Name of the player - PLAYER_ID: Unique identifier for each player - TEAM_NAME_x: Name of the player's team - LOCATION: Game location (home or away) - MIN_x: Minutes played by the player - FGM: Field goals made - FGA: Field goals attempted - FG_PCT: Field goal percentage - FG3M: Three-point field goals made - FG3A: Three-point field goals attempted - FG3_PCT: Three-point field goal percentage - FTM: Free throws made - FTA: Free throws attempted - FT_PCT: Free throw percentage - OREB: Offensive rebounds - DREB_x: Defensive rebounds - REB: Total rebounds - AST: Assists - TOV: Turnovers - STL_x: Steals - BLK_x: Blocks - BLKA: Blocked attempts - PF: Personal fouls - PFD: Personal fouls drawn - PTS: Points scored - PLUS_MINUS: Plus/minus statistic - Efficiency: Player efficiency rating - Season Type: Regular season or playoffs - GP_x: Games played by the player - W_x: Wins by the player's team - L_x: Losses by the player's team - Logo_URL: URL of the team logo - DISPLAY_FIRST_LAST_x: Player's display name - TEAM_ID: Unique identifier for each team - TEAM_ABBREVIATION: Abbreviation of the team name - HEADSHOT_URL: URL of the player's headshot - DISPLAY_FIRST_LAST_y: Player's display name (alternative column) - POSITION: Player's position - TEAM_NAME_y: Team name (alternative column) - GP_y: Games played by the player's team - W_y: Wins by the player's team (alternative column) - L_y: Losses by the player's team (alternative column) - W_PCT: Win percentage of the player's team - MIN_y: Minutes played by the team - DEF_RATING: Defensive rating - DREB_y: Defensive rebounds (team) - DREB_PCT: Defensive rebound percentage - STL_y: Steals (team) - BLK_y: Blocks (team) - OPP_PTS_OFF_TOV: Opponent points off turnovers - OPP_PTS_2ND_CHANCE: Opponent second-chance points - OPP_PTS_FB: Opponent fast-break points - OPP_PTS_PAINT: Opponent points in the paint - GP_RANK: Games played rank - W_RANK: Wins rank - L_RANK: Losses rank - W_PCT_RANK: Win percentage rank - MIN_RANK: Minutes played rank - DEF_RATING_RANK: Defensive rating rank - DREB_RANK: Defensive rebounds rank - DREB_PCT_RANK: Defensive rebound percentage rank - STL_RANK: Steals rank - BLK_RANK: Blocks rank - OPP_PTS_OFF_TOV_RANK: Opponent points off turnovers rank - OPP_PTS_2ND_CHANCE_RANK: Opponent second-chance points rank - OPP_PTS_FB_RANK: Opponent fast-break points rank - OPP_PTS_PAINT_RANK: Opponent points in the paint rank - Conference: Conference of the team

Context

I was having an everyday conversation with two of my friends here about how much programming knowledge we need for our college classes. One of my friends is extremely knowledgeable on basketball statistics; he can recall seemingly randomly stats for almost any college player. When we learned about his process for writing articles and making conclusions based on data, we realized that using machine learning would expedite his process almost immediately. So the first step would be to compile all the data we need.

Content

Some of the statistics are obvious such as points, blocks, etc. However, some advanced statistics employ complicated equations, such as offensive rating and PORPAG. These statistics all need to be taken with a grain of salt, since some can be misleading. Specifically, plus/minus may seem to be an effective statistic for ranking how much of an impact players have on their team, but this can be heavily impacted by rotations.

For instance, on my favorite NBA team, the Golden State Warriors, plus/minus is almost irrelevant, since any player that is on the court with Stephen Curry almost always has a much better plus/minus than players who are forced to play without his presence on the court.

However, with the sheer bulk of stats present, I'm hoping there will be clear patterns that emerge with further digging into the data.

Acknowledgements

Avinash Chauhan and Logan Norman, who helped inspire this idea.

Fantasy Sports Market Outlook

According to our latest research, the global fantasy sports market size reached USD 29.4 billion in 2024, reflecting robust growth driven by increasing digital adoption and the rising popularity of sports-based entertainment worldwide. The market is projected to grow at a CAGR of 13.7% from 2025 to 2033, reaching a forecasted value of USD 92.3 billion by 2033. This impressive expansion is fueled by technological advancements, growing smartphone penetration, and evolving consumer preferences for interactive and immersive experiences in sports engagement.

One of the primary growth factors for the fantasy sports market is the surge in internet connectivity and mobile device usage globally. With the proliferation of affordable smartphones and high-speed internet, users can now access fantasy sports platforms seamlessly, regardless of their location. This accessibility has democratized participation, enabling fans from diverse demographics to actively engage in fantasy leagues. Furthermore, the integration of real-time data analytics and AI-driven insights has enhanced user experience, making team selection, performance tracking, and in-game strategies more interactive and data-driven. This technological integration not only attracts seasoned sports enthusiasts but also draws in new users seeking an engaging and competitive environment.

Another significant driver is the increasing popularity of major sporting events and leagues, which has a direct positive impact on the fantasy sports ecosystem. Global events such as the FIFA World Cup, the Indian Premier League (IPL), the National Football League (NFL), and the NBA have witnessed exponential viewership, translating into higher user engagement on fantasy platforms. Brands and sponsors are leveraging this trend by partnering with fantasy sports companies to enhance fan engagement and brand visibility. Additionally, the gamification of sports through fantasy leagues allows users to test their knowledge, compete with peers, and win rewards, further incentivizing participation and boosting market growth.

The evolving regulatory landscape is also shaping the future of the fantasy sports market. Several countries are recognizing fantasy sports as a game of skill rather than chance, leading to favorable regulatory frameworks that encourage innovation and investment. This legal clarity has attracted significant venture capital and strategic partnerships, fostering the development of advanced platforms with enhanced security, transparency, and user trust. However, regulatory challenges persist in certain regions, necessitating ongoing dialogue between industry stakeholders and policymakers to ensure responsible growth and user protection.

From a regional perspective, North America continues to dominate the fantasy sports market, accounting for the largest share in 2024, followed by Asia Pacific and Europe. The North American market benefits from a mature sports culture, high digital literacy, and established leagues, while Asia Pacific is witnessing rapid growth due to its large, young population and increasing adoption of digital entertainment platforms. Europe is also emerging as a key market, driven by the popularity of football and cricket-based fantasy leagues. The Middle East & Africa and Latin America are gradually gaining traction, supported by improving internet infrastructure and rising interest in sports-based gaming.

Platform Analysis

The platform segment of the fantasy sports market is bifurcated into web-based and mobile-based platforms, each playing a pivotal role in shaping user engagement and market dynamics. Web-based platforms were the initial drivers of the fantasy sports industry, offering comprehensive interfaces and robust functionalities catering to desktop users. These platforms continue to attract a significant user base, particularly among older demographics and traditional fantasy sports enthusiasts who prefer detailed analytics, in-depth research tools, and multi-league management features. The web-based segment

Basketball Fan Travel Market Outlook

The global Basketball Fan Travel market size reached USD 5.8 billion in 2024, reflecting a robust industry that is propelled by the increasing enthusiasm and mobility of basketball fans worldwide. According to our latest research, the market is projected to grow at a CAGR of 8.2% from 2025 to 2033, reaching an estimated USD 11.3 billion by the end of the forecast period. This growth is majorly attributed to the rising popularity of international basketball tournaments, greater accessibility of travel arrangements, and the expansion of digital booking platforms, all of which are making it easier than ever for fans to follow their favorite teams and players globally.

One of the primary factors fueling the growth of the Basketball Fan Travel market is the global proliferation of basketball leagues and tournaments. Events such as the NBA, EuroLeague, FIBA World Cup, and various collegiate championships have witnessed a surge in international viewership and attendance. Fans are increasingly eager to experience live games, especially high-stakes matches and playoff series, which has led to a spike in demand for specialized travel services. The emergence of fan-centric tour packages, exclusive behind-the-scenes experiences, and meet-and-greet opportunities with players are further enhancing the appeal of traveling for basketball events. This trend is amplified by the growing use of social media, where fans share their travel experiences, thus inspiring others to embark on similar journeys.

Another significant growth driver is the advancement in digital technology and the widespread adoption of online booking platforms. The convenience offered by online travel agencies (OTAs), mobile apps, and dedicated sports travel portals has revolutionized how fans plan and book their trips. These platforms provide comprehensive solutions, including ticketing, accommodation, transportation, and even curated itineraries, all in one place. The integration of artificial intelligence and machine learning allows for personalized recommendations, dynamic pricing, and real-time updates, making the travel planning process more efficient and user-friendly. As a result, there is a notable shift from traditional travel agents to digital channels, especially among younger demographics who prefer seamless, tech-driven experiences.

The increasing disposable income and changing lifestyle preferences, particularly in emerging economies, are also contributing to the market's expansion. As middle-class populations grow and travel becomes more affordable, more fans are able to participate in international basketball events. This is complemented by the efforts of sports organizations and tourism boards to promote basketball tourism through strategic partnerships, marketing campaigns, and the development of fan zones and hospitality suites. These initiatives not only attract hardcore enthusiasts but also appeal to casual fans and families, broadening the market base. Additionally, the rise of corporate hospitality and incentive travel programs centered around basketball games is opening new avenues for market growth.

From a regional perspective, North America continues to dominate the Basketball Fan Travel market, accounting for the largest share due to the presence of the NBA and a highly engaged fan base. However, Europe and Asia Pacific are emerging as significant markets, driven by the increasing popularity of basketball and the hosting of major tournaments in these regions. Latin America and the Middle East & Africa are also witnessing gradual growth, supported by grassroots basketball initiatives and improved travel infrastructure. Each region presents unique opportunities and challenges, influenced by cultural preferences, economic conditions, and the maturity of local basketball ecosystems.

Service Type Analysis

The Basketball Fan Travel market is segmented by service type into ticketing, accommodation, transportation, tour packages, and others. Among these, ticketing remains the most critical component, as securing game tickets is the primary motivator for fan travel. The evolution of digital ticketing platforms has streamlined the purchase process, offering fans access to official tickets, resale options, and exclusive event experiences. The emergence of blockchain technology is further enhancing ticket authenticity and reducing fraud, thereby building trust among trav

Context

On April 6, 2022, the NBA released their power rankings for the 2021-2022 Rookie of the Year (ROTY). With seemingly the strongest draft class in recent memory, fans have been eager to see which rookie wins the award this year. The NBA released their current top 5 rookies as:

1. Evan Mobley
2. Cade Cunningham
3. Scottie Barnes
4. Jalen Green
5. Franz Wagner

With the ROTY award right around the corner, I wanted to see if the NBA power rankings listed truly reflected who should win ROTY. As it stands, this list states that Evan Mobley is the current front runner for the award. But does the data support this? As a result, I compiled a list of major stats between rookies and advanced stats from the top 5 rookies to confirm the data for myself. After analyzing the data, I came to the conclusion that Scottie Barnes should be the front runner for ROTY. In the next section, I'll show you why.

Analysis

First, I took a look at the major rookie statistics between all 52 rookies. I wanted to rank the top 5 rookies compared to their peers in these statistics (per game): points, rebounds, assists, steals, and blocks. Here's what I found:

Points

1 - Cade Cunningham, 17.5 2 - Jalen Green, 17 3 - Scottie Barnes, 15.5 4 - Franz Wagner, 15.2 5 - Evan Mobley, 14.9

Rebounds

1 - Evan Mobley, 8.3 3 - Scottie Barnes, 7.6 5 - Cade Cunningham, 5.5 10 - Franz Wagner, 4.5 19 - Jalen Green, 3.4

Assists

2 - Cade Cunningham, 5.5 5 - Scottie Barnes, 3.5 7 - Franz Wagner, 2.9 10 - Jalen Green, 2.6 11 - Evan Mobley, 2.5

Steals

3 - Cade Cunningham, 1.2 5 - Scottie Barnes, 1.1 9 - Franz Wagner, 0.9 11 - Evan Mobley, 0.8 17 - Jalen Green, 0.7

Blocks

1 - Evan Mobley, 1.6 4 - Scottie Barnes, 0.8 6 - Cade Cunningham, 0.7 13 - Franz Wagner, 0.4 26 - Jalen Green, 0.3

From the data, Scottie Barnes is consistently the only rookie from the top 5 that is also top 5 in every major category. Now what if we wanted to take a look at the advanced stats between the top 5 rookies? Would it paint a similar or different picture? Let's inspect stats like usage rate, player efficiency rating (PER), win shares, true shooting percentage (TS%), net rating, and value over replacement player (VORP).

Usage rate

An estimate of the percentage of team plays used by a player while they were on the floor. 1 - Cade Cunningham, 27.2 2 - Jalen Green, 23.2 3 - Franz Wagner, 20.8 4 - Evan Mobley, 20 5 - Scottie Barnes, 18.8

Player efficiency rating (PER)

A measure of per-minute production standardized such that the league average is 15. 1 - Scottie Barnes, 16.5 2 - Evan Mobley, 15.8 3 - Franz Wagner, 14.8 4 - Cade Cunningham, 13.3 5 - Jalen Green, 12.4

Win shares

An estimate of the number of wins contributed by a player. 1 - Scottie Barnes, 6.5 2 - Evan Mobley, 4.9 3 - Franz Wagner, 4.1 4 - Jalen Green, 0.7 5 - Cade Cunningham, -0.2

True shooting percentage (TS%)

A measure of shooting efficiency that takes into account 2-point field goals, 3-point field goals, and free throws. 1 - Franz Wagner, 55.9 2 - Scottie Barnes, 55.1 3 - Evan Mobley, 54.8 4 - Jalen Green, 54.6 5 - Cade Cunningham, 50.6

Net rating

Calculated as offensive rating minus defensive rating that defines how much better or worse the team is when a specific player is on the court. 1 - Scottie Barnes, 2.1 2 - Evan Mobley, 1.4 3 - Franz Wagner, -4.9 4 - Cade Cunningham, -5 5 - Jalen Green, -11.7

Value over replacement player (VORP)

A box score estimate of the points per 100 team possessions that a player contributed above a replacement level (-2.0) player, translated to an average team and prorated to an 82-game season. 1 - Scottie Barnes, 1.9 2 - Evan Mobley, 1.4 3 - Franz Wagner, 0.8 4 - Cade Cunningham, 0.3 5 - Jalen Green, -0.5

Even looking at the advanced stats, Scottie Barnes comes first in every category except in true shooting percentage. This is all while boasting the lowest usage rate out of the top 5 rookies. That's amazing!

Conclusion

After taking a closer look at the data, it's pretty clear that Scottie Barnes is the front runner for the ROTY award. Even so, what's so exciting about this draft class is that any of the top 5 rookies would be front runners in any other draft. That's how strong this draft class is! As a fan, the fact that rookies across the board are posting huge numbers and already making their mark in the league is exciting. I can't wait to see what the future holds for this draft class.

I had a lot of fun compiling this dataset of rookies and taking a closer look at the numbers. Feel free to go through the dataset and find your favorite rookies! Some other rookies I'm excited for include Herbert Jones, Ayo Dosunmu, Josh Giddey, Jonathan Kuminga, and Bones Hyland.

Thanks for stopping by!