Partnow emphasizes that the field of basketball analytics doesn't aim to reduce the game to cold, hard numbers, but instead uses data and measurement to gain a more profound understanding of the complexities and nuances driving player and team performance. It involves inquiring into the right topics to inform strategic decisions and assembling the roster. He advocates for a mindset of continuous curiosity, grounded in a probabilistic approach to the game's inherent randomness.
This section dives into the primary aims of basketball analytics and explores the evolution of analytical tools used by the NBA.
Partnow argues that the primary purpose of using basketball analytics is to contextualize player and team performance, going beyond simple box score statistics to illuminate the intricate interplay of skills, tactics, and situations. It's about identifying not just what happened – who scored, grabbed a rebound, or dished an assist – but how and the reasons behind it.
Analytics helps identify strengths and weaknesses, both on a team and individual level, giving coaches and front offices the information to understand the areas in which a player or team excels or struggles. This in-depth analysis then informs strategy and staffing choices. As Partnow puts it, analytics helps formulate the appropriate inquiries, positioning them to make choices that improve their odds of being successful. It's not about solving basketball, but about navigating the game's randomness with a more informed perspective.
Practical Tips
- Start a hobbyist sports blog where you apply analytical thinking to your favorite team or sport. Write articles that go beyond traditional statistics, using publicly available data to offer deeper insights. This practice will sharpen your analytical skills and could attract a readership interested in nuanced sports analysis.
- Use video recordings to analyze your performance in a hobby or skill you're trying to improve. Just as basketball players review game footage to understand their skills and tactics, you can record yourself practicing a musical instrument, public speaking, or even cooking. Watch the footage to identify areas of strength and weakness, then adjust your practice accordingly.
- Set up a weekly reflection session where you review your dashboard metrics and set specific goals for the upcoming week. This practice encourages you to take actionable steps based on your analytics. If you see that you've been particularly good at networking, you might set a goal to attend one extra professional event next week to leverage this strength.
- Use a fitness app that incorporates analytics to optimize your workout routine. Choose an app that allows you to input your exercise data, such as reps, sets, and weights lifted, or tracks your running routes, pace, and elevation. Analyze the data weekly to adjust your workouts for better results. If you notice that your running pace slows down after a certain distance, you might focus on increasing your endurance through longer, slower runs.
- You can leverage free analytics tools to make informed decisions about your personal projects. Start by using Google Analytics for any blogs or websites you manage to understand visitor behavior and adjust your content strategy accordingly. For example, if you notice a high bounce rate on a particular page, consider revising the content or layout to better engage your audience.
- Engage with a local basketball coach or experienced player for a one-on-one session to discuss the application of analytics in improving your game. Prepare specific questions or scenarios where you're uncertain about the best course of action. Use the insights gained to adjust your training regimen, such as incorporating drills that simulate high-pressure situations if you learn that your performance dips during critical game moments.
- Engage in scenario planning for major life decisions. When facing a significant choice, like moving to a new city or changing jobs, outline several possible scenarios and the decisions you might make in each....
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Partnow dives into the strengths and limitations of advanced metrics, particularly RAPM, and explores the challenges of statistically assessing defense.
This section focuses on APM (Adjusted Plus/Minus) and their successor RAPM (Regularized Adjusted Plus/Minus) in attempting to evaluate players holistically.
As discussed in Chapter 6, RAPM is a statistically rigorous method to estimate how a player affects a team's scoring margin above or below what would be expected given their teammates and opponents, while also accounting for the home court advantage. This metric can identify impactful players, but as Partnow highlights, it struggles to explain why an athlete is impactful. RAPM outputs shouldn't be considered some kind of objective "best player" measurement. Rather, the estimates produced offer a practical contextual measure of how effectively an athlete has performed in the role given to them by their team.
Other Perspectives
- The model could be influenced by biases in the data collection process or the...
Partnow explores how the regulations surrounding the NBA salary cap, as well as the evolution of the game itself, have shaped the approaches teams take to building championship caliber rosters.
This section dives into the complexities of building a contending NBA roster within player compensation limits while pursuing the scarce resource of “supermax”-level talent.
Partnow cautions teams against falling into the “Bird Rights Trap,” which can lock them into mediocre rosters comprised of overpaid, average players. Because teams can exceed the salary cap in order to re-sign their own free agents, they are often compelled to do so due to the inability to easily replace those players if they left. Partnow argues that this can lead to cycles of mediocrity, with teams stuck in the "adequate but insufficient" zone, with little chance to improve because their limited resources are already allocated to athletes who aren’t actually worth it.
Practical Tips
- Create a "loyalty...
The Midrange Theory
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