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 ...

Introduction

Welcome to the ****Professor Hyoseok Hwang's Deep Learning class(2020)**** classification competition. 😃👋👋

Goal of Competition

Through this competition, we hope to improve deep learning skills and improve pytorch coding skills so that you can finally have a wide perspective on the deep learning model. 💪💪

Dataset Description

We offer a total of three types of image datasets: ****cheetahs, jaguars and tigers.****

900 training images and 100 validation images are provided for each type. 100 test images are not provided and final accuracy will be compared with the test dataset. (All images size is 400(H) * 400(W) * 3(RGB))

and each label is specified in the image name. Therefore, you can use the image name to create classification ground truth.

We look forward to seeing good results. 👍 👍 👍

Evaluation Method

Our evaluation method uses only accuracy.

Therefore, there is no limit to ****data preprocessing(data augmentation)**** to increase accuracy. However, models trained without using the provided dataset will be "0" points.

Contact

✔️ Prof. Hyoseok Hwang : hshwang@gachon.ac.kr ✔️ TA: Jihu Kim : paransky9577@gmail.com ✔️ TA: Byunghoon Hwang: byunghoonhwang97@gmail.com

Historical Database of NHL (National Hockey league) games from 1917-2025.

Features: * Date: date of the game * Time: time of the game (if recorded) * Away: name of the away team * AwayGoals: number of goals scored by away team * Home: name of the home team * HomeGoals: number of goals scored by home team * Result: how the game was decided (full time, overtime, Shootout) * Attendance: Amount of people in attendance (if recorded) * Length: length of game (if recorded) * Type: Type of game (regular season or playoff)

Pandas Description: |index|AwayGoals|HomeGoals|Attendance| |---|---|---|---| |count|68897.0|68897.0|22432.0| |mean|2.8212549167598007|3.263668955106899|17192.664140513552| |std|1.7475339032730433|1.9210957512056341|3560.971751060549| |min|0.0|0.0|100.0| |25%|2.0|2.0|16302.0| |50%|3.0|3.0|18006.0| |75%|4.0|4.0|18860.0| |max|16.0|15.0|105491.0|

This dataset contains information on exoplanetary systems, including the properties of their host stars and planets. It includes information such as the name of the planet, the name of the host star, the discovery method, the year and facility of discovery, the orbital period, the maximum distance from the star, the radius and mass of the planet, and the spectral type, effective temperature, and radius of the star. The dataset also includes information on the errors and limits associated with these measurements. The dataset contains 92 columns of information for each planetary system and contains data on over 4,000 confirmed exoplanets as of September 2021. You could use the data in this dataset to create simulations of planetary systems, which could be used to explore the behaviour of different types of exoplanetary systems or to investigate the potential habitability of exoplanets.

Here's a brief description of the columns

pl_name: the name of the planet. hostname: the name of the star that the planet orbits. default_flag: a flag indicating whether the planet is the default planet for the host star (1) or not (0). sy_snum: the number of stars in the system. sy_pnum: the number of planets in the system.

Planet Discovery

discoverymethod: the method used to discover the planet. disc_year: the year the planet was discovered. disc_facility: the facility that was used to discover the planet. soltype: the type of planetary system (e.g. binary star system, triple star system, etc.).

Planet Parameters

pl_controv_flag: a flag indicating whether the planet's existence is controversial (1) or not (0). pl_refname: the reference name for the planet. pl_orbper: the planet's orbital period in days. pl_orbpererr1: the positive error on the planet's orbital period. pl_orbpererr2: the negative error on the planet's orbital period. pl_orbperlim: a flag indicating whether the planet's orbital period is a lower limit (1), an upper limit (-1), or not a limit (0). pl_orbsmax: the planet's semi-major axis in AU. pl_orbsmaxerr1: the positive error on the planet's semi-major axis. pl_orbsmaxerr2: the negative error on the planet's semi-major axis. pl_orbsmaxlim: a flag indicating whether the planet's semi-major axis is a lower limit (1), an upper limit (-1), or not a limit (0). pl_rade: the planet's radius in Earth radii. pl_radeerr1: the positive error on the planet's radius. pl_radeerr2: the negative error on the planet's radius. pl_radelim: a flag indicating whether the planet's radius is a lower limit (1), an upper limit (-1), or not a limit (0). pl_radj: the planet's radius in Jupiter radii. pl_radjerr1: the positive error on the planet's radius. pl_radjerr2: the negative error on the planet's radius. pl_radjlim: a flag indicating whether the planet's radius is a lower limit (1), an upper limit (-1), or not a limit (0). pl_bmasse: the planet's mass in Earth masses. pl_bmasseerr1: the positive error on the planet's mass. pl_bmasseerr2: the negative error on the planet's mass. pl_bmasselim: a flag indicating whether the planet's mass is a lower limit (1), an upper limit (-1), or not a limit (0). pl_bmassj: the planet's mass in Jupiter masses. pl_bmassjerr1: the positive error on the planet's mass. pl_bmassjerr2: the negative error on the planet's mass. pl_bmassjlim: a flag indicating whether the planet's mass is a lower limit (1), an upper limit (-1), or not a limit (0). pl_bmassprov: the method used to determine the planet's mass. pl_orbeccen: the planet's eccentricity. pl_orbeccenerr1: the positive error on the planet's eccentricity. pl_orbeccenerr2: the negative error on the planet's eccentricity. pl_orbeccenlim: a flag pl_insol: the planet's insolation flux in Earth insolation flux units (flux received by Earth at 1 AU). pl_insolerr1: the positive error on the planet's insolation flux. pl_insolerr2: the negative error on the planet's insolation flux. pl_insollim: a flag indicating whether the planet's insolation flux is a lower limit (1), an upper limit (-1), or not a limit (0). pl_eqt: the planet's equilibrium temperature in Kelvin. pl_eqterr1: the positive error on the planet's equilibrium temperature. pl_eqterr2: the negative error on the planet's equilibrium temperature. pl_eqtlim: a flag indicating whether the planet's equilibrium temperature is a lower limit (1), an upper limit (-1), or not a limit (0). ttv_flag: a flag indicating whether there is evidence of transit timing variations for the planet (1) or not (0).

Stellar Data

st_refname: the reference name for the star. st_spectype: the spectral type of the star. st_teff: the effective temperature of the star in Kelvin. st_tefferr1: the positive e...