NBA Players Performance

Players Performance & Statistics

By [source]

About this dataset

This dataset contains comprehensive performance data of National Basketball Association (NBA) players during the 2019-20 season. It includes all the crucial performance metrics crucial to assess a player’s quality of play. Here, you can compare players across teams, positions and categories and gain deeper insight into their overall performance. This dataset includes useful statistics such as GP (Games Played), Player name, Position, Assists Turnovers Ratio, Blocks per Game, Fouls per Minutes Played, Rebounds per Game and more. Dive in to this detailed overview of NBA player performance and take your understanding of athletes within the organization to another level!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides an in-depth look into the performance of NBA Players throughout the 2019-20 season, allowing an informed analysis of various important statistics. There are a number of ways to use this dataset to both observe and compare players, teams and positions.

• By looking at the data you can get an idea of how players are performing across all metrics. The “Points Per Game” metric is particularly useful as it allows quick comparison between different players and teams on their offensive ability. Additionally, exploratory analysis can be conducted by looking at metrics like rebounds or assists per game which allows one to make interesting observations within the game itself such as ball movement being a significant factor for team success.

• This dataset also enables further comparison between players from different positions on particular metrics that might be position orientated or generic across all positions such as points per game (ppg). This includes adjusting for positional skill sets; For example guard’s field goal attempts might include more three point shots because it would benefit them more than larger forwards or centres who rely more heavily on in close shot attempts due to their size advantage over their opponents.

• This dataset also allows for simple visualisation of player performance with respect to each other; For example one can view points scored against assists ratio when comparing multiple point guards etc., providing further insight into individual performances on certain metrics which otherwise could not be analysed quickly with traditional methods like statistical analysis only within similarly situated groups (e.g.: same position). Furthermore this data set could aid further research in emerging areas such as targeted marketing analytics where identify potential customers based off publically available data regarding factors like ppg et cetera which may highly affect team success orotemode profitability dynamicsincreasedancefficiencyoftheirownopponentteams etcet

Research Ideas

• Develop an AI-powered recommendation system that can suggest optimal players to fill out a team based on their performances in the past season.
• Examine trends in player performance across teams and positions, allowing coaches and scouts to make informed decisions when evaluating talent.
• Create a web or mobile app that can compare the performances of multiple players, allowing users to explore different performance metrics head-to-head

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: assists-turnovers.csv | Column name | Description | |:--------------|:----------------------------------| | GP | Number of games played. (Integer) | | Player | Player name. (String) | | Position | Player position. (String) |

File: blocks.csv | Column name | Description | |:--------------|:----------------------------------| | GP | Number of games played. (Integer) | | Player | Player name. (String) | | Position | Player position. (String) |

File: fouls-minutes.csv | Column name | Description | |:--------------|:----------------------...

Description

This dataset contains end-of-season box-score aggregates for NBA players over the 2012–13 through 2023–24 seasons, split into training and test sets for both regular season and playoffs. Each CSV has one row per player per season with columns for points, rebounds, steals, turnovers, 3-pt attempts, FG attempts, plus identifiers.

Brief overview of Files

1. end-of-season box-score aggregates (2012–13 – 2023–24) split into train/test;

2. the Jupyter notebook (Analysis.ipynb); All the code can be executed in there

3. the trained model binary (nba_model.pkl); Serialized Random Forest model artifact

4. Evaluation plots (LAL vs. whole‐league) for regular & playoff predictions are given as png outputs and uploaded in here

5. FAIR4ML metadata (fair4ml_metadata.jsonld);
   see README.md and abbreviations.txt for file details.”

6. For further information you can go to the github site (Link below)

File Details

Notebook

Analysis.ipynb: Involves the graphica output of the trained and tested data.

Trained/ Test csv Data

Others

abbrevations.txt: Involves the fundemental abbrevations of the columns in csv data

Additional Notes

Raw csv files are taken from Kaggle (Source: https://www.kaggle.com/datasets/shivamkumar121215/nba-stats-dataset-for-last-10-years/data)

Some preprocessing has to be done before uploading into dbrepo

Plots have also been uploaded as an output for visual purposes.

A more detailed version can be found on github (Link: https://github.com/bubaltali/nba-prediction-analysis/)

Context

This dataset contains 2022-2023 regular season NBA player stats per game. Note that there are duplicate player names resulted from team changes.

• 2021-2022 NBA Player Stats
• 2023-2024 NBA Player Stats

Content

+500 rows and 30 columns. Columns' description are listed below.

• Rk : Rank
• Player : Player's name
• Pos : Position
• Age : Player's age
• Tm : Team
• G : Games played
• GS : Games started
• MP : Minutes played per game
• FG : Field goals per game
• FGA : Field goal attempts per game
• FG% : Field goal percentage
• 3P : 3-point field goals per game
• 3PA : 3-point field goal attempts per game
• 3P% : 3-point field goal percentage
• 2P : 2-point field goals per game
• 2PA : 2-point field goal attempts per game
• 2P% : 2-point field goal percentage
• eFG% : Effective field goal percentage
• FT : Free throws per game
• FTA : Free throw attempts per game
• FT% : Free throw percentage
• ORB : Offensive rebounds per game
• DRB : Defensive rebounds per game
• TRB : 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

Acknowledgements

Data from Basketball Reference. Image from Clutch Points.

If you're reading this, please upvote.

We will create a customized NBA dataset tailored to your specific requirements. Data points may include player statistics, team rankings, game scores, player contracts, and other relevant metrics.

Utilize our NBA datasets for a variety of applications to boost strategic planning and performance analysis. Analyzing these datasets can help organizations understand player performance and market trends within the basketball industry, allowing for more precise team management and marketing strategies. You can choose to access the complete dataset or a customized subset based on your business needs.

Popular use cases include: enhancing player performance analysis, refining team strategies, and optimizing fan engagement efforts.

Analysis of ‘NBA Player Stats (2019-20)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nicklauskim/nba-per-game-stats-201920 on 13 September 2021.

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

Context

The 2019-20 NBA season is now officially over, closing the books on a season that has been truly remarkable in so many ways. As an avid basketball fan, I watched this season very closely and seeing as I haven't yet seen a complete, compiled set of statistics for the 2020 season, I went about creating this dataset!

Content

This dataset contains several files, each pertaining to a different type of statistic (basic, advanced, per 36 mins, etc.) for all players for the 2019-20 NBA regular season. This dataset contains all kinds of basic and advanced stats, from points and rebounds to box plus-minus and VORP.

Inspiration

These stats can be used for a variety of visualization tasks and exploratory data analysis to show trends and oddities in the numbers these players produced this season. Some example questions to ponder:

• Who were the most efficient scorers in the league this season? Where were these players most effective?
• What numbers are the biggest indicators of a player's contributions to his team's success?
• How were certain stats distributed by position? Are there any outliers, such as Nikola Jokić's assist averages at center or Ben Simmon's three-point numbers as an All-NBA point guard?
• Which players are similar to each other?

I look forward to seeing some of your insights! Have fun with it, NBA fans!

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

Dataset Description

This dataset contains two CSV files with information about the 2018 NBA regular season:

game_results_2018.csv - Contains results for each game played in the 2018 NBA regular season.

player_stats_2018.csv - Contains average per-game stats for every starter in the 2018 NBA season.

  Data Source

The data was scraped from the official NBA website and sports reference sites that track NBA stats and results.

  Data Fields

game_results_2018.csv:… See the full description on the dataset page: https://huggingface.co/datasets/Hatman/NBA-Players-Results-2018.

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 12 November 2021.

--- 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 ---

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.

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.

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...

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.

These datasets contain the list of players for each NBA team together with some attributes about them (from the season when these datasets have been published).

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.

Description

NBA anba WNBA dataset is a large-scale play-by-play and shot-detail dataset covering both NBA and WNBA games, collected from multiple public sources (e.g., official league APIs and stats sites). It provides every in-game event—from period starts, jump balls, fouls, turnovers, rebounds, and field-goal attempts through free throws—along with detailed shot metadata (shot location, distance, result, assisting player, etc.).

Also you can download dataset from github or GoogleDrive

Tutorials

1. NBA play-by-play dataset R example

I will be grateful for ratings and stars on github, but the best gratitude is use of dataset for your projects.

Useful links:

• nba-on-court: package for work with NBA and WNBA play-by-play data
• Ryan Davis: Analyze the Play by Play Data
• Python nba_api package for work with NBA API: https://github.com/swar/nba_api
• R hoopR package for work with NBA API: https://hoopr.sportsdataverse.org/

Motivation

I made this dataset because I want to simplify and speed up work with play-by-play data so that researchers spend their time studying data, not collecting it. Due to the limits on requests on the NBA and WNBA website, and also because you can get play-by-play of only one game per request, collecting this data is a very long process.

Using this dataset, you can reduce the time to get information about one season from a few hours to a couple of seconds and spend more time analyzing data or building models.

I also added play-by-play information from other sources: pbpstats.com, data.nba.com, cdnnba.com. This data will enrich information about the progress of each game and hopefully add opportunities to do interesting things.

Contact Me

If you have any questions or suggestions about the dataset, you can write to me in a convenient channel for you:

• LinkedIn
• GIthub
• X
• Telegram

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 Draft Combine Measurement Data from 2012-2019

Examining the Physical Attributes of Basketball Prospects

By Andrew Chou [source]

About this dataset

This dataset contains vital statistics from the NBA Draft Combine for the period of 2012 to 2019. It includes player information, height (both with and without shoes), wingspan, vertical jump, weight, body fat percentage, hand size, bench press reps, agility score and sprint time. Through these gathered statistics, a unique snapshot is offered insight into an athlete's physical performance prior to the draft in order to form an informed decision on their potential relative to other draft prospects. This wealth of data is essential in understanding how players can differ and may influence both their importance in the league as well as their potential value drafted. The combination of variables paints a detailed portrait of each athlete which further acts as great resource for both scouts and analysts alike when predicting future impact at the professional level

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains measurements from the NBA Draft Combine from 2012 to 2019, including player information, height, wingspan, vertical jump, weight, body fat percentage, hand size, bench press reps and agility & sprint times. In order to use this dataset effectively and gain valuable insights from it to understand the trends of NBA pre-draft training around each year during the 2012-2019 Draft Combines.

• Firstly analyze the necessary data fields available in this which is essential for exploring certain tendencies between players and predicting their potential as a professional athlete based on their draft combine measure experiences: Player name; Year; Weight; Height (No Shoes); Vertical (Max Reach); Body Fat %; Hand Length; Bench Press Reps & Agility & Sprint Times.
• Secondly evaluate Raw Data closely by plotting graphs with selected fields e

Research Ideas

• Comparisons of draft prospects from year to year. For example, by analyzing the data from multiple years, patterns in the data could be identified that suggest trends in preferred measurements for certain player types and positions.
• Predictive analytics for predicting where a player might be drafted based on their talent level, athletic abilities and measurements taken at the NBA Draft Combine.
• Visualization of the various categories (height, wingspan, body fat percentage) and how these correlate with player performance on the court as well as scouting reports/opinions about particular players/positions

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_draft_combine_all_years.csv | Column name | Description | |:-----------------------------|:---------------------------------------------------| | Player | Name of the player. (String) | | Year | Year of the NBA Draft Combine. (Integer) | | Draft pick | Draft pick of the player. (Integer) | | Height (No Shoes) | Height of the player without shoes. (Float) | | Height (With Shoes) | Height of the player with shoes. (Float) | | Wingspan | Wingspan of the player. (Float) | | Standing reach | Standing reach of the player. (Float) | | Vertical (Max) | Maximum vertical jump of the player. (Float) | | Vertical (Max Reach) | Maximum vertical jump reach of the player. (Float) | | Vertical (No Step) | No step vertical jump of the player. (Float) | | Vertical (No Step Reach) | No step vertical jump reach of the player. (Float) | | Weight ...

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.

Context

As the season has come to an end and at the moment we are already deep in playoff basketball, I wanted to take a look and see if I can at any way get to some data so I can predict the MVP of 2018-19 season. After a quick search, I came across all mvp votings since 1968-69 up to this past seasons on basketball-reference. I wrote a scraper and got the data. I also got the data for current season. However, I scraped only the data from 1980-81 season up to now because that's when the media started to choose MVP of the league.

Content

The mvp_votings.csv represents the train data. It holds various basketball statistics. You can view some of the descriptions of the stats in my medium post The target value for regression can be award_share column which represents the share of the votes that the players have won.

Acknowledgements

All of the data is owned by basketball reference, and I do not own any of the data.

Image belongs to nba.com

Inspiration

What is the most important statistic which defines how will be the MVP?

What are your predictions for this season?

How did the most important feature change over the year?

How big of an impact does a team's win percentage hold with all other features?

Data was collected using a mixed qualitative-quantitative web survey, which was administered using E-lomake survey software. The survey included 20 questions of which 3 included multiple statements on a 5-point Likert-like scale. 12 of the questions were open-ended and the focus of both data collection and analysis was on qualitative understanding rather than quantification. The respondents were asked to describe and rate their experiences of the development-led archaeology process, usefulness and use of archaeological information, and to indicate the branch and size of the organisation they represented. The sample is essentially a convenience sample of Finnish and Swedish organisations, which contracted archaeological investigations in 2013-2014. For Sweden, the names of the organisations were harvested semi- automatically using custom-written php-scripts from the PDF reports covering the chosen timeframe and available at the Samla database of the NHB (samla.raa.se). For Finland, the same data was collected from Muinaisjäännösten hankerekisteri (engl. Antiquities Project Registry) database (http://kulttuuriymparisto. nba.fi) maintained by the National Board of Antiquities of Finland. Email addresses of the organisations and, as possible, individuals working at relevant parts of the organisation (depending on the type of the organisation, generally planning, development and property management related functions) were collected using public online sources, including the websites of the organisations. Invitations were sent during the summer and autumn of 2015 to 241 Swedish organisations and 131 Finnish organisations. One reminder to participate in the survey was submitted to all organisations. Nine invitations were returned as definitely undeliverable. In total 34 organisations participated in the survey, 14 from Finland and 20 from Sweden. Twenty of the 34 respondents classified their organisations as municipal which corresponds relatively well with the distribution of the organisations in the original population (126 of the 241 Swedish and 87 of the 131 Finnish organisations were municipalities, excluding municipal e.g. energy and water supply companies). Five of the 34 respondents represented construction companies, 3 organisations in the energy branch, 3 regional and 2 national public bodies. One organisation from the property development, mining and environmental consulting branches participated in the survey. Amounts of employess varied - eight of 34 organisations had less than 10, 14 of the 34 organizations had between 11 and 100, five of the 34 had between 101 and 999, and seven of the 34 organizations had over 1000 employees. Especially for Sweden, it is important to note that the collection of reports is not complete, partly because only a part of the available reports contained information on the organisations who had contracted and/or financed investigations. It is also possible that the semi-automated harvesting process failed to find a small number of organisations. In addition, it is likely that in a number of organisations, the invitation did not reach the relevant respondents even if the invitation contained a request to forward it to a colleague if the recipient considered herself to be unable to take the survey. Therefore, even if the sampling approach was designed to reach a reasonable level of systematicity, coverage and comparability, the lack of a comprehensive project or central report registry in Sweden, technical issues, variation in the reporting of the contracting organisations, and the varying specificity of contact details mean that the final sample is closer to a convenience sample than a systematic cross section. (Description from Huvila, I. Land developers and archaeological information. Open Information Science, 2017, 1(1), 71-90) The dataset was originally published in DiVA and moved to SND in 2024. Se engelsk version av denna katalogpost för beskrivning. Datasetet har ursprungligen publicerats i DiVA och flyttades över till SND 2024.

These data and R files are used to demonstrate the threat of collider bias, which has been largely ignored in work disability research. Data include NBA player data and aggregated workers' compensation data, plus several simulated datasets.