Basketball on Paper 4

Total Page:16

File Type:pdf, Size:1020Kb

Basketball on Paper 4 BASKETBALL ON PAPER بسکتبال روی کاغذ گرداوری: مهران حاتمی امار امار:سنتی پیشرفته امتیاز میزان استفاده از توپ مقایسه ای انلیز اماردفاعی فاکتور چهارگانه میزان تﻻش بازیکن ارائه روش و راه حل برای پیگیری فردی بازیکن پیشرفت امار برترین ها امار سنتی)عرف( پیشرفت فردی و تیمی از طریق دستیابی به نقاط شوت و ضعف و قوت برد و حرکات کارایی فانتزی و.... مربی تماشاچی بازیکن مربیگری راجع انچه میدانید ویاانچه میتوانید بر روی کاغذ بنویسید نیست بلکه راجع به توانایی شما درانجام وظیفه بازیکنان در زمین است ” ون گاندی ” در تمام رشته ها امار و انالیز تبدیل به فرهنگ ان رشته ورزشی شده است ارزیابی بازیکنان خود و حریف ارزیابی تیم خود وحریف ایجاد انگیزه به تیم برای افرایش کارایی از طریق امار موثر در طراحی پﻻن دفاعی ارزیابی توان شوت زنی تیم خودی و حریف بوسیله دیاگرام شوت تبیین اهداف دفاعی و حمله ای با دیاگرام شوت که منتهی به اجازه دادن به نحوه پرتاب شوت توسط بازیکنان حریف و یا خود میشود ......... جداول اماری:)کاغذ هایی که تواما تحلیل میشوند و ما را به حقایق نزدبک میکنند( تﻻش HUSTLE- کاربردی USAGE- سنتی TRADITIONAL- دفاع DEFENCE - فاکتور چهارگانه FOUR FACTOR - پیشرفته ADVANCE- مقایسه ای MATCH UP - سایر فاکتورها MISCELLANUEUS - امتیاز SCORING- • TO اماراز دید اکثر مربیان: OPP TO • - فردی - تیمی ASS • - بررسی اهداف مربی OPP ASS • -بررسی استراتزی خود وحریف STL • -امار در زمین خود و یا حریف --امار بازیکن پیرامونی و یا پست OPP STL • • FT% SHAQ SYNDRUM 80% • FG% 40% OPP FG% LESS THAN 35% WIDE - OPEN SHOT • OUR POINTS • OPP POINTS LESS THAN 15 POINTS 2 PLAYERS TOGETHER 25 PTS 3 TOP PLAYERS TOGETHER 30 PLS TOTALLY 50-60 PTS • DOUBLING UP • DOUBLING DOWN • FOR POST OPP REB BLK SHO • ALL CHAMPIONS HAVE GOOD REBOUNDER SHOT BLOCKER PEOTECT THE RIM سایر مربیان:امار تنها کافی نیست تحلیل و برداشت از امار صحیح مهم است علم در صددغلیه برشم و غریزه و تجربه برای پیش بینی و توضیح وقایع - نحوه اماده سازی وطراحی پﻻن واستراتزی در بسکتبال است بازیکنانی هستند که: - اماری ندارند ولی ایجاد انگیزه میکنند - هدایت میکنندد - تیم نظم برقرار میکنند - بعضی ها غیر قابل باور دفاع میکنند - سایر بازیکنان را تر و تازه و موثرنگه میدارند - این 50 درصد تﻻش و 50 درصد ذاتی است بعضی بازیکنان در هیچ راداری دیده نمیشوندو مربی باید انها را پیدا و خلق کند. بنابراین بعضی ها فقط در امار به ترن اور ریباند درصد شوت و درصد پرتاب ازاد بسنده میکنندو دنبال بهتر شدن خود از طریق تمرکز در موارد زیرند: ----- افرایش توانایی بدنی ------ انتخاب و پیشبرد توانایی ذهنی بازیکن و اطﻻعات ساختاری بسکتبال ------ ایجاد پختگی روانی بازیکن به منظور جلوگیری از حسادت خودخواهی و غر زدن ----- امادگی روانی ---- مهارتهای بسکتبال نحوه برخورد با امار)عرف....( هدف گذاری یاد اوری قبل از مسابقه تخریب نه سعی برای رسیدن به هدف با تحلیل امار درست و بجا تحلیل درست: STEAL/4 OPP EASY LAY UP GOOD DEFENDER!!!!! ????? NO -4 -50% FG GOOD SHOOTER!!!!!!???????? NO MOST LAYUP -10 DEF REBOUND GOOD REB!!!!!!!?????? NO ALWAYS FAR FROM OPPONENT LET HIM SHOOT -51 REBOUND GREAT BUT WE SHOULD BE BETTER SHOOTER چرا درصد ریباند دفاع و حمله و نه تعداد انها از اهمیت زیادی MEHRAN 10MIN 6REB برخوردار است؟ HASSAN 11MIN 4REB سوال : چه کسی ریباندر بهتری است؟ MEHRAN 2 MORE!!!!! 6< 4 تیمها ضعیف شوت زدند و 22فرصت ریباند MEHRAN 6/22 وضعیت شوت بهتر شد و فقط 9 فرصت ریباند بود HASSAN 4/9 4/9>>>6/22 HASSAN>>>MEHRAN بنابراین درصد ریباند اهمیت بیشتری دارد تا تعداد ریباند بنابراین باید به دنبال دو مفهوم زیر باشیم: DRB% & ORB% در هر کاری با بررسی شاخص های عملکردی میتوان به موفقیت و یا عدم ان پی برد با بررسی شاخص های دفاعی میتوان به نحوه تمرینات دفاعی و ارزیابی خود رسید 1-درصد شوت حریف در مقایسه با خود 2- امتیازحریف در هر بازی POINT PER POSSESION) GOOD DEF PPP LOW) 3-تعداد مالکیت که با فاکتورهای زیرافزایش مییابد: درصد ریباند دفاعی درصد ریباند حمله ترن اور توپ دزدی 4-امتیاز در هر مالکیت RUN AND GUN GAME (تعداد بیشتر مالکیت برای دو تیم ) مثال :تحلیل مناسب SHOOTING PPP% YOUR TEAM 33% 60 /90 TO=15 MORE توجه: ( OPPONENT 30% 70 / 105 )FT 15/20 کدام تیم عملکرد بهتری دارد؟تیم شما درصد شوت بهتر ولی بعلت خطا درصد و ترن اور مالکیت کمتری دارد!!! مشکل در دفاع است؟ علت تفاوت 10 امتیاز چیست مشکل حمله است نه دفاع چهار فاکتور مهم در پیروزی تیم شوت 40 درصد ترن اور 25 درصد ریباند 20 درصد پرتاب ازاد 15 درصد مقایسه این فاکتور با حریف در سال NBA 2013 درصد شوت: AVERAGE 49.5% فینالیست ها MAIMI HEAT 55.24% SAN ANTONIO 53.06% جنگ ترن اور نسبت همکاری به ترن اور???? = AST/TO حدود شوت 50 درصد امار نرمال است پس اگر 10 توپ لوداده داشته باشی باید بیست همکاری داشته باشی تا در این جدال بازنده نباشی. همکاری موجب شوت خوب میشودونه پرتاب اجباری که موجب ضد حمله میشود جنگ ریباند پرتاب ازاد بیشتر تیمی نفوذی تر است که درصد پرتاب پنالتی بیشتری نسبت به شوت سه امتیازی دارد. همکاری در به ثمر رساندن گل بیشتری هم دارد. ترن اور : موجب ضدحمله با گل درصد باﻻ افزایش فول افزایش پرتاب ازاد افت روحی روانی تیم بردن جنگ ترن اور) اهمیت مالکیت توپposs( مناطق قوی و ضعیف شوت بازیکنان خودی و حریف را نشان میدهد که در تعیین تاثیر ستهای حمله ای منجر به شوت موثر است معموﻻ بازیکنان در دو منطقه از سه منطقه نشان داده شده بهتر شوت میزنند!!!!! قانون پیرسون وقتی عملکردی اندازه گیری میشود بهبود مییابد اما وقتی عملکرد انداره گیری میشود وگزارش گیری وثبت میشود سرعت پیشرفت شتاب مییابد تعداد و تفاضل ریباند خیلی کار گشا نیست بلکه درصد ریباند دفاعی و حمله ای مهم است امارها را باریکنان در میان گذاشته و هدف گذاری کنید)کوتاه مدت و بازی به بازی( کدام تیم بهتر ریباند دفاعی داشته؟ YOUR TEAM 30%SHOOT 20 REB • کدام تیم بهتر ریباند حمله ای داشته؟ OPPONENT 60% SHOOT 14REB • • DRB%=DEFREB/(DEFREB+OPPOFFREB) NBA 73% • ORB%=OFFREB/(OFFREB+OPPDEFREB) NBA 27% برای بهبودی ریباند دو فاکتور اماری دیگر نیز مهم است: • ORA% = NO OF REB ATTEMPT/NO OF OPPORTUNITY(VIDEO TAPE) (INFRONT OR BEHIND DEFENSIVE REB) • BOXOUT %=NO OF BOXOUT ATTEMPT/NO OF OPPORTUNITY(VIDEO TAPE) • FETCH AND ATTACK THE BALL NOT JUST BOXOUT, • STAR PLAYER 25% OTHERS 50% !!!!!! HELP HIM "مقایسه امار نماینده ایران و کره در جام باشگاههای اسیا " 12<<<7 !!!!!!!! امار سنتی)عرف( :Traditional box score امارهایی که کم و بیش اکثرا با ان اشنا هستیم و انرا مﻻک قرار می دهیم: این امار بخشی از واقعیت هستند و اگر به پشت امار فکر نکنیم دچار تصمیم و مﻻکهای اشتباه میشویم مثال:تعداد ریباند دفاع و حمله مﻻک صحیحی از ریباندر بودن بازیکنان نیست امار سنتی نمونه در فیبا: لیست بازیکنان برداشت ها و دیاگرامهای بدست امده از امار سنتی که به طرز فوق العاده ای نشان داده شده اند نتیجه استفاده صحیح از برگه اماری است که من انرا تحلیل امار نامگذاری میکنم دیاگرام های مقایسه ای دو تیم که از امار سنتی بدست می اید: سایردیاگرام های مقایسه ای: مقایسه درصد شوت –فول-بﻻک-ترن اور-همکاری و توپ دزدی و روندامتیاز گیری موقعیت شوت: دیاگرام شوت های زده شده و شوت های به ثمر نشسته در هر کوارتر موقعیت شوت: موقعیت شوت: موقعیت گل نزده تیمی موقعیت گل زده فردی موقعیت گل زده موقعیت گل نزده گزارش گیری لحظه به لحظه بازی گزارش گیری لحظه به لحظه بازی گزارش گیری لحظه به لحظه بازی گزارش گیری لحظه به لحظه بازی گزارش گیری لحظه به لحظه بازی فقط دو پریود اورده شده است درصد شوت تیمی امار شوت تیمی 4th 4th 5th 6th این امار نشان نمیدهد که پترو تیم قهرمان 8 نفره است!!!!!!! ریباند تیمی ریباند بازیکن در هر 40 دقیقه 1st تعداد فول در هر بازی:14.8 پترو 74 1st گل زده و همکاری تیمی در به ثمر رساندن ان 4th 5th 2-3th 1th 1th 5th تعداد مالکیت بیشتر پترو را قهرمان اسیا کرد امار انفرادی بازیکنان در همکاری –توپ دزدی-ریباندو دفاع کم خطا تﻻش زیادی کردند امار کل تورنمنت بصورت انفرادی: تحلیل اولیه: در حمله ضعیف ولی یکنواخت و عدم وابستگی به فرد در دفاع عالی بی فول در درصد شوت 2 امتیازی چهارم در درصد شوت 3 امتیازی چهارم در درصد شوت پالتی پنجم بهترین تیم از نظر تعداد ریباند کمترین فول کننده علیرغم مدافع ترین تیم نفوذ کم امار پیشرفته Advancd box score امتیازگیری از 100 مالکیت توپ OffRtg Offensive Rating Measures a team's points scored per 100 possessions. On a player level this statistic is team points scored per 100 possessions while he is on court Formula 100*((Points)/(POSS) امتیازدادن از 100 مالکیت توپ حریف DEFRTG Defensive Rating The number of points allowed per 100 possessions by a team. For a player, it is the number of points per 100 possessions that the team allows while that individual player is on the court Formula 100*((Opp Points)/(Opp POSS)) تفاوت امتیاز گرفته شده و داده شده از 100 مالکیت توپ NetRtg Net Rating Measures a team's point differential per 100 possessions. On player level this statistic is the team's point differential per 100 possessions while he is on court .Formula OFFRTG - DEFRTG درصد همکاری بازیکن به کل همکاریها AST% Percent of Team's Assists Definition The percentage of a team's assists that a player has while on the court نسبت ترن اور به همکاری AST/TO Assist to Turnover Ratio Definition The number of assists for a player or team compared to the number of turnovers they have committed تعداد همکاری از 100 مالکیت توپ AST Ratio Assist Ratio Definition Assist Ratio is the number of assists a player averages per 100 possessions used Formula (AST * 100) / (POSS) درصد ریباند دفاعی فردی یا تیمی %DREB Defensive Rebounding Percentage The percentage of available defensive rebounds a player or team obtains while on the floor.
Recommended publications
  • Difference-Based Analysis of the Impact of Observed Game Parameters on the Final Score at the FIBA Eurobasket Women 2019
    Original Article Difference-based analysis of the impact of observed game parameters on the final score at the FIBA Eurobasket Women 2019 SLOBODAN SIMOVIĆ1 , JASMIN KOMIĆ2, BOJAN GUZINA1, ZORAN PAJIĆ3, TAMARA KARALIĆ1, GORAN PAŠIĆ1 1Faculty of Physical Education and Sport, University of Banja Luka, Bosnia and Herzegovina 2Faculty of Economy, University of Banja Luka, Bosnia and Herzegovina 3Faculty of Physical Education and Sport, University of Belgrade, Serbia ABSTRACT Evaluation in women's basketball is keeping up with developments in evaluation in men’s basketball, and although the number of studies in women's basketball has seen a positive trend in the past decade, it is still at a low level. This paper observed 38 games and sixteen variables of standard efficiency during the FIBA EuroBasket Women 2019. Two regression models were obtained, a set of relative percentage and relative rating variables, which are used in the NBA league, where the dependent variable was the number of points scored. The obtained results show that in the first model, the difference between winning and losing teams was made by three variables: true shooting percentage, turnover percentage of inefficiency and efficiency percentage of defensive rebounds, which explain 97.3%, while for the second model, the distinguishing variables was offensive efficiency, explaining for 96.1% of the observed phenomenon. There is a continuity of the obtained results with the previous championship, played in 2017. Of all the technical elements of basketball, it is still the shots made, assists and defensive rebounds that have the most significant impact on the final score in European women’s basketball.
    [Show full text]
  • An Analysis of the Contract Year Phenomenon in the NBA: Do Players Perform Better Or Worse Tyler Gaffaney Claremont Mckenna College
    Claremont Colleges Scholarship @ Claremont CMC Senior Theses CMC Student Scholarship 2013 An Analysis of the Contract Year Phenomenon in the NBA: Do Players Perform Better or Worse Tyler Gaffaney Claremont McKenna College Recommended Citation Gaffaney, Tyler, "An Analysis of the Contract Year Phenomenon in the NBA: Do Players Perform Better or Worse" (2013). CMC Senior Theses. Paper 768. http://scholarship.claremont.edu/cmc_theses/768 This Open Access Senior Thesis is brought to you by Scholarship@Claremont. It has been accepted for inclusion in this collection by an authorized administrator. For more information, please contact [email protected]. CLAREMONT McKENNA COLLEGE An Analysis of the Contract Year Phenomenon in the NBA: Do Players Perform Better or Worse? SUBMITTED TO Professor Ricardo Fernholz AND Dean Nicholas Warner BY Tyler Gaffaney for SENIOR THESIS Fall 2013 December 2, 2013 Acknowledgements First, I would like to thank anyone who contributed to this thesis in any way. I would also like to thank my family, especially my parents for their support over the years. Specifically, I would like to thank my reader Professor Ricardo Fernholz for his keen insight and continued advice. Without his help, this thesis would not have been possible. i Abstract The present study uses a novel measure of over performance (percent deviation from career average) to analyze the contract year phenomenon in the NBA. Historically, the literature has pointed toward over performance across almost all statistical measures of performance. However, previous research has assumed that all players are universally affected by the presence of a contract year in the same manner.
    [Show full text]
  • Forecasting Most Valuable Players of the National Basketball Association
    FORECASTING MOST VALUABLE PLAYERS OF THE NATIONAL BASKETBALL ASSOCIATION by Jordan Malik McCorey A thesis submitted to the faculty of The University of North Carolina at Charlotte in partial fulfillment of the requirements for the degree of Master of Science in Engineering Management Charlotte 2021 Approved by: _______________________________ Dr. Tao Hong _______________________________ Dr. Linquan Bai _______________________________ Dr. Pu Wang ii ©2021 Jordan Malik McCorey ALL RIGHTS RESERVED iii ABSTRACT JORDAN MALIK MCCOREY. Forecasting Most Valuable Players of the National Basketball Association. (Under the direction of DR. TAO HONG) This thesis aims at developing models that would accurately forecast the Most Valuable Player (MVP) of the National Basketball Association (NBA). R programming language was used in this study to implement different techniques, such as Artificial Neural Networks (ANN), K- Nearest Neighbors (KNN), and Linear Regression Models (LRM). NBA statistics were extracted from all of the past MVP recipients and the top five runner-up MVP candidates from the last ten seasons (2009-2019). The objective is to forecast the Point Total Ratio (PTR) for MVP during the regular season. Seven different underlying models were created and applied to the three techniques in order to produce potential outputs for the 2018-19 season. The best models were then selected and optimized to form the MVP forecasting algorithm, which was validated by predicting the MVP of the 2019-20 season. Ultimately, two underlying models were most robust under the LRM framework, which is considered the champion approach. As a result, two combination models were constructed based on the champion approach and proved to be most efficient.
    [Show full text]
  • Mathematics Improves Basketball
    Mathematics Improves Basketball Angelica Klivan, Iris Latorre, Carmen Rosa, Cristian Yepes Early Start Program Math 105, Professor Gary Welz, Peer Mentor Kenny Gonzalez Introduction Terms to Know Methodology &RQFOXVLRQ In the game of basketball, mathematics is used ƒ True Shooting Percentage (TS%)- A statistic in ● Three players will shoot from three different Mathema constantly to improve one’s performance. basketball used to gauge shooting efficiency that positions: tics is key - Chest Height Achieving the objective of shooting a takes into consideration points scored from three in pointers, field goals, and free-throws to get a basketball includes using percentages and - Chin Height basketbal measure of points scored each shooting attempt. l. Most angles. By finding the most consistent - Over Head Height players percentage of shots made while using a certain ƒ Trajectory- A curve or surface cutting a family of curves or surfaces at a constant angle All players will shoot from the free throw line. do not angle, you can find out which player will score Each player will start from a different position, In I actively the most baskets. While shooting a ball into ƒ Backspin- A backward spin given to a moving ball, focus on causing it to stop more quickly or rebound at a resulting in a different trajectory, and continue to the basket, there has to be a mental projection mathema steeper angle upon hitting a surface rotate with each shot. on how a certain angle will affect the outcome tics while Each player will take 10 shots per position. they are of the shot.
    [Show full text]
  • Actual Vs. Perceived Value of Players of the National Basketball Association
    Actual vs. Perceived Value of Players of the National Basketball Association BY Stephen Righini ADVISOR • Alan Olinsky _________________________________________________________________________________________ Submitted in partial fulfillment of the requirements for graduation with honors in the Bryant University Honors Program APRIL 2013 Table of Contents Abstract .................................................................................................................................1 Introduction ...........................................................................................................................2 How NBA MVPs Are Determined .....................................................................................2 Reason for Selecting This Topic ........................................................................................2 Significance of This Study .................................................................................................3 Thesis and Minor Hypotheses ............................................................................................4 Player Raters and Perception Factors .....................................................................................5 Data Collected ...................................................................................................................5 Perception Factors .............................................................................................................7 Player Raters .........................................................................................................................9
    [Show full text]
  • NBA Statistics Feeds
    Updated 10.01.18 NBA Statistics Feeds 2017-18 Season 1 SPORTRADAR NBA STATISTICS FEEDS Updated 10.01.18 Table of Contents NBA Statistics Feeds .................................................................................................................................... 3 Coverage Levels ............................................................................................................................................. 4 League Information ....................................................................................................................................... 5 Game & Series Information ........................................................................................................................ 7 Venue Information .......................................................................................................................................10 Team Information ........................................................................................................................................11 Player Information .......................................................................................................................................13 Boxscore Information .................................................................................................................................16 Play-by-Play Information ...........................................................................................................................18 Period Statistics – Team ............................................................................................................................22
    [Show full text]
  • Scoring and Shooting Abilities of NBA Players
    Journal of Quantitative Analysis in Sports Volume 6, Issue 1 2010 Article 1 Scoring and Shooting Abilities of NBA Players James Piette, University of Pennsylvania Sathyanarayan Anand, University of Pennsylvania Kai Zhang, University of Pennsylvania Recommended Citation: Piette, James; Anand, Sathyanarayan; and Zhang, Kai (2010) "Scoring and Shooting Abilities of NBA Players," Journal of Quantitative Analysis in Sports: Vol. 6: Iss. 1, Article 1. Available at: http://www.bepress.com/jqas/vol6/iss1/1 DOI: 10.2202/1559-0410.1194 ©2010 American Statistical Association. All rights reserved. Scoring and Shooting Abilities of NBA Players James Piette, Sathyanarayan Anand, and Kai Zhang Abstract We propose two new measures for evaluating offensive ability of NBA players, using one- dimensional shooting data from three seasons beginning with the 2004-05 season. These measures improve upon currently employed shooting statistics by accounting for the varying shooting patterns of players over different distances from the basket. This variance also provides us with an intuitive metric for clustering players, wherein performance of players is calculated and compared to his cluster center as a baseline. To further improve the accuracy of our measures, we develop our own variation of smoothing and shrinkage, reducing any small sample biases and abnormalities. The first measure, SCAB or, Scoring Ability Above Baseline, measures a player's ability to score as a function of time on court. The second metric, SHTAB or Shooting Ability, calculates a player's propensity to score on a per-shot basis. Our results show that a combination of SCAB and SHTAB can be used to separate out players based on their offensive game.
    [Show full text]
  • Essays in Basketball Analytics
    Essays in Basketball Analytics Suraj Keshri Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2019 c 2019 Suraj Keshri All Rights Reserved ABSTRACT Essays in Basketball Analytics Suraj Keshri With the increasing popularity and competition in professional basketball in the past decade, data driven decision has emerged as a big competitive edge. The advent of high frequency player tracking data from SportVU has enabled a rigorous analysis of player abilities and interactions that was not possible before. The tracking data records two-dimensional x-y coordinates of 10 players on the court as well as the x-y-z coordinates of the ball at a resolu- tion of 25 frames per second, yielding over 1 billion space-time observations over the course of a full season. This dissertation offers a collection of spatio-temporal models and player evaluation metrics that provide insight into the player interactions and their performance, hence allowing the teams to make better decisions. Conventional approaches to simulate matches have ignored that in basketball the dy- namics of ball movement is very sensitive to the lineups on the court and unique identities of players on both offense and defense sides. In chapter 2, we propose the simulation in- frastructure that can bridge the gap between player identity and team level network. We model the progression of a basketball match using a probabilistic graphical model. We model every touch event in a game as a sequence of transitions between discrete states. We treat the progression of a match as a graph, where each node represents the network structure of players on the court, their actions, events, etc., and edges denote possible moves in the game flow.
    [Show full text]
  • A Starting Point for Analyzing Basketball Statistics
    Journal of Quantitative Analysis in Sports Volume 3, Issue 3 2007 Article 1 A Starting Point for Analyzing Basketball Statistics Justin Kubatko, The Ohio State University and basketball- reference.com Dean Oliver, Basketball on Paper and Denver Nuggets Kevin Pelton, Seattle Sonics & Storm Dan T. Rosenbaum, University of North Carolina at Greensboro and Cleveland Cavaliers Recommended Citation: Kubatko, Justin; Oliver, Dean; Pelton, Kevin; and Rosenbaum, Dan T. (2007) "A Starting Point for Analyzing Basketball Statistics," Journal of Quantitative Analysis in Sports: Vol. 3: Iss. 3, Article 1. DOI: 10.2202/1559-0410.1070 ©2007 American Statistical Association. All rights reserved. Brought to you by | University of Toronto-Ocul Authenticated Download Date | 3/2/16 11:03 PM A Starting Point for Analyzing Basketball Statistics Justin Kubatko, Dean Oliver, Kevin Pelton, and Dan T. Rosenbaum Abstract The quantitative analysis of sports is a growing branch of science and, in many ways one that has developed through non-academic and non-traditionally peer-reviewed work. The aim of this paper is to bring to a peer-reviewed journal the generally accepted basics of the analysis of basketball, thereby providing a common starting point for future research in basketball. The possession concept, in particular the concept of equal possessions for opponents in a game, is central to basketball analysis. Estimates of possessions have existed for approximately two decades, but the various formulas have sometimes created confusion. We hope that by showing how most previous formulas are special cases of our more general formulation, we shed light on the relationship between possessions and various statistics.
    [Show full text]
  • Basketball Reference Nba Pace by Year
    Basketball Reference Nba Pace By Year brontosauruses.tawdrily.Holotypic Single-entry Hal kids Aubrey shaggily and neoterizing eutrophic and monthly, Albrechther sasines she alwaysunderspending demonstrably, smear petrologically her she robe-de-chambre attack and it soli. jaunts agglomerate his Fep_object be efficient, and tarik black told silver screen and nail against regression techniques, by reference nba pace factor is an emotional return in what exactly constitutes a professional service synergy sports How will players perform this season? Steal rate estimates the percentage of opponent possessions that end with a steal by a player. TODO: we should review the class names and whatnot in use here. Despite this, player salaries have struggled to keep pace. Made some crucial adds using the smart tool and daily player projection tool. What does the community think of players? Offensive rebound percentage is an estimate of the percentage of available offensive rebounds a player grabbed while he was on the floor. Data: Share of population by weight category. Time leading scorers list. Rebound rate is a common stat found on any advanced stats site. NBA pace factor is an advanced basketball stat to help you bet smarter and make more money betting totals, spreads, and more. How do you become a referee for a math journal? The formula adds positive stats and subtracts negative ones through a statistical point value system. Of course, the downside is the loss of Anderson, to a division rival no less. Fique por dentro do calendário de NBA, por data e com toda a informação do início das partidas, cobertura na TV e mais.
    [Show full text]
  • Sport & Entertainment
    Volume 1, Issue 1, February 2015 EDITOR Joris Drayer D.H. Kwak Stacy Warner Bob Heere Temple University University of Michigan East Carolina University University of South Carolina Brendan Dwyer Daniel Lock Jules Woolf [email protected] Virginia Commonwealth Griffith University Ohio University ASSOCIATE EDITOR University Eric MacIntosh Xiaoyan Xing Chad Seifried Andrea Eagleman Ottawa University Capital University of Physi- Louisiana State University Griffith University Kimberly Mahoney cal Education and Sport [email protected] Sheranne Fairley University of New Haven Masayuki Yoshida EDITORIAL BOARD University of Queensland Haylee Mercado Biwako Seikei Sport College Artemisia Apostolopoulou Kevin Filo University of South Carolina EDITORIAL ASSISTANT Robert Morris University Griffith University Brianna Newland Henry Wear Khalid Ballouli Damian Gallagher University of Delaware University of South Carolina University of South Carolina University of Ulster Nico Schulenkorf MANAGING EDITOR Tim Breitbarth Marion Hambrick University of Technology Matt Brann Bournemouth University University of Louisville Sydney FiT Publishing Beth Cianfrone Yuhei Inoue Stephen Shapiro Georgia State University University of Minnesota Old Dominion University DESIGNER Jamie Pein Geoff Dickson Jeremy Jordan Brian Soebbing Auckland University of Temple University Louisiana State University Technology Shannon Kerwin Scott Tainsky Brock University University of Illinois Sport & Entertainment Review is an electronic journal owned Subscription rates: US: $50 (individual), $200 (institution). by the University of South Carolina’s Department of Sport Back issue articles are searchable and can be downloaded at and Entertainment Management and published three times www.fitpublishing.com. a year (February, June, and October) by FiT Publishing, A Division of the International Center for Performance Ex- Advertising: A current rate card is available upon request.
    [Show full text]
  • Ranking Methods for Data Analytics on Players' Performance in Basketball Games
    Università degli studi di Padova Dipartimento di Scienze Statistiche Corso di Laurea Magistrale in Scienze statistiche Relazione Finale Ranking methods for data analytics on players’ performance in basketball games Relatore Ch.ma Prof.ssa Laura Ventura Dipartimento di Scienze Statistiche Correlatore Ch.mo Prof. Luigi Salmaso Correlatore Ch.mo Prof. Livio Corain Dipartimento di Tecnica e Gestione dei sistemi industriali Laureando Silvio Vadruccio Matricola N 1106878 Anno Accademico 2017/2018 Contents Introduction 11 1 The mathematics of hoops 13 1.1 Basketball meets data . 14 1.2 The rise of basketball data analysis . 17 1.3 Basketball data analysis worldwide . 21 1.4 A game of spaces . 22 1.4.1 Talking with numbers . 23 1.5 Data, data, data . 24 1.6 Roles in basketball . 25 2 Performance measures 27 2.1 The player or the team? . 27 2.1.1 Bottom-up measures . 28 2.1.2 Top-down measures . 29 2.2 Player value metrics . 30 2.2.1 Possessions . 31 2.2.2 Player Efficiency Rating . 33 2.2.3 Wins produced . 37 2.2.4 Approximate Value . 41 3 Basketball Data 47 3.1 The box score . 48 3.2 Play-by-play . 54 3.3 Combining the data . 57 3.3.1 Extracting player’s performance from opponent’s pro- duction . 58 3.3.2 Extracting player’s performance from team’s production 63 3.3.3 Levelling players performances . 63 4 Ranking method for sport data analysis 65 4.1 Round robin design . 66 4.2 Testing hypothesis and ranking . 68 4.2.1 Properties of the ranking estimator .
    [Show full text]