Category: Theory

  • Basketball Epistemology

    Basketball Epistemology

    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!

  • My Tentative Process of Ranking NBA Players

    Ranking players, especially in the widespread arbitrary sense by which most instances occur, essentially has no practical value. But that’s because “player ranking” is often treated as a self-contained thing that looks inward of the result, disregards the implications value-systems have on the processes of team-building and assigning market values. Therefore, while the “result” (a list) of player ranking doesn’t matter for any reason which concerns the on-court product of a basketball game, it serves as an infamous source of entertainment value. Player ranking allows people on the internet to gain or lose their self-esteem vicariously through the quality of their basketball opinions, and thus the human instinct makes the performances all the more memorable.

    The “Non-Ideal” Theory of Player Ranking

    Basketball does not occur in a vacuum, nor should it as the product of systems within systems. However, the systems of the game impose more boundaries; if the ideal benchmark of a player’s value is his “intersystemic” efficacy, that is what he provides across a variety of systems, there is little room for an intelligible process. Thus, ranking players in this fashion involves to some degree the need to play god, to transpose instances upon others with limited bites of data. Namely, our ideas concern the “non-ideal” axioms we may invoke to provide rigidity in the process, to avoid incoherence. Perhaps there is meaning in working with such limited measuring sticks, encouraging collaboration and the expansion of our worldviews. So, in this post, let us set up a version of a player ranking process that emphasizes a player’s intersystemic value.

    The actions of players (parts), in conjunction with decision-making from non-player members of an NBA franchise, positively affect the team (system) by contributing toward the underlying mechanism that wins basketball games: scoring as many points as possible on offense and saving as many points as possible on defense within the time/space constraints of a typical game. The intrinsic difficulty in untangling the process of possessions stems from the degree to which actions are intertwined and indistinguishable among parts, meaning to continue with the task requires an observable number of finite dimensions in which decisions and the ensuing actions occur. From such emerges the models of possessions and practical applications of playbooks, which exist as sets of premeditated actions that describe patterns in players’ actions and their interactions with other players. (Major signs of caution are advised to remain aware of whether or not we censor certain information.)

    To estimate the manners in which players contribute to the process of possessions by proxy of his impact on a finite number of models of possessions, we employ a bottom-up approach that evaluates the consistency and efficacy of a player’s actions (in most cases, “skills”) based on varieties of qualitative and quantitative data and data points. Those initial “player profiles” which are intrinsically bound by their intrasystemic natures are then transposed onto intersystemic principles that similarly evaluate changes in consistency and efficacy, which is achieved through generalized pattern recognition of 1) how players of similar profiles tend to change through systems and 2) how varieties of teammates typically change based on their tendencies. The “end result,” the data point estimation which sorts the rankings, is a proxy for a player’s intersystemic value by estimation of how he fuels the successes and failures of possible systems.

    Knowledge Through Impartiality

    Film study is the most important part of our process, the fundamental “visual” tool which is falsely contrasted with analytics or statistics, the “numerical” tools. The visual aspect takes precedence because of the degree to which it constrains our interpretations of its data; statistics are represented on a far more rigid surface than are observations from tape, which can extend past the crude data point to qualitative analysis. We can observe the minutiae of what constitutes, for example, a play type on  A “post up” is a generalization, a short-hand with which inferences can be made quicker, but not necessarily more effectively. This is why the process requires diligence, a hyperactive form of analysis that trades off between pitfalls and follows the route which will (hopefully) lead us to the “best” possible decision.

    Pushing back against generalization is a broader theme in film study. When we search for something, the other things are filtered out in what we may ascribe to noise. But the censoring of information is not necessarily the most desirable course. Remember, we’re looking to emulate the bottom-up approach of how parts interact within systems, so to flow with the process organically will broaden our worldview of what considers contributions and what doesn’t. The resulting observations about interactions and synergies, which are selected to cover wide areas of possible circumstances, are condensed into “tendencies” by which players impact systems.

    Statistics aren’t omitted from the process and exemplify a trade-off between bias and variance (analogous to forms of regression modeling) shared with film. Statistics are shorthands that account for a player’s entire time on the court during a given season, Playoffs, career, et cetera, but the tools are biased toward the measurements that are decided upon. Meanwhile, film has the potential for the reduction of bias based on the viewer, but the length of seasons and typical thresholds that decrease the variance of observations would presumably require an inhuman amount of time and energy to overcome. Not all statistics are “good,” as has been proven many times. How many points a player scores per 75 possessions or his relative True Shooting percentage likely isn’t that “important” in this process, especially as self-contained objects. For this process, the most “important” statistics are “tracking” (non-traditional, non-box counting) stats and lineup stats, for their abilities to shed light on tendencies which may be less prone to variation among systems and synergies among parts (WOWY, assist networks).

    While on the topic of analytics, there surely must be some mention of “impact” (composite, one-number) metrics! Without them, we’d have virtually no idea the degree to which a player can impact the game outside of an arbitrary, dissonant mental estimate. Though it is important to continually be mindful of their weak spots and how certain modeling techniques may capture one player’s intrasystemic value fairly well, but not another. These are ideally the concluding steps in the process, a crude benchmark that offers strong, rigid methods with which we can connect the actions a player performs with the underlying “impact” on the successes and failures of the systems. 

    The Interpretation of Player Rankings

    By “ranking” players and devising lists, the purpose is not to create a perfect representation of reality or estimate within some strict interval the degree to which the process produces plausible results. Player rankings are not intended to be a reflection of how one interprets the process of possessions (the higher-dimensional, purely intersystemic basketball), but rather the entertainment-based alternate process by which one can estimate such a reality with a finite number of parameters, all of which are prone to human error, misinterpretation, and reduction. Ranking players is a social experiment, so let us treat it as such!

  • The Consequences of “Knowing” Individual Scoring

    “All things appear and disappear because of the concurrence of causes and conditions. Nothing ever exists entirely alone; everything is in relation to everything else.”

    Basketball is not the study of individuals, but rather the study of the interactions among parts which form wholes. The conditions of the sport make it so, repressing individuality, providing one-dimensional views of the ways in which parts adapt to and interact in systems. This leaves us, as evaluators of basketball, in a constant state of Epoché whose curtains deflect approximations of intersystemic truth, guided by logic and pattern recognition. But those mimicries of knowledge emphasize the ultimate pitfalls of intersystemic thinking: perceiving data for one thing and allowing the underlying motivations to narrow the descriptive power, the resulting knowledge.

    Possessions as a Process

    Scoring is perennially misrepresented as an individual skill, a sound heuristic on which to form judgments and construct an individual by his abilities. But this, of course, assumes that the priorities of the evaluator are in line with understanding the processes by which systems produce results, by which systems succeed or fail. To entertain the attribution of scoring to “putting the ball in the basket” in such a context would be a blasphemous reduction of the self-imposed heuristic. The process produces the results, but the latter does not describe the former, merely functions as a false indicator by other self-imposed heuristics.

    Points ascribed to individuals are the pyrite in the muddy solution to the complex question, one which has already been reduced to fit into the narrowing worldview that seeks knowledge. They encourage the interpretation of the result as the whole fruit rather than its outermost layer which conceals the seeds which had been planted to instigate the process. Measures have been derived from points as data points for players to attempt context, yet still ignore the underlying functions of the process, namely, a “shot quality” metric. Such presentation may encourage the idea of scoring as a measure of points relative to expectation, which remains a result-oriented approach.

    Yet, scoring remains a process which spins webs between individuals that conceal intersystemic phenomena in the guise of individuals making shots. The concepts of individual scoring, of shot quality, and of additional context attributed to the moment of a shot, serve as psychological safety nets against the masses of tangible and intangibles processes at work during possessions, processes within processes. To understand the extent to which data accumulates, let us tentatively outline a fundamental, ecological process of offensive possessions.

    The Pick-and-Roll

    Perhaps the most widespread tactical approach in basketball’s collective knowledge: the pick-and-roll, and any variation on which the “roller” (if not multiple) will typically relocate to a higher space on the court. Such plays are instinctually recognized as processes, either premeditated or an impromptu one whose execution is predetermined. A common goal of basketball offenses is to convert on the “best” shot possible, the one which will maximize their output in the limited space and time which they receive. They are shots that exist as possibilities and ranges; they are conditional and require recognition of what can be instead of merely what is, and sometimes are never found.

    Shots are not free, bound by the limited space and time of possessions but also by the alternatives by which the team might have scored. All shots have costs. (This is why the notion that “efficiency” does not matter is often disregarded.) Sometimes that cost, that next-best alternative, is more than the actual result (team fails to convert on “best” shot possible) and sometimes is it less (team succeeds to convert on “best” shot possible). The pick-and-roll illustrates how this phenomenon relates to the process of scoring, the manners in which teams seek the “best” shot possible and how the process influences the ability to seek, the trade-offs involved in a multi-dimensional scoring process.

    Let us conceptually omit the variance in remaining teammates and opponents, coaching staffs, and any parts which influence the happenings on the court during an offensive possession. During the pick-and-roll, there exist a Ball-Handler and a Roller, the former designated with the initiation of optimizing the “goal” (to find the “best” shot possible) with the ball in his hands while the latter encourages this by setting a screen. The two-man interaction between the Ball-Handler the Roller can be viewed as cyclical, an interdependent process by which both parts attempt to optimize the goal by improving each other’s shot quality.

    A “traditional” pick-and-roll would ideally result in a field-goal attempt at the rim for the Roller, as such shots (on average) garner the highest expected point-values and taller, sturdier bigs who set screens are less prone to physical resistance in the key. A manner in which the Handler can improve the Roller’s shot quality is by preoccupying defenders, as more space to operate will increase the shot quality of the Roller because he has less physical resistance against his shot. To act out such a thing, the Handler must communicate to the defense a reason for which he must receive an “extra” amount of attention, to open space for the Roller or instigate a chain-reaction of help defense which could improve shot quality for teammates. (Although we solely focus on the Roller in this instance.)

    To receive that extra attention is to possess a threat by which the Handler could score with the ball to a degree that exceeds the concerns of his teammates. Thus, the Handler must possess what is colloquially known as a “scoring threat” to improve the Roller’s shot quality in the manner expressed earlier. To do so he must previously score through ways which threaten the defense (processes within processes) and predispose the defense to cautionary measures in following possessions. If the Handler is successful in this regard, he may successfully contribute to the shot quality improvement at the moment of the Roller’s attempt and contribute to the process of scoring.

    So why don’t teams employ this two-man game in every possession if they will consistently maximize the difference between their shot quality and the opportunity cost? Because observed repetition refines cautionary measures, and the play is designed to exploit cautionary measures. A team’s shot quality would trend downward because the interaction between the Handler and Roller changed significantly; the further removed a defense is from observing the Handler’s scoring threat, the less likely they are to instigate the cautionary measure which allows for the play in the first place. Therefore, the Handler must recognize the trade-off, revert to earlier habits of attack to keep opponents on their toes and create a possession of possibilities, thereby allowing the process to continue.

    Simultaneously, the Roller may continue to garner defensive attention due to the shift toward his scoring threat. The defense would expect him to shoot more frequently and more efficiently, and thus alter their cautionary measures to account for more of his shooting attempts. The result would draw a discrete amount of attention away from the Handler, thus allowing for more opportunities for the Handler to score frequently and more efficiently, the precedence by which the Handler can then influence the Roller’s ability to score frequently and more efficiently. Thus, the process is cyclical, one which evaluates trade-offs and alters the roles of the parts within the system to interdependently optimize its goals.

    The Quasi-Existence of Individual Scoring

    Points and efficiency, although functional as crude data points of the results of a player’s shots, shed minimal light on the processes by which teams score under the principles of intersystemic thinking. Because the processes often involve trade-offs, the selections of attacks which repress individual talent and independent decision-making, the concept of individual scoring is intertwined in an elegant, endless system from which the concept cannot be unraveled. So why do we so often prescribe such incomplete data to the questions that arise?

    People’s predisposition to default to digestible, if-incomplete measuring tools breeds the ground for selectivity, taps into our insatiable need to quantify and rank our self-imposed classifications that narrow our worldviews and set the stage for unknowing, the consequence to the tactics of pattern recognition and the reconfirmation of our heuristics. Scoring as the main principle of basketball understands this, exists as a thing born out of many but is often reduced to the one, and urges us to reconsider the manners in which we observe and judge.