Basketball’s best-kept secret is the manner in which it exposes the ways humans think analytically; the fluidity of its events and “optimal” thinking handle abstract concepts which are prone to reduction. Namely, during a Knowledge Revolution in the midst of exponential growth, the lack of agreement on what constitutes “knowledge” has complicated the acceptance and interpretations of new data. Thusly, the main conflict of this Revolution stems from a perceived diametric relationship between basketball’s two most prominent sources of knowledge: film and stats, visual and numerical reference points, respectively. The objectively illogical approaches to characterizing basketball knowledge prompted me to approximate the bases for “Basketball Epistemology,” or the theories of knowledge in basketball.

NB: Theories of knowledge are based on the optimization of certain goals, with the focus of this article being the comprehensive, scientific analysis of “things” that happen on the court (referred to as “events”) and the resulting descriptive and predictive powers which assist in on-court decision-making.

I. Philosophical Analysis

The foundation of philosophical analysis in basketball relates to system thinking, the unique interactions of “smaller” systems (e.g. lineup combinations, head coaching and assistant coaching) and how they interact to form “larger” systems (e.g. teams). The nature of basketball makes it so that knowledge relating to events is always conditional, meaning systems are not the products of the sums of their parts, but the products of the interactions of their parts. Given the “goal,” such a foundation would be entirely relevant in player forecastings in which executives will interpret conditional information within the context of a separate system.

There also exists a definitive absence of justified belief which stems from the intersystemic traits of optimization, meaning there is also an absence of conclusion to intersystemic problems. Similar “abstract” problems can be thought of as any questions which pose answers outside the scopes of qualitative or quantitative measurements, not to be confused with estimates. Examples could form as such:

• Which player scored the most points during a time frame? (Non-abstract)
• Which player was the best scorer during a time frame? (Abstract)
• Which team managed the highest point differential during a time frame? (Non-abstract)
• Which team had the best defense during a time frame? (Abstract)

Namely, the inferences drawn from intersystemic analysis, regardless of confidence level, are definitively opinionated. 

II. Sources of Knowledge

“Numerical” and “visual” tools have been named the primary sources of knowledge under these branches, and both come with caveats and signs of caution when interpreting within the context of knowledge. Numerical and visual tools are interrelated and express events in different ways. Let’s start with visual tools, which typically involve an active viewing of a basketball game to identify patterns and trends to then be interpreted within the context of a goal. But unlike a crude, numerical value, visual tools have an incomprehensible scope in which all on-court events are visible to the viewer. But there are inferential limitations: memory and registering.

Memorizing events which span a significant number of games (hundreds or more?) which characterize five players, five opponents, their actions, movements, body language, speech, coaching, coaching decisions, player decisions, communication between systems, among more… is evidently a fruitless endeavour, and any claim otherwise would be a gross overestimation of human cognition. Therefore, while visual tools are direct representation of reality, the ability to memorize and then register (i.e. interpret optimally within the context of a goal) all events does not exist.

Variability exists among visual tools as similarly-labeled problems exist among numerical tools. Namely, how one viewer perceives the same film will vary from how another perceives the same film based on their memory and processing skills. (Note-keeping can be a valuable tool in such scenarios.) The result is that observations do not qualify as data outside of its scope (e.g. establishing an abstract or inconclusive cause-and-effect relationship based on an observation) regardless of confidence level. Examples could form as follows:

• Draymond Green sets a ball screen for Stephen Curry around the three-point line. (Data)
• Draymond Green setting a ball screen for Stephen Curry around the three-point line was a continuation of the Warriors’ patterned playbook. (Not data)

Antithetically, it’s popular saying that “numbers don’t lie,” but such a proposition encounters similar problems. Numerical tools are similarly plagued with scopes and thusly can only qualify as data within the context of its scope. Examples could form as follows:

• Stephen Curry led the NBA in points per possession and True Shooting percentage (as calculated by Basketball-Reference) in the 2015-16 regular season. (Data)
• Stephen Curry leading the NBA in points per possession and True Shooting percentage in the 2015-16 regular season means he was the NBA’s best scorer during that period. (Not data)

The overlap in the examples pertaining to visual and numerical tools is how the observations are interpreted within the contexts of their finite scopes, meaning a judgment ascribed to the observation does not qualify as data but rather an opinion, regardless of confidence. As a result, these numerical and visual tools are unreasonable tools to answer questions outside of their scopes. The theme of such connectivity carries over to the relationships between numerical and visual tools.

As stated earlier, the tools are interrelated and are different mediums which express events. There are direct measurable relationships and abstract inferential relationships between these numerical and visual mediums. Namely, take the examples as follows:

• Kobe Bryant swishes a two-point jump shot while crossing his legs with three defenders in his immediate vicinity.
• Luke Kennard is intentionally fouled at the end of a regulation period and banks two free-throw attempts.

The points column in the box score only registers that both Bryant and Kennard scored two points in those instances, which characterize the direct measurable relationship as both numerical values can be traced back to equal measurements observed visually. However, the inferential relationship between the tools which concerns abstract questions outside the tools’ scopes is not direct. This is another way of saying: “Not all points are created equally.” The principle applies to virtually every instance of cross-referencing between tools, as the contexts in which events occur will seldom possess significant overlap.

III. Philosophical Skepticism

The presence of abstract problem-solving relates to how sources of knowledge and questions deemed optimal disqualify the answers as knowledge. Therefore, skepticism is a natural byproduct in the classification of knowledge and the resulting judgments. The aforementioned example of player forecasting which estimates a player’s intersystemic qualities contains a hypothetical component in which the evaluator must estimate the transition based on conditional information. If the overarching question is posed along the lines of:

• How does Player A in System A raise the point differential in System B in the following season?
• How does knowledge of Player C in system C against opponent defense D imply changes in knowledge of Player C if he had played against opponent D in System E?

The resulting abstractness poses threats to the concept of intersystemic knowledge and corroborates the deduction that contrary claims would rely on judgment. Namely, the skepticism of intersystemic knowledge serves to interweave the abstract nature of optimal problem-solving, the finite scopes of perceived sources of knowledge, and the abstract inferential relationships between tools. (Epoché)

Listen (or watch!) to the companion piece to this post on YouTube!