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|There are 27 entries in the glossary.|
|Big Data & Advanced Analytics Summit|
IE. Big Data & Analytics is a global community and think-tank for senior level Big Data executives, data engineers, architects, data scientists & advanced analytics executives.
IE. Summits held on fields that include Sports Analytics, Predictive Analytics, Business Intelligence and Web&Social Media Analytics.
Advanced Scout was developed by IBM during the mid 1990's as a data mining and knowledge management tool.
Advanced Scout reveals hidden patterns in NBA play-by-play data and provide additional insights to coaches and other related organizations.
Advanced Scout not only collects in-game structured stats, but also unstructured multimedia footage. NBA teams have access to Advanced Scout, so that coaches and players can use this tool to prepare for upcoming opponents and study them using historical footage.
APBRmetrics takes its name from the acronym APBR, which stands for the Association for Professional Basketball Research.
|Basketball On Paper|
A book which is written by Dean Oliver. In his book , Oliver highlights general strategies for teams when they're winning or losing and what aspects should be the focus in either situation. He describes and quantifies the jobs of team leaders and role players, then discusses the interactions between players and how to achieve the best fit.
Oliver conceptualizes the meaning of teamwork and how to quantify the value of different types of players working together. He examines historically successful NBA teams and identifies what made them so successful: individual talent, a system of putting players together, or good coaching.
A Bayesian network is a probabilistic graphical model that represents a set of variables and their probabilistic independencies.
|Bill James Revolution|
George William “Bill” James is a baseball writer, historian, and statistician whose work has been widely influential. Since 1977, James has written more than two dozen books devoted to baseball history and statistics. His approach, which he termed sabermetrics in reference to the Society for American Baseball Research (SABR), scientifically analyzes and studies baseball, often through the use of statistical data, in an attempt to determine why teams win and lose. In 2006, Time named him in the Time 100 as one of the most influential people in the world.
Access to historical NBA box score stats that are exportable to Excel.
A box score is a structured summary of the results from a sport competition. The box score lists the game score as well as individual and team achievements in the game.
1986-87 season is the earliest season available with complete box score stats.
Comments: In basketball, we need to account for intangible things such as setting the pick, making the right pass, positioning in the corner to spread the floor, clearing space by moving down the lane and etc. These kind of data are not available in the box scores, that's why it's often been misleading. Metrics derived from play-by-play (PBP) data, not 100% perfect though, might help more than box score stats.
|Competitive Balance Ratio (CBR)|
A metric that reflects team-specific variation in winning percentage over time and league-specific variation. Based on estimation of a model of the determination of annual attendance in professional baseball during the past 100 years, variation in the CBR explains more of the observed variation in attendance than other alternatives measures of competitive balance, suggesting that CBR is a useful metric.
|Front Office Staff of NBA Teams|
Looking for a dream job in the NBA or getting curious about the organizational structure of NBA franchises? The list below compiles links to front office staff of NBA teams.
|Game Flow Chart|
A unique way of illustrating a revealing summary of a basketball game with a chart showing the point innovative plots which has been presented by Peter H. Westfall in 1990.
Check out some NBA research tools with links to sources which offer game flow charts.
Comments: In basketball the outcome is difficult to summarize in a simple boxscore. Game flow charts is a very helpful tool if you didn't watch the game.
The Herfindahl index, also known as Herfindahl-Hirschman Index or HHI, is a measure of the size of firms in relationship to the industry and an indicator of the amount of competition among them.
The Herfindahl Index can be used to measure scoring balances for NBA teams.
To calculate HHI for each team: (1) divide each player's points scored by the total number of points scored by the team, (2) square that result for each player, (3) sum those squares.
|Home Court Advantage|
The home-court advantage is the net effect of several factors that may have an (generally positive) effect on the play of the home team and an (generally negative) effect on the play of the road team.
|How the NBA Schedule is Made|
Download NBA league or team schedule in Excel format including own and opponent rest days.
Matt Winick, who stepped down in September 2014 as Senior Vice President of Scheduling and Game Operations, had been the architect of the NBA schedule for more than 20 seasons. In his interview with ESPN, he unveiled how he responds to complaints about strength of schedule and common questions on building the NBA schedule. According to Matt Winick, the NBA sets the league schedule to accomplish both competitive balance and a reduction of costs. The goal of the NBA schedule, as it is constructed, is to be efficient from a competitive standpoint with an indirect consideration of travel costs.
Matt Winick has a complicated system that assigns a point value to each date or series of dates a team makes available. The point system rewards a team for making several consecutive dates available instead of insisting on a particular date. Each time team must amass at least 50 points. Generally, teams play 3.5 games in a week and those 82 games take roughly 165 days through the end of regular season.
In MIT Sloan Sports Analytics Conference 2016, Evan Wasch who is the Senior Vice President of Basketball Strategy & Analytics for the NBA has presented the most recent challenges and improvements in NBA scheduling.
Getting started in February, NBA schedule have usually been released in the first weeks of August.
Factors that have an impact on setting NBA schedule can be summarized as follows:
1. NBA SCHEDULING FORMULA
Each team have to play:
A five year rotation determines which out-of-division conference teams are played only 3 times.
2. COURT AVAILABILITY
All teams, about a month before the end of the preceding regular season, have to submit to the NBA office a list of:
3. OFFICIAL BREAKS (on which no games are played)
The conflicts such as NHL games on the same court have to be resolved.
Games can be moved to satisfy the NBA's TV partners (ABC, ESPN and TNT). Game times can be tweaked.
|Journal of Quantitative Analysis|
Journal of Quantitative Analysis in Sports is being edited by Ben Alamar who works for the Thunder as senior quantitative analyst.
|Logistic Regression Markov Chain (LRMC)|
LRMC (Logistic Regression Markov Chain) is a college basketball rankings system designed to use only basic scoreboard data, including which teams played, which team had home court advantage and the margin of victory.
|MIT Sloan Sports Analytics Conference|
This began a focus in sports business that was extended when Daryl Morey and Jessica Gelman founded the MIT Sloan Sports Analytics Conference in the winter of 2006.
The inaugural conference in 2007 was highlighted by keynote speakers JP Ricciardi and Jamie McCourt. Panel topics included baseball analytics featuring Bill James, sponsorship across all leagues, league management/expansion, and careers in sports.
The 2008 conference doubled in attendance as Wyc Grousbeck was the keynote speaker. Featured panels included Defending the Title, which included General Managers or Decision-makers from the then reigning champions of the four major sports leagues; Bill Polian (President, Indianapolis Colts), RC Buford (GM, San Antonio Spurs), Brian Burke (then-GM, Anaheim Ducks), and Jed Hoyer (Assistant GM, Boston Red Sox).
Building on continued success and growth, the 2009 conference transitioned to a featured panel format and again doubled in attendance. The first featured panel was Evolution of the Fan Experience, which was moderated by Bill Simmons and included Jeff Van Gundy and Brian Burke looking at how new technology, stadium design, game innovations, and customer initiatives are taking the fan experience to the next level. The second featured panel, Value of Icon Players, included Carla Christofferson and all-star guard Ray Allen discussing how to quantify the value icon players bring to a team or city. Other featured speakers included Adam Silver talking about the evolving value of new media and sports, Jonathan Kraft speaking on the globalization of sports, and Mark Cuban debating basketball analytics with some of the leagues top analytics users.
The 2010 conference was again a tremendous success, attracting over 1,000 attendees with another 400 on the waitlist. It also marked the first time the conference was held away from the MIT campus, moving to the Boston Convention & Exhibition Center. The feature panel was What Geeks Don’t Get: The Limits of Moneyball, and included Mark Cuban, Jonathan Kraft, Daryl Morey, Bill Polian, and Bill Simmons, and was moderated by Michael Lewis. The panel explored the decision making processes general managers and owners go through beyond the numbers. Beyond panel discussions, 2010 also saw the introduction of the research paper track, an extremely popular addition.
Watch the panels presented at MIT Sloan Sports Analytics Conference.
A research-driven approach that relies heavily on empirical analysis of player performance.
Oakland Athletics' general manager Billy Beane built successful baseball teams year after year, rather than relying on the gut instincts of old-time scouts, as was standard practice for decades. Writer Micheal Lewis realized another investment game was being played out in baseball, notably by the A's. He gained inside access to A's general manager Billy Beane and got a look at how Beane's value players differently than other teams. In 2003, he wrote a book called Moneyball: The Art of Winning an Unfair Game
The Oakland organization assesses offensive production differently than others, stressing on-base percentage and power, de-emphasizing stolen bases and putting the ball in play. It has engendered an approach to acquiring talent based as much on statistical achievement as on traditional tools, an approach that has gripped some franchises and galled many traditionalists.
|NBA Teams That Have Analytics Department|
Last updated on: May 12, 2016Contribute to this list by sending your tips / corrections to
The list includes the NBA teams using advanced stats by either employing basketball analytics professionals or working with statistical consultants.The NBA itself also hires
professionals; Evan Wasch who is the Senior Vice President of Basketball Strategy & Analytics and Jason Rosenfeld serving as the director of analytics.
Dan Rosenbaum (Analytics Consultant)
Mike Zarren (Assistant General Manager & Team Counsel), David Lewin (Director of Scouting), David Sparks (Director of Basketball Analytics), Drew Cannon (Analyst)
Glenn DuPaul (Director of Basketball Analytics), Rami Antoun (Basketball Analyst)
Steve Weinman (Basketball Operations and Analytics Manager), Miles Abbett (Basketball Analytics Coordinator)
Jon Nichols (Director of Analytics)
James Brocato (Director of Basketball Analytics), Chris Newell (Basketball Analytics & Strategy), Mondrick Jones (Player Analytics Manager)
Tommy Balcetis (Basketball Analytics Manager)
Kenneth Catanella (Assistant General Manager), Charles Klask (Statistics Coach), Zach Bradshow (Basketball Analytics Manager)
Kirk Lacob (Assistant General Manager), Samuel Gelfand (Coordinator of Basketball Analytics)
Daryl Morey (General Manager), Ed Kupfer (Consultant), Eli Witus (Basketball Operations Analyst), Monte McNair (Basketball Operations Analyst)
Ryan Renteria (Basketball Analytics Coordinator)
Yuju Lee (Director of Analytics), Aaron Danielson (Associate Director of Analytics)
John Hollinger (Vice President of Basketball Operations)
Bob Chaikin (Basketball Analyst)
Michael Clutterbuck (Director of Basketball Analytics)
Matt Bollero (Manager of Basketball Analytics, Personnel Video Scout)
Mike Smith (Director of Analytics and Pro Scout)
Jesse Weinstein-Gould (Basketball Information Coordinator)
Wynn Sullivan (Basketball Data Analyst)
Ben Falk (Vice President of Basketball Strategy), Lance Pearson (Coordinator of Coaching Analytics)
Steve Ilardi (Analytics Consultant), Eddie Kendralla (Manager, Business Analytics), Ken Borkan (Analytics Manager), Jake Loo (Senior Analytics Manager)
Jeff Ma (Consultant), Ryan Parker (Statistical Analyst)
Roland Beech (Head of Analytics)
Gabe Farkas (Director of Basketball Analytics), Kirk Goldsberry, (Basketball Analytics Coordinator) Logan MacPhail, (Manager of Coaching Analytics)
Alex Rucker (Consultant), Keith Boyarsky (Consultant)
Bob Bellotti (Consultant), Joe Sill (Consultant),
Ryan Saunders (Assistant Coach/Statistical Analysis)
Comments: Contribute to this list by sending your tips / corrections to
NBAstuffer.com does not guarantee the accuracy or timeliness of any information on this list.
Neural networks are one of the machine learning systems in sports. By the help of neural networks, datasets are learned by the system and hidden trends can be revealed for creating a competitive edge.
Simulations and machine learning systems means a lot for sports analytics. The ability to apply statistics and rigorous mathematical models to provide instantaneous results is quickly becoming an invaluable commodity. Systems of this type vary from simulations that model an entire upcoming season’s worth of data to identify the best chance of winning, to simulations that identify weaknesses in motion and offer advice for correcting them.
Other than statistical predictive algorithms, neural networks can be used as sports betting and fantasy league tools in following ways;
Other machine learning techniques include;
|New England Statistics Symposium NESSIS|
NESSIS is an event in which statisticians from all over the world come together and present academical studies. NBA and other sports analytics community has been benefiting from NESSIS that many studies on statistics regarding professional sports are being introduced first time at NESSIS just like MIT Sloan Sports Analytics Conference.
The first New England Statistics Symposium was held at the University of Connecticut, which traditionally hosts the Symposium on alternate years. Now, The Department of Statistics of Harvard University hosts the New England Statistics Symposium.
|Play by Play|
Historical play-by-play data sets are available for download in CSV format. Data sets include a separate CSV file for each game, and a season file where all games combined in a CSV file.
Traditional box score shows per-game totals for players and for teams and reveals only a fraction of what happens in a game and that the information therein is often misleading, especially at defensive stats. At this point, Play-by-Play (PBP) data has been the main source of many advanced stats such as adjusted plus-minus.
Play-by-play provides a transcript of the game in a format of individual events.
Play-by-play data is being tracked since the 1996-1997 season.
|Pro Basketball Forecast|
With the Pro Basketball Forecast, John Hollinger takes an in-depth and insightful look at the game. Downplayed are all the per-game statistics; in their place are points, rebounds, and assists per forty minutes. Hollinger also examines how many possessions each player uses and what percentage of his team's rebounds he collects.
|Regression To The Mean|
Regression to the mean refers to the fact that those with extreme scores on any measure at one point in time will, for purely statistical reasons, probably have less extreme scores the next time they are tested. Scores always involve a little bit of luck. Real situations fall between these two extremes: scores are a combination of skill and luck.
How it applies to basketball statistics is, any athlete who posts a significant outlier, whether as a rookie or particularly after their prime years can be expected to perform more in line with their established standards of performance.
SportVu Player Tracking Stats are made available at NBA.com by the 2013-2014 NBA season
SportVU is an automated ID and tracking technology that has the ability to collect positioning data of the ball, players and referees during a game. SportVu, which is founded in Israel in 2005, was acquired by STATS LLC in 2008 December.
SportVU tells us defensive and offensive alignment relative to ball location, shot trajectory and the number of dribbles and passes made by a team and player.
Players, referees and the ball with an exact location in x,y coordinates are tracked. 25 times a second, software analyzes the video, and stores information about where everyone is and what is occurring. 1,000,000 entries per game is added to databases. Without having to chart and track a game by hand and eye, player position and defender proximity to a player are now available. In another words, defense is more quantifiable with what SportVu brings to the table. Here's an article on how the Raptors use SportVu technology.
In the near future, Sportvu could allow coaches to make real-time in-game adjustments to their play calling and could allow the broadcasters to use the same graphics and analysis that now is available only after the game. NBA also plans to automate some of the calls now made by human referees and scorekeepers
* The first five teams to have SportVu cameras are Dallas, Houston, Oklahoma City, San Antonio and Golden State.
SAMPLE SPORTVU VIDEOS
StatsCube is a new data warehouse tool which the NBA has been developing over the last few years.
StatsCube has in it every point, rebound, assist, steal, block, turnover, missed shot, foul and substitution since the 1996-97 season, when play-by-play data first started being tracked courtside. The point in the game when each occurred, and what players were on the floor at the time, is recorded. Best of all, StatsCube can slice and dice the data so teams can analyze it instantaneously.
Say a team's power forward grabs only X rebounds per minute. Is the team better when he's on the floor? What players shoot the best in the last three minutes of a close game? Does a center block as many shots after he's picked up his fourth foul as he does when he has fewer than four?
What's Next: During Game 4 of the 2009 Finals, the NBA did a demo of a system in development that tracks player and ball movement through the use of six HD cameras placed around the arena. This technology, called SportVu, gives the precise location of every one on the floor so that the play that is run, all of the the ball touches and the positioning for anybody on the court in terms of x,y coordinates is a matter of few clicks. Even the trajectory of the ball as it went to the basket will be available.
|The Hot Hand Myth|
The "hot hand" describes the belief that the performance of a player, temporarily improves following a string of successes.
Players, coaches, commentators and fans believe in streaky shooting, but the academic studies against this conventional wisdom suggest that there is no player with the hot hand. Analysis through play-by-play data strongly considers streaks of made shots are some sort of natural variation.
Making 10 consecutive shots does not prove that a player is hot. NBA players tend to become significantly overconfident after making consecutive shots. After making one shot, a player's shooting percentage actually drops for the next field goal attempt. As if the player and his teammates believed him to be the team's best scoring option. Behaving as though the hot hand existed might actually be detrimental and cost an average team about four victories over one season!
|The Price of Anarchy|
Author: Brian Skinner
It’s a suboptimal arrangement used in "traffic networks" that can be applied to Basketball as well.
ESPN.com sportswriter Bill Simmons calles it the “Ewing Theory”. The idea that a team could improve after losing one of its best players may in fact have a network-based justiﬁcation, and not just a psychological one.
Optimizing the performance of a basketball offense may be viewed as a network problem, wherein each play represents a "pathway" through which the ball and players may move from origin (the in-bounds pass) to goal (the basket). Effective field goal percentages from the resulting shot attempts can be used to characterize the efficiency of each pathway. The analysis suggests that there may be a significant difference between taking the highest-percentage shot each time down the court and playing the most efficient possible game. There may also be an analogue of Braess's Paradox in basketball, such that removing a key player from a team can result in the improvement of the team's offensive efficiency.