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The use of analytics in basketball, also known as Moneyball, has become increasingly prominent in the sport. The exact origins of basketball analytics are difficult to pinpoint, but it is known that basic statistical data such as points, assists, and rebounds have been collected since the sport's inception in 1891. Over time, the collection and analysis of data have become more sophisticated, with the development of advanced metrics and technologies such as SportVU, which tracks player and ball movements during games. The publication of Michael Lewis's book Moneyball in 2003 and the subsequent release of the film adaptation in 2011 further fueled the analytics movement in basketball, as teams sought to emulate the success of the Oakland A's in using data to inform their strategies and player evaluations. Today, analytics plays a significant role in basketball, influencing decisions made by coaches, players, and management, and shaping the way the game is played.
| Characteristics | Values |
|---|---|
| Analytics in basketball also known as | Moneyball |
| The old school method of scouts measuring talent | Eye-test |
| The first book on basketball analytics | "Defensive Basketball" by UNC coach Frank McGuire, published in 1959 |
| The first important modern basketball analytics book | "Basketball on Paper" by Dean Oliver, published in 2004 |
| The first full-time statistical analyst in the NBA | Dean Oliver, hired in 2004 |
| The Sloan Conference founder | Daryl Morey |
| The NBA's first exclusive rights to use AutoStats data | Orlando Magic, since February 2019 |
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What You'll Learn
The early days of basketball analytics
The exact origins of basketball analytics are difficult to pinpoint, but the sport has a long history of collecting basic statistical data. As far back as the 1950s, UNC coach Frank McGuire published a book, "Defensive Basketball" (1959), which discussed the importance of evaluating team offences and defences based on per-possession stats, not just per game.
In the early days of the internet in the 1990s, Dean Oliver and John Hollinger were doing some early "modern" analytics work and discussing it on their websites. Bob Belloti's "Points Created" metric from 1988 may have influenced them. In the early 2000s, Oliver published "Basketball on Paper" (2004), considered the first important modern basketball analytics book. It introduced the Four Factors: shooting efficiency, rebounding percentage, turnovers per possession, and getting to the line. The same year, Oliver became the first full-time statistical analyst in the NBA.
In 2003, Roland Beech started 82games.com, which used newly available play-by-play data to display and evaluate plus/minus and lineup stats. This period, from 2003 to 2008, is when modern awareness of analytics began for most curious stat enthusiasts, likely influenced by the growing field of sabermetrics in baseball.
In 2004, Daniel Rosenbaum introduced the concept of Adjusted Plus/Minus, which examined how to isolate a player's influence on a team's plus/minus data. Also in 2004, Justin Kubatko, the founder of Basketball-Reference, shared his Win Shares metric, which is still one of the most commonly used advanced stats.
In 2005, John Hollinger was hired by ESPN, bringing his Player Efficiency Rating (PER) stat to a wider audience. By 2006, Daryl Morey started the Sloan Conference (MIT Sloan Sports Analytics Conference), and was named the Rockets' Assistant GM. He would later become a key figure in the analytics movement as the general manager of the Houston Rockets.
In 2007, Oliver, Kubatko, Rosenbaum, and Kevin Pelton published the research paper, "A Starting Point for Analyzing Basketball Statistics", to help standardize and speed up basketball analytics research. This period, from 2009 to 2013, is when "normal" fans became more aware and accepting of analytics, and early-adopting stat enthusiasts began to collaborate and interact more.
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The influence of Moneyball
Moneyball, or sabermetrics, is a data-driven approach to player and team evaluation, using advanced statistics to identify efficient players and market inefficiencies. The term was popularised by Michael Lewis in his 2003 book Moneyball: The Art of Winning an Unfair Game, which detailed the Oakland Athletics' use of analytics to build a successful team despite financial constraints.
The book highlighted how Billy Beane, Oakland's general manager, valued players differently from other teams. Beane stressed offensive efficiency, emphasising on-base percentage and power while de-emphasising stolen bases. This approach to player acquisition, based on statistical achievement as much as traditional tools, has since influenced basketball, with the NBA embracing advanced analytics as the Moneyball movement swept through professional basketball in 2013.
The core idea of Moneyball in basketball is to gain a better understanding of what wins games. This involves reworking and revaluing historical statistics to more accurately describe a player's value and impact on the team. For example, in basketball, it is difficult to attribute occurrences solely to individual players as any recorded statistics are influenced by all ten players on the court to varying degrees. Advanced analytics can help identify players who may have been overlooked using traditional evaluation methods.
The use of player tracking technology, such as cameras installed above NBA courts since 2013, provides valuable data on player movements and the ball's location. This allows teams to assess the value of certain actions, such as a good screen or box out, and determine the impact of individual players within a team context.
While some critics argue that the Oakland Athletics never won a title using Moneyball, several baseball teams have flourished after adopting sabermetrics, and it has become an essential tool for coaches, general managers, scouts, and player personnel directors in making informed decisions.
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The role of SportVU data
SportVU is an automated identification and tracking technology that collects positioning data of the ball, players, and referees during a basketball game. It was founded in Israel in 2005 and was acquired by STATS LLC in 2008. SportVU uses cameras to collect data 25 times per second, tracking the exact locations of players, referees, and the ball in x,y coordinates. This data is then analyzed by software and stored, providing information on player and ball positioning, shot trajectory, and the number of dribbles and passes made by a team and player.
SportVU data has played a significant role in basketball analytics, providing detailed insights that were previously unavailable. This data is used to quantify defensive plays, such as identifying players who draw the defense by attracting multiple defenders, and measuring on-ball defense to see how often a defender successfully prevents their opponent from scoring. SportVU also enables visualizations, such as illustrating which players control specific parts of the court, providing a more comprehensive understanding of court coverage and spacing.
The availability of SportVU data has empowered basketball analysts, statisticians, and data scientists to employ machine learning techniques and develop innovative research. This has led to advancements in predicting shot outcomes, evaluating decision-making, and analyzing player and team performance. The data has also influenced decision-making in the NBA, with teams utilizing these insights for player development and strategic planning.
SportVU was first introduced in the NBA during the 2010-2011 season, with four teams contracted to use the technology. By the 2013-2014 season, all 30 teams had installed SportVU cameras, and the NBA began featuring SportVU statistics on their platforms and in arenas across the country. This widespread adoption marked a significant step forward in the basketball analytics movement, providing a wealth of data for fans, analysts, and teams to utilize and driving a shift towards data-driven decision-making in the sport.
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Player value metrics
The history of basketball analytics can be traced back to the 1950s, with UNC coach Frank McGuire's 1959 book "Defensive Basketball", which discussed the importance of evaluating team offences and defences based on per-possession stats. However, the early days of modern basketball analytics are often associated with the work of Dean Oliver and John Hollinger in the 1990s and the early 2000s.
Oliver, the author of "Basketball on Paper" and former director of quantitative analysis for the Denver Nuggets, played a pivotal role in advancing basketball analytics. His work focused on player valuation metrics, ranking players from best to worst, and introduced the concept of the "Four Factors": shooting efficiency, rebounding percentage, turnovers per possession, and getting to the line. This shifted the emphasis towards shooting and defending layups and 3-point shots, which have the highest effective field goal percentage.
Another notable figure in basketball analytics is Michael Lewis, who wrote "Moneyball" in 2003. The book highlighted the Oakland A's, a low-budget MLB franchise that utilised analytics to maintain success despite financial constraints. This approach, emphasising statistical achievement alongside traditional tools, has influenced basketball franchises and challenged traditional scouting methods.
The evolution of basketball analytics has led to the development of various player value metrics, such as:
- Statistical Player Value (SPV): This metric quantifies a player's accomplishments by converting stats like minutes, points, rebounds, assists, steals, blocks, and turnovers into points, providing a cohesive measure to compare players with different skill sets.
- Adjusted Plus/Minus (APM): APM isolates a player's influence on their team's plus/minus data, helping to understand their impact on the team's performance.
- Box Plus-Minus (BPM): While details of BPM are scarce, it is presumably a variation of APM, focusing on box score stats.
- Offensive Bayesian Performance Rating (OBPR): OBPR reflects a player's offensive value, incorporating individual efficiency stats, play-by-play impact, and accounting for the strengths of teammates and opponents on the floor.
- Team Efficiency Metrics: These metrics evaluate the difference in team offensive and defensive efficiency with certain players on the court, adjusted for opponent quality.
These player value metrics provide a more nuanced understanding of a player's contributions beyond traditional box scores, influencing decision-making on and off the court. They offer insights into a player's impact on their team's performance, offensive and defensive capabilities, and overall value.
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The future of basketball analytics
The use of analytics in basketball has grown significantly in recent years, with the NBA leading the way in the adoption of advanced statistical tools to optimise performance, strategy, and player development. The future of basketball will see a continued integration of technology and data, transforming how teams play and how fans experience the game.
One area that is expected to evolve is the use of data in player development and scouting. In the past, scouts relied heavily on subjective assessments when evaluating young talent. Now, teams use a more data-driven approach, combining traditional scouting methods with advanced statistical analysis. This helps identify undervalued players who might have been overlooked in the past. For example, players who excel in defensive efficiency or shot creation can now be recognised and drafted earlier.
The collection and analysis of granular data on players' movements will also continue to play a significant role in the future of basketball analytics. This data, collected through high-tech cameras installed in basketball courts, is analysed by machine learning models to recommend winning strategies. By understanding players' movements and "tells", coaches can better plan defensive strategies, decide on player matchups, and recommend offensive moves.
Additionally, the use of analytics will continue to shape the fan experience. With the rise of big data, fans will have access to more information, leading to increased engagement and entertainment. This could include the expansion of sports betting, providing fans with more opportunities to interact with the game and their favourite teams and players.
While the integration of analytics in basketball has brought a new level of excitement and innovation, there are also challenges to be aware of. One challenge is maintaining the "'fun'" and "unpredictability" of the game. Basketball is a game of suspense and unexpected moves, and over-reliance on data models can lead to repetitive and boring gameplay. Another challenge is considering the human element, such as player psychology and team dynamics, which are currently missing from data models.
In conclusion, the future of basketball analytics holds great potential for the sport. The continued integration of technology and data will shape how teams play, scout, and develop players, as well as how fans experience the game. However, it is important to strike a balance between the use of analytics and the traditional aspects of the game to ensure it remains exciting and engaging for all.
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Frequently asked questions
The use of analytics in basketball can be traced back to the 1950s, but the modern analytics movement in basketball began in the early 2000s.
Some of the pioneers of basketball analytics include Dean Oliver, author of "Basketball on Paper", John Hollinger, and Daryl Morey, who founded the Sloan Conference in 2006.
Basketball analytics have evolved from basic statistical data collection to advanced metrics and data tracking using technology such as SportVU and AI.
Analytics provide valuable insights for coaches and teams, helping them make informed decisions about game strategies, player evaluations, and recruitment. Analytics can also be used to compare players and predict the success of draftees.
