Seth Stephens-Davidowitz — Who Makes the NBA? (EP.250)
Jan 9, 2025
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Seth Stephens-Davidowitz, a data scientist and bestselling author, uses data to challenge our understanding of the NBA. He delves into why many players share the name Chris and the impact of height on NBA opportunities. The conversation touches on the potential for a Moneyball moment in basketball and critiques the glamorization of the rags-to-riches narrative. Additionally, he discusses how tools like Code Interpreter revolutionized his writing process, allowing him to complete his latest book in just 30 days!
Seth Stephens-Davidowitz emphasizes the transformative power of data analysis, illustrating how advanced tools can enhance creativity in research and writing.
The podcast reveals surprising correlations in the NBA, notably that height significantly increases a player's chances, though not all tall players excel athletically.
It challenges traditional rags-to-riches narratives by demonstrating that socioeconomic factors, such as wealth and family stability, play a crucial role in NBA success.
Deep dives
Understanding Infinite Loops in Thinking
The concept of infinite loops in thinking refers to the cyclical nature of our thought processes, especially when analyzing complex issues such as market fluctuations. In the podcast, the necessity of developing a multifaceted approach to understanding various problems is emphasized, drawing from disciplines like history, philosophy, and quantitative analysis. By broadening the lens through which we view problems, individuals can gain fresh perspectives that challenge existing paradigms. This approach not only aids in becoming better investors but also cultivates becoming more nuanced thinkers about the world.
The Power of Data in Unlocking Insights
Data is portrayed as a transformative tool that can reveal insights beyond our intuitions and assumptions. The conversation highlights how advanced tools like Code Interpreter can significantly streamline the data analysis process, enabling rapid comparisons and conclusions that would typically require months of work. For example, data scientists can automate mundane tasks, freeing them to focus on generating creative ideas and hypotheses. This efficiency can lead to profound discoveries, changing how one approaches both research and writing.
Exploring Height Correlations in the NBA
The discussion dives into the surprising correlation between height and the likelihood of making it to the NBA, establishing that each additional inch of height roughly doubles one's chances of becoming a player. The analysis reveals that while height provides a competitive advantage, not all tall players exhibit exceptional athleticism. This pattern indicates that players who are exceptionally tall may not need extraordinary talents to succeed at the highest levels, prompting the creation of a new metric called the 'Muggsy's stat' to better assess player effectiveness irrespective of their height. Such insights challenge conventional views and highlight how data can unravel unexpected relationships within sports.
Reevaluating Stereotypes About Player Backgrounds
Counteracting popular narratives, the podcast reveals that wealth and stable family structures significantly correlate with a higher likelihood of NBA success. Rather than being shaped solely by hardship, players from affluent backgrounds frequently outperform their less fortunate counterparts. A particular insight pertains to the names of players, indicating that those with common names are more likely to hail from stable and middle-class environments. This data shifts traditional views about the backgrounds of successful athletes, emphasizing the importance of socioeconomic factors over dramatic rags-to-riches stories.
Big Data vs. Small Data in Decision Making
The importance of large datasets over smaller samples in deriving accurate conclusions is heavily emphasized, especially in the context of decision-making. The conversation illustrates how small data can yield misleading results, as seen in the example of unusual outcomes in smaller hospitals versus larger ones. Being able to analyze vast amounts of data facilitates the discovery of nuanced patterns that drive more informed choices, whether in healthcare or sports analytics. This understanding challenges individuals to recognize the limits of their experiences and to seek broader data sources to inform their decisions effectively.
Seth Stephens-Davidowitz, a data scientist and bestselling author, is known for his brilliant use of data to upend conventional wisdom - often with humorous, surprising, and occasionally shocking results. His latest book, Who Makes the NBA, uses data to interrogate some of basketball’s biggest questions, consistently yielding unexpected insights. Here’s the kicker - he wrote the entire book in just 30 days after discovering Code Interpreter.
Unsurprisingly for a former quant, I had a blast chatting to Seth. Topics discussed include why so many NBA players are called Chris, whether basketball is due for a Moneyball moment, and why so many of us misunderstand the rags-to-riches story.
I hope you enjoy this conversation as much as I did. For the full transcript, episode takeaways, and bucketloads of other goodies designed to make you go, “Hmm, that’s interesting!”, check out our Substack.