Global Basketball Statistics Summary

Total Page:16

File Type:pdf, Size:1020Kb

Global Basketball Statistics Summary Updated 08.16.18 Global Basketball Statistics Summary 2018 1 SPORTRADAR GLOBAL BASKETBALL STATISTICS FEEDS Updated 08.16.18 Category & Sport Information Category Country Code Category Name Sport Name Category Id Sport Id Tournament Information Coverage Includes Current Season Id Season Id Coverage Information Current Season Name Season Name Coverage Level Abbreviation Current Season Start Date Season Start Date Coverage Level Name Current Season Year Season Year Coverage Tournament Name Group Name Tournament Id Coverage Type Abbreviation Round Number Tournament Name Coverage Type Name Round Type Current Season End Date Season End Date Team Information Jersey Base Color Jersey Squares Manager Nationality Jersey Horizontal Stripe Jersey Stripes Team Abbreviation Jersey Number Color Jersey Type Team Country Jersey Shirt Type Manager Country Code Team Country Code Jersey Sleeve Color Manager Id Team Id Jersey Split Manager Name Team Name Player Information Active Height Position Country Code Id Role Type/Position Date Of Birth Jersey Number Start Date End Date Nationality Weight Gender Name Venue Information Capacity Country Name Map Coordinates City Id Name Country Code Probability Information Away/Home Team Win Probability Market Name Outcome Name Match Information Away Score Sport Event Status Away Score Tournament Round Group Home Score Sport Event Status Home Score Tournament Round Match Number Match Id Sport Event Status Tournament Round Matches Period Number Sport Event Status Match Status Tournament Round Number Period Type Sport Event Status Winner Id Tournament Round Other Match Id Scheduled Date/Time Start Time to be Determined Tournament Round Type Sport Event Conditions Attendance Team Qualifier 2 SPORTRADAR GLOBAL BASKETBALL STATISTICS FEEDS Updated 08.16.18 Timeline Information Assist Player Id Event Period Event Value Assist Player Name Event Period Name Event X Coordinate Assist Type Event Points Event Y Coordinate Event Away Score Event Stoppage Time Goal Scorer Id Event Home Score Event Team Goal Scorer Name Event Id Event Time Event Match Time Event Type Team Match Statistics Assists Misses Three Point Attempts Total Ball Possession Rebounds Time Spent In Lead Biggest Lead Shots Blocked Timeouts Fouls Steals Turnovers Free Throw Attempts Successful Team Leads Two Point Attempts Successful Free Throw Attempts Total Three Point Attempts Successful Two Point Attempts Total Player Match Statistics Assists Free Throws Three Pointers Attempts Blocked Shots Free Throws Attempts Total Rebounds Blocks Against Offensive Rebounds Turnovers Defensive Rebounds Personal Fouls Two Pointers Efficiency Points Two Pointers Attempts Fouls On Three Pointers Standings Information Change Group Type Rank Current Outcome Losses Standings Tie Break Rule Games Behind Losses Conference Standings Type Group Group Name Losses Division Streak Group Id Played Wins Group Name Points Against Wins Conference Group Parent Points For Wins Division 3 SPORTRADAR GLOBAL BASKETBALL STATISTICS FEEDS .
Recommended publications
  • The Evolution of Basketball Statistics Is Finally Here
    ows ind Sta W tis l t Ready for a ic n i S g i o r f t O w a e r h e TURBOSTATS SOFTWARE T The Evolution of Basketball Statistics is Finally Here All New Advanced Metrics, Efficiencies and Four Factors Outstanding Live Scoring BoxScore & Play-by-Play Easy to Learn Automatically Tags Video Fast Substitutions The Worlds Most Color-Coded Shot Advanced Live Game Charts Display... Scoring Software Uncontested Shots Shots off Turnovers View Career Shooting% Second Chance Shots After Each Shot Shots in Transition Includes the Advanced Zones vs Man to Man Statistics Used by Top Last Second Shots Pro & College Teams Blocked Shots New NET Rating System Score Live or by Video Highlights the Most Sort Video Clips Efficient Players Create Highlight Films Includes the eBook Theory of Evolution Creates CyberLink Explaining How the New PowerDirector video Formulas Help You Win project files for DVDs* The Only Software that PowerDirector 12/2010 Tracks Actual Rebound % Optional Player Photos Statistics for Individual Customizable Display Plays and Options Shows Four Factors, Event List, Player Team Stats by Point Guard Simulated image on the Statistics or Scouting Samsung ATIV SmartPC. Actual Screen Size is 11.5 Per Minute Statistics for Visit Samsung.com for tablet All Categories pricing and availability Imports Game Data from * PowerDirector sold separately NCAA BoxScores (Websites HTML or PDF) Runs Standalone on all Windows Laptops, Tablets and UltraBooks. Also tracks ... XP, Vista, 7 plus Windows 8 Pro Effective Field Goal%, True Shooting%, Turnover%, Offensive Rebound%, Individual Possessions, Broadcasts data to iPads and Offensive Efficiency, Time in Game Phones with a low cost app +/- Five Player Combos, Score on the Only Tablets Designed for Data Entry Defensive Points Given Up and more..
    [Show full text]
  • Australian Basketball Statistics Association
    Basketball Statistics Calling Protocol Calling Protocol – April 2009 Edition. Australian Basketball Statistics Committee AUSTRALIAN BASKETBALL STATISTICS COMMITTEE CALLING PROTOCOL APRIL 2009 EDITION 2 Calling Protocol – April 2009 Edition. Australian Basketball Statistics Committee Written by The Australian Basketball Statistics Committee The contents of this manual may not be altered or copied after alteration TABLE OF CONTENTS Calling Protocol ........................................................................................................4 Reasons for a Protocol: ................................................................................................. 4 General Principles: ........................................................................................................ 4 Calling The Action: ....................................................................................................... 5 LiveStats - SPECIFIC CALLS .................................................................................. 7 Time Outs: .................................................................................................................... 7 Substitutions: ............................................................................................................... 7 Player Checks: .............................................................................................................. 7 CALLING IN SEQUENCE ......................................................................................... 8 3 Calling Protocol
    [Show full text]
  • Data-Driven Basketball Web Application for Support in Making Decisions
    Data-driven Basketball Web Application for Support in Making Decisions Tomislav Horvat1a, Ladislav Havaš1b, Dunja Srpak1c and Vladimir Medved2d 1Department of Electrical Engineering, University North, 104 Brigade 3, Varaždin, Croatia 2Department of General and Applied Kinesiology, Faculty of Kinesiology, University of Zagreb, Zagreb, Croatia Keywords: Basketball, Information System, Making Decisions, Statistics Analysis, Web Application. Abstract: Statistical analysis combined with data mining and machine learning is increasingly used in sports. This paper presents an overview of existing commercial information systems used in game analysis and describes the new and improved version of originally developed data-driven Web application / information system called Basketball Coach Assistant (later BCA) for sports statistics and analysis. The aim of BCA is to provide the essential information for decision making in training process and coaching basketball teams. Special emphasis, along with statistical analysis, is given to the player’s progress indicators and statistical analysis based on data mining methods used to define played game point’s difference classes. The results obtained by using BCA information system, presented in tables, proved to be useful in programing training process and making strategic, tactical and operational decisions. Finally, guidelines for the further information system development are given primarily for the use of data mining and machine learning methods. 1 INTRODUCTION information for decision making in training process and coaching basketball teams. The first version of Nowadays, sports statistics and analysis, more the BCA information system, called AssistantCoach, particular the information and communication was presented at the International Congress on Sport technologies are omnipresent in sport and have Sciences Research and Technology Support in Lisbon become a very important factor in making decisions (Horvat et al., 2015).
    [Show full text]
  • Official Basketball Statistics Rules Basic Interpretations
    Official Basketball Statistics Rules With Approved Rulings and Interpretations (Throughout this manual, Team A players have last names starting with “A” the shooter tries to control and shoot the ball in the and Team B players have last names starting with “B.”) same motion with not enough time to get into a nor- mal shooting position (squared up to the basket). Article 2. A field goal made (FGM) is credited to a play- Basic Interpretations er any time a FGA by the player results in the goal being (Indicated as “B.I.” references throughout manual.) counted or results in an awarded score of two (or three) points except when the field goal is the result of a defen- sive player tipping the ball in the offensive basket. 1. APPROVED RULING—Approved rulings (indicated as A.R.s) are designed to interpret the spirit of the applica- Related rules in the NCAA Men’s and Women’s Basketball tion of the Official Basketball Rules. A thorough under- Rules and Interpretations: standing of the rules is essential to understanding and (1) 4-33: Definition of “Goal” applying the statistics rules in this manual. (2) 4-49.2: Definition of “Penalty for Violation” (3) 4-69: Definition of “Try for Field Goal” and definition of 2. STATISTICIAN’S JOB—The statistician’s responsibility is “Act of Shooting” to judge only what has happened, not to speculate as (4) 4-73: Definition of “Violation” to what would have happened. The statistician should (5) 5-1: “Scoring” not decide who would have gotten the rebound if it had (6) 9-16: “Basket Interference and Goaltending” not been for the foul.
    [Show full text]
  • Ranking the Greatest NBA Players: an Analytics Analysis
    1 Ranking the Greatest NBA Players: An Analytics Analysis An Honors Thesis by Jeremy Mertz Thesis Advisor Dr. Lawrence Judge Ball State University Muncie, Indiana July 2015 Expected Date of Graduation May 2015 1-' ,II L II/du, t,- i II/em' /.. 2 ?t; q ·7t./ 2 (11 S Ranking the Greatest NBA Players: An Analytics Analysis . Iv/If 7 Abstract The purpose of this investigation was to present a statistical model to help rank top National Basketball Association (NBA) players of all time. As the sport of basketball evolves, the debate on who is the greatest player of all-time in the NBA never seems to reach consensus. This ongoing debate can sometimes become emotional and personal, leading to arguments and in extreme cases resulting in violence and subsequent arrest. Creating a statistical model to rank players may also help coaches determine important variables for player development and aid in future approaches to the game via key data-driven performance indicators. However, computing this type of model is extremely difficult due to the many individual player statistics and achievements to consider, as well as the impact of changes to the game over time on individual player performance analysis. This study used linear regression to create an accurate model for the top 150 player rankings. The variables computed included: points per game, rebounds per game, assists per game, win shares per 48 minutes, and number ofNBA championships won. The results revealed that points per game, rebounds per game, assists per game, and NBA championships were all necessary for an accurate model and win shares per 48 minutes were not significant.
    [Show full text]
  • Basketball - Wikipedia, the Free Encyclopedia
    Basketball - Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/Basketball Basketball From Wikipedia, the free encyclopedia Basketball is a sport played by two teams of five players on a rectangular court. The objective is to shoot a ball through a Basketball hoop 18 inches (46 cm) in diameter and 10 feet (3.048 m) high mounted to a backboard at each end. Basketball is one of the world's most popular and widely viewed sports.[1] A team can score a field goal by shooting the ball through the basket during regular play. A field goal scores three points for the shooting team if the player shoots from behind the three-point line, and two points if shot from in front of the line. A team can also score via free throws, which are worth one point, after the other team was assessed with certain fouls. The team with the most points at the end of the game wins, but additional time (overtime) is issued when the score is tied at the end of regulation. The ball can be advanced on the court by bouncing it while walking or running or throwing it to a teammate. It is a violation to lift or drag one's pivot foot without dribbling the ball, to carry it, or to hold the ball with both hands then resume dribbling. Michael Jordan goes for a slam dunk at the old Boston Garden As well as many techniques for shooting, passing, dribbling Highest FIBA and rebounding, basketball teams generally have player governing body positions and offensive and defensive structures (player positioning).
    [Show full text]
  • Basketball Statistics Manual
    Basketball Statistics Manual FIBA Europe Statistics Manual Field Goals A field goal attempt (FGA) is charged to a player any time they shoot, throw or tap a live ball at their opponent’s basket in an attempt to score a goal unless the player is fouled in the act of shooting and the goal is missed or not counted. When a violation or foul is committed by the shooter or a player from either team, after the ball has been released for a shot, a FGA is credited because the shot would count if successful. A field goal attempt (FGA) is not charged to the shooter if the shot is nullified because of illegal interference with the ball (goal tending) by an offensive player. In this case the offensive player who commits the violation is charged with a turnover and a team rebound charged to the defensive team. A field goal made (FGM) is credited to a player any time a FGA by them results in a goal being scored or being awarded because of illegal interference with the ball (goal tending) by a defensive player. When a player is fouled in the act of shooting and the shot results in a FGM, then a FGA must also be credited. A FGA is not charged if the player shooting the ball, a teammate or a defensive player commits a violation or foul just prior to the ball being released. The official will call the violation or foul and signal that the score or play following the call is canceled. This indicates that the ball was not released for the shot prior to the infringement so no FGA is awarded.
    [Show full text]
  • Team Leaders
    2021 FIBA Americas U16 Championship CONFERENCE BASKETBALL STATISTICS Through games of Aug 29, 2021 (All games) SCORING OFFENSE SCORING DEFENSE # Team G W-L Pts Avg/G # Team G Pts Avg/G ------------------------------------------- ------------------------------------- 1.USA................. 6 6-0 718 119.7 1.USA................. 6 189 31.5 2.Canada.............. 6 5-1 491 81.8 2.Canada.............. 6 321 53.5 3.Puerto Rico......... 6 4-2 399 66.5 3.Brazil.............. 6 355 59.2 4.Brazil.............. 6 2-4 374 62.3 4.Puerto Rico......... 6 378 63.0 5.Mexico.............. 6 3-3 338 56.3 5.Argentina........... 6 434 72.3 6.Argentina........... 6 2-4 333 55.5 6.Chile............... 6 455 75.8 7.Chile............... 6 2-4 266 44.3 7.Mexico.............. 6 500 83.3 8.Costa Rica.......... 6 0-6 219 36.5 8.Costa Rica.......... 6 506 84.3 SCORING MARGIN FREE THROW PERCENTAGES # Team G OFF DEF Margin # Team G FTM FTA Pct --------------------------------------------- -------------------------------------------- 1.USA................. 6 119.7 31.5 +88.2 1.USA................. 6 113 170 .665 2.Canada.............. 6 81.8 53.5 +28.3 2.Puerto Rico......... 6 67 105 .638 3.Puerto Rico......... 6 66.5 63.0 +3.5 3.Mexico.............. 6 64 101 .634 4.Brazil.............. 6 62.3 59.2 +3.2 4.Canada.............. 6 79 126 .627 5.Argentina........... 6 55.5 72.3 -16.8 5.Brazil.............. 6 76 147 .517 6.Mexico.............. 6 56.3 83.3 -27.0 6.Chile............... 6 45 90 .500 7.Chile............... 6 44.3 75.8 -31.5 7.Argentina..........
    [Show full text]
  • Analyzing the Influence of Player Tracking Statistics on Winning Basketball Teams
    Analyzing the Influence of Player Tracking Statistics on Winning Basketball Teams Igor Stan čin * and Alan Jovi ć* * University of Zagreb Faculty of Electrical Engineering and Computing / Department of Electronics, Microelectronics, Computer and Intelligent Systems, Unska 3, 10 000 Zagreb, Croatia [email protected], [email protected] Abstract - Basketball player tracking and hustle statistics year at the Olympic games, by winning a gold medal in the became available since 2013-2014 season from National majority of cases. Off the court, there are specialized Basketball Association (NBA), USA. These statistics provided personnel who try to improve each segment of the game, us with more detailed information about the played games. e.g. doctors, psychologists, basketball experts and scouts. In this paper, we analyze statistically significant differences One of the most interesting facts is that the teams in the in these recent statistical categories between winning and losing teams. The main goal is to identify the most significant league, and the league itself, have expert teams for data differences and thus obtain new insight about what it usually analysis that provide them with valuable information about may take to be a winner. The analysis is done on three every segment of the game. There are a few teams who different scales: marking a winner in each game as a winning make all of their decisions exclusively based on advanced team, marking teams with 50 or more wins at the end of the analysis of statistics. Few years ago, this part of season as a winning team, and marking teams with 50 or organization became even more important, because NBA more wins in a season, but considering only their winning invested into a computer vision system that collects games, as a winning team.
    [Show full text]
  • Assessing the Skill of Football Players Using Statistical Methods
    Assessing the skill of football players using statistical methods Łukasz Szczepanski´ SALFORD BUSINESS SCHOOL, UNIVERSITY OF SALFORD,SALFORD, UK SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS OF THE DEGREE OF DOCTOR OF PHILOSOPHY, JANUARY 2015 Contents 1 Introduction 1 1.1 Background . .1 1.2 Problem statement . .4 1.3 Contribution . .7 2 Player evaluation in team sports 11 2.1 Characteristics of player evaluation metrics . 12 2.1.1 Definitions . 12 2.1.2 Approaches to player evaluation . 13 2.2 Football research . 14 2.3 Research in other sports . 16 2.3.1 Baseball . 16 2.3.2 Invasion sports . 21 3 Problem statement 24 3.1 Team production model . 24 3.1.1 Individual and team performance . 25 3.1.2 Player valuation in the context of the model . 26 3.2 High I - high II approach for evaluating football players . 27 3.2.1 Regularised plus/minus model . 28 3.2.2 Introducing mixed effects Markov chain model for player eval- uation . 29 4 Data 32 5 Signal and noise in goalscoring statistics 38 5.1 Background and motivation . 39 5.2 Data . 41 5.3 Methods . 41 i CONTENTS ii 5.3.1 Generalized Linear Mixed Model . 43 5.3.2 Shot counts . 44 5.3.3 Shots to goals conversion rates . 47 5.3.4 Predicting future performance . 49 5.4 Results . 50 5.4.1 Model fit for shot counts . 50 5.4.2 Model fit for shots to goals conversion . 54 5.4.3 Comparing characteristics of the model fits . 55 5.4.4 Goals predictions .
    [Show full text]
  • The Effect Touches, Post Touches, and Dribbles Have on Offense for Men's Division I Basketball
    Brigham Young University BYU ScholarsArchive Theses and Dissertations 2009-03-04 The Effect Touches, Post Touches, and Dribbles Have on Offense for Men's Division I Basketball Kim T. Jackson Brigham Young University - Provo Follow this and additional works at: https://scholarsarchive.byu.edu/etd Part of the Exercise Science Commons BYU ScholarsArchive Citation Jackson, Kim T., "The Effect Touches, Post Touches, and Dribbles Have on Offense for Men's Division I Basketball" (2009). Theses and Dissertations. 2006. https://scholarsarchive.byu.edu/etd/2006 This Thesis is brought to you for free and open access by BYU ScholarsArchive. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of BYU ScholarsArchive. For more information, please contact [email protected], [email protected]. THE EFFECT TOUCHES, POST TOUCHES, AND DRIBBLES HAVE ON OFFENSE FOR MEN’S DIVISION I BASKETBALL by Kim T. Jackson A thesis submitted to the faculty of Brigham Young University in partial fulfillment of the requirements for the degree of Master of Science Department of Exercise Sciences Brigham Young University April 2009 Copyright © 2009 Kim T. Jackson All Rights Reserved BRIGHAM YOUNG UNIVERSITY GRADUATE COMMITTEE APPROVAL of a thesis submitted by Kim T. Jackson This thesis has been read by each member of the following graduate committee and by majority vote has been found to be satisfactory. Date Keven A. Prusak, Chair Date Todd R. Pennington Date Gilbert W. Fellingham BRIGHAM YOUNG UNIVERSITY As chair of the candidate’s graduate committee, I have read the thesis of Kim T. Jackson in its final form and have found that (1) its format, citations, and bibliographical style are consistent and acceptable and fulfill university and department style requirements; (2) its illustrative materials including figures, tables, and charts are in place; and (3) the final manuscript is satisfactory to the graduate committee and is ready for submission to the university library.
    [Show full text]
  • 2020-21 Dekalb County Boys Basketball Statistics
    2020-21 DEKALB COUNTY BOYS BASKETBALL STATISTICS FIRST INPUT – 1/7/21 SCORING – TOP 25 REBOUNDING – TOP 25 2-Point FG Percentage – Top 25 (15 Attempts) PTS REB 2PT 2PT 2PT PLAYER/SCHOOL YR GP PTS AVG PLAYER/SCHOOL YR GP REB AVG PLAYER/SCHOOL YR GP FGM FGA PCT Raheem Swain, Lithonia SR 5 123 24.6 Keshawn Evans, Druid Hills JR 2 26 13.0 Raheem Swain, Lithonia SR 5 38 56 0.68 Edwin Walker, Stephenson SR 4 89 22.3 Mason Lockhart, Columbia JR 8 87 10.9 Jaden Lingo, Druid Hills SR 2 10 16 0.63 Jaden Lingo, Druid Hills SR 2 34 17.0 Jaylen Peterson, Stephenson JR 4 40 10.0 Chase Champion, Lithonia JR 5 14 23 0.61 Mason Lockhart, Columbia JR 8 116 14.5 AJ Green, Cedar Grove NA 2 18 9.0 Mason Lockhart, Columbia JR 8 46 79 0.58 Kaleb Brown, M.L. King JR 7 97 13.9 Julius Lymon, Columbia SR 8 65 8.1 Jadan Baugh, Columbia FR 6 11 19 0.58 Jordan Sanders, SWD JR 2 27 13.5 Terrin Wofford, M.L. King SR 7 46 6.6 Wakeem Lawton, M.L. King SR 7 9 16 0.56 Kawasiki Ricks, Columbia SR 8 99 12.4 Brinson Modest, Cedar Grove SR 4 24 6.0 Daquavious Harrison, Columbia JR 8 16 29 0.55 Terrin Wofford, M.L. King SR 7 86 12.3 Jorden Ushery, Stephenson SR 4 24 6.0 Jonah Huff, Cedar Grove JR 3 11 20 0.55 Martweze Grier, M.L.
    [Show full text]