• 2023 All-Star Power Rankings | Volume I

    2023 All-Star Power Rankings | Volume I

    Monthly data from the NBA begets the fruitless (and slightly masochistic) tradition of ranking players. This post won’t rank players in the typical sense—in, say, an ordered list. Rather, I’m continuing a series I’ve done each of the past two seasons in which I update my All-Star ballot continuously throughout the season. (Read introductory editions for 2021 and 2022 for list structure.) Now, with 13-16 games under the healthy stars’ belts, I’m slightly comfortable indulging myself in this kind of thing. Leave your criticisms in the comments!

    Tier 1

    Regardless of positional constraints, these players are performing at All-Star levels. (The lower bound of their estimated value matches or exceeds All-Star “level.”) To argue otherwise may earn you the label of a basketball heretic.

    • Giannis Antetokounmpo (East)
    • Devin Booker (West)
    • Jimmy Butler (East)
    • Stephen Curry (West)
    • Anthony Davis (West)
    • Luka Doncic (West)
    • Kevin Durant (East)
    • Joel Embiid (East)
    • De’Aaron Fox (West)
    • Paul George (West)
    • Shai Gilgeous-Alexander (West)
    • Tyrese Haliburton (East)
    • James Harden (East)
    • LeBron James (West)
    • Nikola Jokic (West)
    • Damian Lillard (West)
    • Donovan Mitchell (East)
    • Ja Morant (West)
    • Pascal Siakam (East)
    • Jayson Tatum (East)
    • Karl-Anthony Towns (West)
    • Myles Turner (East)
    Tier 2

    These players have a larger margin of error associated with their status as All-Star performers. I’d consider these guys to be strong candidates. Whether or not they make the ballot is, in large part, determined by the NBA’s talent distribution at the top and positional constraints.

    • Bam Adebayo (East)
    • Jarrett Allen (East)
    • Jaylen Brown (East)
    • DeMar DeRozan (East)
    • Darius Garland (East)
    • Rudy Gobert (West)
    • Draymond Green (West)
    • Jrue Holiday (East)
    • Brandon Ingram (West)
    • Kyrie Irving (East)
    • Brook Lopez (East)
    • Chris Paul (West)
    • Domantas Sabonis (West)
    • Zion Williamson (West)
    • Trae Young (East)
    The Ballot

    You know the rules: 5 starters (2 frontcourt, 3 backcourt); 5 reserves (2 frontcourt, 3 backcourt); and 2 wild cards (position negligible). Rosters for both the Eastern and Western Conference.

    Eastern Conference
    • James Harden
    • Donovan Mitchell
    • Giannis Antetokounmpo
    • Kevin Durant
    • Joel Embiid


    • Darius Garland
    • Tyrese Haliburton
    • Jimmy Butler
    • Pascal Siakam
    • Jayson Tatum


    • Kyrie Irving
    • Myles Turner
    Western Conference
    • Stephen Curry
    • Luka Doncic
    • Anthony Davis
    • Nikola Jokic
    • Karl-Anthony Towns


    • Shai Gilgeous-Alexander
    • Ja Morant
    • Paul George
    • LeBron James
    • Domantas Sabonis


    • Devin Booker
    • De’Aaron Fox

    The NBA’s talent distribution makes it increasingly harder to choose All-Stars in its current roster format. Before, I’ve done up to 4 tiers of players to be considered for All-Star, in which the gaps among tiers were fairly recognizable. But this year, I somehow managed to fill 37 spots in 2 tiers. (Hence, I omit the last 2 tiers.) Stat “inflation” is one thing to consider in which the values of counting statistics like points and assists are lower than in seasons past. But there’s also a clear distinction between current and previous talent distributions. Thus, it may be worth revising the definition of an All-Star. (For example, expanding the number of roster spots to 15.)

    This season has legitimately been a fever dream. The Kings and Pacers have 2 All-Stars each (according to me). Brook Lopez and Myles Turner are officially on my agenda. Shai Gilgeous-Alexander might be the box-score MVP if not for an impromptu Stephen Curry mega-explosion. (Curry is currently my MVP frontrunner.) At the end of the day, I’m glad doing this didn’t worsen my tension headache. Please leave criticisms below! I don’t watch every game or look at every stat.

  • 2022 NBA Preview | Win Predictions & Power Rankings

    2022 NBA Preview | Win Predictions & Power Rankings

    (Picture via NBA)

    I typically don’t do things like this (because I think they’re pointless), but this season is as good as any for the masochistic practice of predictions! Yes, these will, in part, be your typical run-of-the-mill record predictions, but I’ll also throw in some power rankings to spice it up. Let me start by differentiating between those two things: 1) The win predictions are, shockingly, the number of games I estimate a team to win in 2023. By no means are they accurate or reliable, but they serve as ballparking values for more-or-less how I’m feeling about a team right now. 2) The power rankings, or the actual order in which teams are ranked, are based on the likelihood that I think each team will win the title. These are what I’d consider my actual “ranking” of each team. That means I may project a higher win total in the regular season for a team whose championship odds are superseded by another. With that wrapped up, let’s get into the nitty-gritty!

    (NB: I don’t end up predicting wins. Fairly self-explanatory.)

    “Just wait and see, five years from now…”

    30. Utah Jazz


    29. Houston Rockets

    Jalen Green could drop 20 a game. Looking out for him to blossom into a real offensive threat in the near future.

    28. Oklahoma City Thunder

    27. San Antonio Spurs

    Sometimes I forget they exist.

    26. Indiana Pacers

    25. Detroit Pistons

    Minefield of young talent. Not nearly fleshed out enough to make a push for anything, but I am eager to explore the synergies between their rookies and sophomores.

    24. Sacramento Kings

    Mike Brown as HC will make them an interesting watch. Preseason has shown us some gritty, switch-heavy defense, although the Kings have been treating the preseason like it’s the NBA Finals. They have an interesting assortment of players who are worth the view.

    23. Orlando Magic


    22. Washington Wizards

    I am oddly intrigued by this team. Not because I think they’ll be good, but because Beal-Porzingis will be an interesting offensive combo spread over time. Would tune into several of their games for Deni Avdija alone.

    21. Charlotte Hornets

    20. New York Knicks

    19. Chicago Bulls

    18. Portland Trail Blazers


    17. Atlanta Hawks

    16. New Orleans Pelicans

    Looking like they could ascend to “good” in the near future if the indicators show up. Throwing Zion into Ingram-McCollum offenses could be dangerous (for the opponents). But they did lose Tony Snell. Probably near the top of my watchlist. Basically tied with next team.

    15. Los Angeles Lakers

    Darwin Ham might be a basketball genius. Anthony Davis’s health is obviously key if they want to come close to contending. LeBron will probably still receive soft MVP consideration. Austin Reaves will manhandle your favorite backcourt. But the Westbrook signals don’t look great so far, especially on defense. If that domino doesn’t fall, I don’t see a spectacular ceiling for this team.


    14. Memphis Grizzlies

    13. Toronto Raptors

    Canadians are too defensive of their basketball team for me to risk ranking them any lower. But seriously, the Raptors are looking like they’ll be a good team. They’re young and complement each other well enough, so some upside is feasible. Not quite past that play-in level. Not quite average.

    12. Minnesota Timberwolves

    I don’t know.

    11. Cleveland Cavaliers

    10. Brooklyn Nets

    By now, I’m just playing Russian Roulette trying to get this right.


    9. Dallas Mavericks

    8. Denver Nuggets

    Lots of offense! Lots of offense! Jokic-MPJ-Murray lineups, in the spirit of throwing out predictions, will produce the highest offensive ratings in history this season. That’s it. Keep Murray healthy. Don’t let Michael Porter Jr. handle the basketball. Jokic. Defensive questions.

    7. Miami Heat

    6. Phoenix Suns

    No, I don’t think this team fell off a cliff. Well-coached, Devin Booker is a legitimate primary offender (get it?) at this point, so Chris Paul’s aging curve at least has a failsafe. Losing JaVale McGee is only a minor tragedy. Not sure if this is going to be a team that runs away with another top seed, though.


    5. Philadelphia 76ers

    De’Anthony Melton and P.J. Tucker are sneaky good pieces. Harden and Embiid shared minutes will produce some of the highest offensive ratings in the league, and they’re looking like a contender for one of the league’s best defenses too. Montrezl Harrell? I really don’t know.

    4. LA Clippers

    Literally flipped a coin to choose between them and Philly. No, I’m not kidding! I know that sounds like a joke, but it’s not.

    3. Boston Celtics

    This team got better on paper, but… Until they demonstrate in the regular season that the off-court predicaments are going to bleed into the on-court stuff to a “significant” level, I’m riding with the talent. Malcolm Brogdon is such a savvy addition to this roster, brings the pick-and-roll chops their offense would have thrived with last year. Timelord’s knee and the other stuff likely will cost them several games in the regular season, so let’s hope for them that home-court advantage falls their way (even if it’s not crucial).

    2. Milwaukee Bucks

    The Bucks added Joe Ingles! Let us fall to our knees and rejoice in this miracle! But seriously, that was quite the player to bring in during an offseason in which they lost no rotational pieces. Milwaukee underperformed in the regular season last year and lost a cutthroat semis that was decided by Grant Williams’s hot hand. This team is legitimately great and there should be no surprises if they capture their second title in three seasons next June.

    1. Golden State Warriors

    I’m liking Golden State to repeat for the championship this year. (Not particularly worried about the Draymond situation yet.) Donte DiVincenzo looks to be a prototypical Warrior, passing to cutters with solid perimeter defense off the bench. But Gary Payton II will be sorely missed at the point-of-attack. That dude was legitimately a monster and his contributions will never be fully appreciated. JaMychal Green also has some passing chops apparently, is a nice movement shooter you can stick in the corner and pull defenses. James Wiseman is not a finished product, but he’s big (even if his handle is vulnerable), incredible lateral quickness for his size, with a wide dunk radius that adds some versatility to the Warriors’ passing targets with lob finishing. Poole continues to grow, Curry (no explanation needed), and Draymond doesn’t fall off a cliff, and this team is a sure contender.

    Time to roast me in the comments!

  • 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 NBA.com.  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.

  • The Ultimate PER Guide | Untangling John Hollinger’s Analytics Lovechild

    The Ultimate PER Guide | Untangling John Hollinger’s Analytics Lovechild

    (Image via Bleacher Report)

    Player Efficiency Rating (PER) was the turning point for basketball analytics in the public domain; but with time comes controversy. Lots will claim that PER is an outdated metric, that recent efforts of all-in-one metrics have left PER useless. Let’s say this is true, and for good reason. PER has a limited scope compared to lots of modern metrics, which incorporated Plus/Minus and tracking data that seems to map a player’s value to an objectively greater degree than the box score. Let us be reminded that this is a good thing! More advancements means more descriptive power among these all-in-one metrics, so the science of player analysis has undergone significant evolution since Hollinger’s initial effort. So, as a brief summation, PER is NOT one of the absolute best measures of player impact for the modern era. But like any other subject with a rich history, there is value in going back to appreciate tradition. Let us explore the inner workings of PER. What makes it good or bad, and why should or shouldn’t you use it?

    How PER Works

    PER puts the “E” in “Efficiency” because it was designed as a fairer comparison between high-volume superstars and role players. Like lots of modern impact metrics, PER is a per-possession metric that estimates a player’s effect on his team’s point differential [1]. But instead of using advanced, complex mathematical modeling to accomplish its results, PER is grounded in concept and critical thinking. It works so that each box score statistic (points, assists, etc.) is assigned a weighting, which is personalized to its box score stat such that each weighting reflects the statistic’s point-value. If that sounds a bit confusing, hopefully this example makes things clearer:

    LeBron James committed 196 turnovers [2] during the 2022 regular season. Most basketball fans will agree that turnovers are bad! A turnover means the offensive team lost possession of the ball without having scored. Thus, they lost out on the opportunity to score points in that possession; and basketball is often a game that is won in a matter of possessions. Plus, there’s also the drawback from permitting the opposition a transition possession, which tends to yield more points than typical half-court offense. So we can all agree turnovers are bad, right? But how do we know how bad one is? Well, we have to consider what a turnover is taking away from, and the big thing is those aforementioned points the team could have scored if the ball weren’t turned over.

    Since a variety of outcomes could’ve followed barring one of James’s turnovers, Hollinger decided to use a single placeholder number to sum up all possible outcomes of a possession. This estimates just how many points the Lakers were missing out on because of the turnover. Let’s think about it this way: on average, NBA offenses scored 1.12 points per possession in the 2022 regular season [3]. Hollinger concludes this was the best estimate of how much an offense is missing out on from turning the ball over, not accounting for how the opponent can gain a transition possession, as stated earlier. But since the turnover is deducting that amount of value, a turnover in 2022 is worth approximately -1.12 points! If we apply that weighting to all of LeBron James’s 196 turnovers this year, we can estimate that he cost the Lakers almost 220 points from his turnovers.

    This process is done for all box score statistics (most of which have far more complicated weightings), and the results are summed up to estimate how many points a player is adding (or subtracting) to his team. However, as stated earlier, there is one last adjustment that converts the results to a per-possession basis. Namely, superstars don’t receive any benefits for playing more of the game. This “adjusted” form of PER is what you’ll see on Basketball-Reference or ESPN leaderboards. With that out of the way, let’s discuss the good and the bad of PER, and what its process means for the metric’s ability to evaluate players.

    The Good and the Bad

    Believe it or not, there is some good stuff about PER. Take this as more of an opinion than a fact, but PER has solid (and interesting) critique behind it. The idea that each statistics has a hypothetical point-value behind it is logic that is used to this day, even with the most revered metrics like Estimated Plus/Minus and BBall-Index’s LEBRON; and PER was the first to do it. Let’s also consider the benefit to translating the results to per-possession rather than per-game or across an entire season. PER can be a reasonable tool for identifying hidden gems slotted at the end of the bench, and can act as an indicator of whether a role player is outperforming one of the better players on the team. Back when PER was the gold standard, there was some value it could provide to teams looking to manage their roster and shuffle their lineups. Not at all a bad result!

    However, there are a considerable number of drawbacks to discuss with PER; and unfortunately, these dominate the conversation and are often valid concerns of the metric. Let’s return to the per-possession basis of PER. While there may be benefits, there are also flaws with this approach. There is the matter of fatigue in which players with a heavier load tend to tire out near the end of games, whereas role players can spend more of their time on the court with a fresh set of legs. Superstars also tend to player with better teammates, meaning they have fewer chances to exert their skill; and they often play tougher opponents, whereas role players tend to match up with the opponent’s bench units as well. That is, not all statistics are created equally, and a player’s impact can be reframed and reimaged depending on who else is on the court, which is something PER struggles to account for.

    The other glaring issue with PER is that it lacks a benchmark, or some type of measurement to fall back on to confirm the metric is grounded in reliability. If you’re familiar with other popular impact metrics, you may remember that they are often backed by RAPM, which is arguably the strongest all-in-one metric basketball has to offer. If not, I have written in-depth about RAPM before [4] if you’re interested in learning more about it. (TW: Math.) PER has been around for nearly twenty years, so it’s no surprise that it has been one-upped by more recent metrics, but the larger issue is that John Hollinger has refused to update the metric. PER has been more or less the same since 2005, which excludes the metric from the comprehensive analytical techniques of today. This, paired with the inconclusive nature of the metric’s logic, makes PER one of the less viable options to analyze players.

    Should You Use It?

    The biggest argument against using PER is that there are many better alternatives, which is true. Estimated Plus/Minus, LEBRON, RAPTOR, and BPM, among others, all have more solid bases in accomplishing their goals. We’ve discussed the positives and the negatives of the metric, and are hopefully all able to recognize that both exist. But, again, PER is simply outdated. Hollinger’s refusal to refine his metric has left it in the dust. You’re much better off using the Backpicks (proprietary) or Basketball-Reference models of BPM if you’re looking to view a player’s impact from the perspective of the box score. But does this mean PER is bad?

    I wouldn’t say so. PER has a very intriguing basis, and the logic Hollinger incorporated into the metric shows a considerable understanding of how basketball is played and won. Therefore, PER is still going to be quite a hit-or-miss metric, as lots of them are. But it’s not “bad” to the point that it measures every player off the mark. Heck, there’s the off-chance that PER pins a player down just right! However, it’s not great either, especially considering its peers. So the next time someone tells you PER is the worst thing to grace the Earth, tell them it could be worse. If it helps, tell them to look into Wins Produced from the Wages of Wins Journal. But that’s all from me, folks. I hope this was a somewhat insightful look into PER, and that you’re next perusing of a leaderboard is that much more confident.

    [1] https://www.basketball-reference.com/about/per.html

    [2] https://www.basketball-reference.com/players/j/jamesle01.html

    [3] https://www.basketball-reference.com/leagues/NBA_2022.html

    [4] https://www.cryptbeam.com/2021/03/08/how-do-nba-impact-metrics-work/

  • My Top-10 NBA MVP Candidates (2/27/22)

    My Top-10 NBA MVP Candidates (2/27/22)

    (📸 The Ringer)

    The conflict between performing and storytelling on the basketball court has clouded the MVP race for decades; and like other highly-divisive topics in sports, both sides are represented by two camps:


    Traditionally, the MVP has a very loose criterion that hinges on the voter’s ability to tell a story with their vote. For example, take 2011 Derrick Rose’s MVP. He was clearly not the best regular-season player that year, but the Chicago Bulls sneaked up behind the newly-formed Heatles and snagged the first seed in the East. Sure, the Bulls were successful because of their defense and not their offense, but Rose’s floor-raising efforts and acrobatic, engaging playstyle drew fans to the screens and promoted the league’s popularity. Was Derrick Rose the “Most Valuable Player” in 2011? No; but nominating him as the MVP was good for the NBA.

    Perhaps more recently, there are those who strictly adhere to how “good” a player is when selecting their MVP ballot. This is a basketball-only approach that looks to recognize players based on their ability to affect a team’s chances of winnings: win-loss record, seeding, home-court advantage, all of that. There’s an obvious appeal here: it’s less biased than narrative-based voting when done right; it provides a sturdier baseline to evaluate candidates (not everyone cares who the best player on the best team is when using storylines); and it forces us to look closer at what’s happening on the court and overcome the deficiencies in our thought-processes.

    I flesh out these two approaches to not advocate for one or the other, but to establish my priorities as I assemble this list. Typically, my content has been based around the second approach: trying to use observations and material evidence to support arguments for one player or another. While such an approach is fully appropriate for, say, a player rankings list, I’m not sure the same applies to the MVP Award. Perhaps the “Most Valuable Player” isn’t necessarily providing the most value toward winning, but toward the welfare of the Association. With this in mind, I’ll still reference a player’s actual skill and heavily weigh it in my final ranking, but another priority is how a player has been drawing fan interest and promoting league viewership to provide a more holistic view of who has been the NBA’s Most Valuable Player in 2022.


    10. Kevin Durant

    The Slim Reaper has missed some time with injury troubles, but has still been playing around MVP levels when he’s on the court. Durant has assembled a masterful offensive skill set throughout his career, pairing his length in driving lanes with all-time shooting ability, which are exactly the types of characteristics you’d wish to see in a player who moves the needle for championship offenses. 

    9. Luka Doncic

    The greatest basketball prodigy since LeBron James started off the season in a bit of a slump, but picked himself up in recent weeks, re-evolving into the dangerous offensive centerpiece we’ve seen him be for the third consecutive season. Doncic can hit defenses quickly with unexpected attacks and passes out of traps extremely well. He’s a similar build to James in that he can quarterback an elite offense surrounded by shooters who leverages his drive-and-kick game to their advantage. Dallas has also made a surprise entrance as a top-five team in the West by SRS. [1]

    8. Chris Paul

    The Phoenix Suns have been the best team in basketball so far, and it would be narratively unjust to go without recognizing one of their players. Among the league leaders in my hand-tracking of shot creation, Paul is a masterful pick-and-roll ball-handler, splitting defenses at will and punishing drop with his lethal mid-range shooting. He drives Phoenix’s offense and manages to play at All-NBA levels while fighting against Father Time’s inescapable aging curve. CP3 is a talent for the ages.

    7. Ja Morant

    My favorite player to watch in 2022 and the sport’s next great athletic freak. Morant has been the figurehead of an exciting Memphis Grizzlies squad while being one of the more improved players in the league, playing around All-NBA basketball this year. At his best, Morant creates easy offense like the game’s very best. His incredibly dynamic scoring and uncanny passing vision formulates one of the most unique offense skill sets in the sport. Morant will do all this and also steal defensive rebounds from your favorite big man.

    6. DeMar DeRozan

    DeRozan is one of very few players who have proved they can still be one of the game’s very best scorers without three-point shooting. He’s been breaking basketball with his mid-range shooting, making 51% of a mind-shattering 14.5 attempts per 75 possessions! [2] DeRozan’s storyline would arguably make him a finalist if I were more partial to narratives, but there’s too weak of a stance based on performance alone. Playoff concerns aside, this player has been a unit on the basketball court, revitalizing a stunted career and shedding loads of doubt from critics.

    5. LeBron James

    The King is still really good at basketball. He’s been having one of his best regular season in years, punishing defenses with the scoring that vaulted his name into the GOAT conversation many years ago. Also one of the best passers in the sport and an adequate floor-spacer whose timeless athletic prowess makes him a strong defensive player even in his nineteenth season. The regular season could be hinting toward the reemergence of Playoff LeBron in a few month’s time.

    4. Giannis Antetokounmpo

    Ah, the wonders of voter fatigue. The Greek Freak was the third finalist on my ballot from last year, and while he’s done an outstanding job of raising the ceiling for a smaller-market team, the league might do better spicing things up a bit. Regardless, Antetokounmpo is arguably the first or second-best player in basketball right now, making the fourth spot my absolute floor for him.

    3. Stephen Curry

    Probably the face of the NBA. Curry is among the league-leaders in merchandise sales [3] and promotes the best TV ratings [4] in the league. His scoring has taken a massive hit from its glorious past, but his ability to bend and break defenses remains. Curry is loved by impact metrics and is having one of the best defensive and passing seasons of his career. Given he had maintained his early-season hot streak, he would be the top player on this list. But for now, he slides back to third.

    2. Joel Embiid

    Embiid is a masterful scorer who pairs punishing brute force in the paint with grateful skill on the block that garnered (self-anointed) comparisons to the likes of Kobe Bryant and Hakeem Olajuwon. He’s still one of the strongest defenders in the league and an improving playmaker. The fans seem to want to see Embiid as a finalist for a second straight season, and such recognition is warranted.

    1. Nikola Jokic

    The claim for the best player in the league in 2022 seems particularly exclusive this season because of the Joker. We’re talking about a player who has legitimate arguments as both the best scorer and playmaker in the sport right now, blending three-level scoring with an unheralded array of passes that stretch defenses to their absolute limits. Jokic has also been a clear positive on defense this season. By the way, Denver outscoring teams by +9.7 points per 100 with him on the floor and have been 19.2 points worse with him off. [5]

    [1] https://www.basketball-reference.com/leagues/NBA_2022.html

    [2] https://backpicks.com/2022-players/

    [3] https://www.nba.com/news/nba-top-selling-jerseys

    [4] https://sbi.co.in/web/sbi-in-the-news/research-desk

    [5] https://www.basketball-reference.com/players/j/jokicni01.html

  • Park-Adjusted Statistics for the MLB

    Park-Adjusted Statistics for the MLB

    (📸 MLB.com)

    The development of sabermetrics in Major League Baseball has coincided with an increased awareness of confounders, one of which is the effect of ballparks and their playing environments. The ubiquitous example is Coors Field in Denver, whose abnormal elevation is known to cause baseballs to travel 5 to 10% farther than at typical sea level. [1] While comprehensive statistics like On-Base Plus Slugging (OPS) and weighted Runs Created (wRC) have spawned improvements that account for such prior knowledge, these principles have yet to be widely applied to traditional box scores. Thus, I set out to develop a subset of “park factors” that can be used to manipulate statistics like batting average and home runs to provide a more level playing field when comparing these measurements between players of varying ballpark conditions.

    The Example

    Colorado Rockies players often have more scrutinized slash lines and power statistics because of the aforementioned data on the elevation effect. Let’s use home runs in the 2021 regular season as the example of park-adjusted statistics to observe the supposed bottom-line effect in home-run frequency between stadiums. To estimate a ballpark’s effect on home runs among all thirty teams, their home-run frequency (measured as home runs per plate appearance) is compared between home and away games. To include a larger breadth of information, a five-year data set was used. (For 2021, seasons 2017 through 2021 were used to calculate park factors.)

    Because the entire league is usually more prone to hit home runs at home than on the road (likely stemming from the home-field advantage), a small adjustment is made to reflect this. Additionally, only half of the measured effect is added to the rating because teams split roughly half of their schedule between home and away games. Therefore, the final measurement we’re left with is the percent increment of a team’s home-run frequency on the road versus at home relative to the league. This park factor can be used on the player level, too. Let’s see how the power-hitting landscape changes when these listed adjustments are applied to MLB batters.

    The Results

    NB: Traded players occupy multiple rows for each team played for.

    NameTeamAdj HRHRLuck
    Salvador PerezKCR5548-7
    Vladimir Guerrero Jr.TOR46482
    Marcus SemienTOR43452
    Fernando Tatis Jr.SDP4342-1
    Shohei OhtaniLAA43463
    Brandon LoweTBR4339-4
    Matt OlsonOAK4139-2
    Mitch HanigerSEA4139-2
    Rafael DeversBOS4038-2
    Tyler O’NeillSTL3934-5
    Nolan ArenadoSTL3934-5
    Pete AlonsoNYM3837-1
    Jose RamirezCLE3736-1
    Kyle SeagerSEA3735-2
    Aaron JudgeNYY36393
    Mike ZuninoTBR3633-3
    Paul GoldschmidtSTL3631-5
    Brandon BeltSFG3429-5
    Jorge PolancoMIN3433-1
    Austin RileyATL3433-1
    Yordan AlvarezHOU3433-1
    Max MuncyLAD33363
    Hunter RenfroeBOS3331-2
    Giancarlo StantonNYY33352
    Joey VottoCIN32364
    Bryce HarperPHI32353
    Freddie FreemanATL3231-1
    Jose AltuveHOU31310
    Franmil ReyesCLE3130-1
    Miguel SanoMIN3130-1
    Nick CastellanosCIN31343
    Teoscar HernandezTOR31321
    Ozzie AlbiesATL3130-1
    Kyle TuckerHOU30300
    Adolis GarciaTEX30311
    Austin MeadowsTBR3027-3
    J.D. MartinezBOS2928-1
    Ryan MountcastleBAL29334
    Mike YastrzemskiSFG2925-4
    Matt ChapmanOAK2927-2
    Jose AbreuCHW29301
    Manny MachadoSDP2928-1
    Patrick WisdomCHC28280
    Juan SotoWSN28291
    Avisail GarciaMIL28291
    Brandon CrawfordSFG2824-4
    Eugenio SuarezCIN28313
    Bo BichetteTOR28291
    Dansby SwansonATL27270
    Jared WalshLAA27292
    Bryan ReynoldsPIT2724-3
    Cedric Mullins IIBAL27303
    Josh DonaldsonMIN2726-1
    Carlos CorreaHOU26260
    Bobby DalbecBOS2625-1
    Josh BellWSN26271
    Ian HappCHC25250
    Justin TurnerLAD25272
    C.J. CronCOL25283
    Rhys HoskinsPHI24273
    Andrew McCutchenPHI24273
    Ronald Acuna Jr.ATL24240
    Kyle SchwarberWSN24251
    Xander BogaertsBOS2423-1
    Jesus AguilarMIA2422-2
    Adam DuvallMIA2422-2
    Joey GalloTEX24251
    Robbie GrossmanDET2423-1
    Will SmithLAD23252
    Eduardo EscobarARI2322-1
    Jonathan SchoopDET2322-1
    Eric HaaseDET2322-1
    Luis UriasMIL22231
    Javier BaezCHC22220
    Yasmani GrandalCHW22231
    Randy ArozarenaTBR2220-2
    Paul DeJongSTL2219-3
    Carlos SantanaKCR2219-3
    Jesse WinkerCIN22242
    Jake CronenworthSDP21210
    Gary SanchezNYY21232
    Trevor StoryCOL21243
    Seth BrownOAK2120-1
    Willson ContrerasCHC21210
    Mookie BettsLAD21232
    Enrique HernandezBOS2120-1
    Randal GrichukTOR21221
    George SpringerTOR21221
    Buster PoseySFG2118-3
    Wilmer FloresSFG2118-3
    LaMonte Wade Jr.SFG2118-3
    Francisco LindorNYM2120-1
    Dylan CarlsonSTL2118-3
    Ryan McMahonCOL20233
    Lourdes Gurriel Jr.TOR20211
    Jazz Chisholm Jr.MIA2018-2
    Austin HaysBAL20222
    Byron BuxtonMIN19190
    Nelson CruzMIN19190
    Max KeplerMIN19190
    Andrew BenintendiKCR1917-2
    Willy AdamesMIL19201
    A.J. PollockLAD19212
    Jonathan IndiaCIN19212
    Ty FranceSEA1918-1
    Jonathan VillarNYM1918-1
    Trey ManciniBAL19212
    Darin RufSFG1916-3
    Cesar HernandezCLE1918-1
    Harrison BaderSTL1816-2
    Chris TaylorLAD18202
    Hunter DozierKCR1816-2
    Brad MillerPHI18202
    Kris BryantCHC18180
    Mark CanhaOAK1817-1
    Sean MurphyOAK1817-1
    Trea TurnerWSN17181
    Wil MyersSDP17170
    Nathaniel LoweTEX17181
    Tyler NaquinCIN17192
    Jeimer CandelarioDET1716-1
    Bobby BradleyCLE1716-1
    Adam DuvallATL16160
    Tim AndersonCHW16171
    Anthony SantanderBAL16182
    Elias DiazCOL16182
    Justin UptonLAA16171
    Luis TorrensSEA1615-1
    Kevin PillarNYM1615-1
    Miguel CabreraDET1615-1
    J.T. RealmutoPHI15172
    Jesus SanchezMIA1514-1
    Trent GrishamSDP15150
    Tommy PhamSDP15150
    Yuli GurrielHOU15150
    Evan LongoriaSFG1513-2
    Alex DickersonSFG1513-2
    Jed LowrieOAK1514-1
    Ramon LaureanoOAK1514-1
    Jorge SolerKCR1513-2
    Corey SeagerLAD15161
    Jarred KelenicSEA1514-1
    Michael ConfortoNYM1514-1
    Ketel MarteARI1514-1
    Kyle FarmerCIN14162
    Ryan JeffersMIN14140
    Andrew VaughnCHW14151
    Jorge SolerATL14140
    Chas McCormickHOU14140
    Nelson CruzTBR1413-1
    Yandy DiazTBR1413-1
    Brett PhillipsTBR1413-1
    Anthony RizzoCHC14140
    Austin SlaterSFG1412-2
    Rougned OdorNYY14151
    Alex VerdugoBOS1413-1
    Michael A. TaylorKCR1412-2
    Ryan ZimmermanWSN14140
    Kolten WongMIL14140
    Carson KellyARI13130
    Akil BaddooDET13130
    Yoan MoncadaCHW13141
    Mitch GarverMIN13130
    Brendan RodgersCOL13152
    Frank SchwindelCHC13130
    Gio UrshelaNYY13141
    Jean SeguraPHI13141
    Yadier MolinaSTL1311-2
    Tommy EdmanSTL1311-2
    Manny PinaMIL13130
    Dylan MooreSEA1312-1
    Luis RobertCHW12131
    Sam HilliardCOL12142
    Gregory PolancoPIT1211-1
    Eric HosmerSDP12120
    Martin MaldonadoHOU12120
    Alex BregmanHOU12120
    Joey GalloNYY12131
    Max StassiLAA12131
    Joey WendleTBR1211-1
    Ji-Man ChoiTBR1211-1
    Odubel HerreraPHI12131
    Didi GregoriusPHI12131
    Tyrone TaylorMIL12120
    DJ PetersTEX12120
    Tom MurphySEA1211-1
    Charlie BlackmonCOL11132
    Dominic SmithNYM11110
    Josh RojasARI11110
    Daulton VarshoARI11110
    Pavin SmithARI11110
    Whit MerrifieldKCR1110-1
    Amed RosarioCLE11110
    Colin MoranPIT1110-1
    Rafael OrtegaCHC11110
    Joc PedersonCHC11110
    Albert PujolsLAD11121
    Manuel MargotTBR1110-1
    D.J. StewartBAL11121
    Omar NarvaezMIL11110
    Mitch MorelandOAK1110-1
    Nick SolakTEX11110
    Danny JansenTOR11110
    Gavin SheetsCHW10111
    James McCannNYM10100
    Christian WalkerARI10100
    Austin HedgesCLE10100
    Ryan O’HearnKCR109-1
    Luke VoitNYY10111
    Lewis BrinsonMIA109-1
    Garrett CooperMIA109-1
    Miguel RojasMIA109-1
    Pedro SeverinoBAL10111
    Maikel FrancoBAL10111
    Garrett HampsonCOL10111
    Jonah HeimTEX10100
    Eloy JimenezCHW10100
    Jake FraleySEA990
    J.P. CrawfordSEA990
    Javier BaezNYM990
    Steven DuggarSFG98-1
    Willi CastroDET990
    Niko GoodrumDET990
    DJ LeMahieuNYY9101
    Brett GardnerNYY9101
    Kyle HigashiokaNYY9101
    Yu ChangCLE990
    Jack MayfieldLAA9101
    Brent RookerMIN990
    Trea TurnerLAD9101
    Cody BellingerLAD9101
    Tyler StephensonCIN9101
    Aristides AquinoCIN9101
    Yoshi TsutsugoPIT98-1
    Ben GamelPIT98-1
    Jacob StallingsPIT98-1
    Dom NunezCOL9101
    Lewin DiazMIA98-1
    Yan GomesWSN990
    Yadiel HernandezWSN990
    Christian YelichMIL990
    Daniel VogelbachMIL990
    Tony KempOAK880
    Taylor TrammellSEA880
    Gleyber TorresNYY891
    Brandon NimmoNYM880
    David PeraltaARI880
    Bradley ZimmerCLE880
    Alex KirilloffMIN880
    Ha-seong KimSDP880
    Thairo EstradaSFG87-1
    Tommy La StellaSFG87-1
    Donovan SolanoSFG87-1
    Kris BryantSFG87-1
    William ContrerasATL880
    Michael BrantleyHOU880
    Aledmys DiazHOU880
    Jason CastroHOU880
    Jason HeywardCHC880
    David BoteCHC880
    Freddy GalvisBAL891
    Michael PerezPIT87-1
    Lorenzo CainMIL880
    Isiah Kiner-FalefaTEX880
    Starling MarteMIA87-1
    Brian AndersonMIA87-1
    Wander FrancoTBR87-1
    Alejandro KirkTOR880
    Brian GoodwinCHW880
    Anthony RizzoNYY781
    Jose IglesiasLAA781
    Mike TroutLAA781
    Taylor WardLAA781
    Kyle SchwarberBOS770
    Jeff McNeilNYM770
    Asdrubal CabreraARI770
    Eddie RosarioCLE770
    Josh NaylorCLE770
    Jordan LuplowCLE770
    Roberto PerezCLE770
    Harold RamirezCLE770
    Willians AstudilloMIN770
    Trevor LarnachMIN770
    Victor CaratiniSDP770
    Marcell OzunaATL770
    Eddie RosarioATL770
    Travis d’ArnaudATL770
    Joc PedersonATL770
    Connor JoeCOL781
    Edmundo SosaSTL76-1
    Adalberto MondesiKCR76-1
    Lane ThomasWSN770
    Rowdy TellezMIL770
    Andy IbanezTEX770
    Cavan BiggioTOR770
    Ke’Bryan HayesPIT76-1
    Yermin MercedesCHW770
    Adam EngelCHW770
    Francisco MejiaTBR76-1
    Phil GosselinLAA671
    Gavin LuxLAD671
    Zach McKinstryLAD671
    Matt BeatyLAD671
    Chad PinderOAK660
    Ronald TorreyesPHI671
    Alec BohmPHI671
    Christian VazquezBOS660
    Christian ArroyoBOS660
    Tucker BarnhartCIN671
    Jose PerazaNYM660
    Ramon UriasBAL671
    Josh VanMeterARI660
    Andy YoungARI660
    Dustin GarneauDET660
    Jake RogersDET660
    Wilson RamosDET660
    Zack ShortDET660
    Oscar MercadoCLE660
    Joshua FuentesCOL671
    Jake MeyersHOU660
    Abraham ToroHOU660
    Mauricio DubonSFG65-1
    Curt CasaliSFG65-1
    Josh HarrisonWSN660
    Luis GarciaWSN660
    Carter KieboomWSN660
    Jace PetersonMIL660
    Jackie Bradley Jr.MIL660
    Eduardo EscobarMIL660
    Travis ShawMIL660
    Willie CalhounTEX660
    Jason MartinTEX660
    Eli WhiteTEX660
    Lars NootbaarSTL65-1
    Jake LambCHW660
    Edward OlivaresKCR65-1
    Anthony AlfordPIT65-1
    Phillip EvansPIT65-1
    Kevin NewmanPIT65-1
    Rodolfo CastroPIT65-1
    Miguel AndujarNYY660
    Anthony RendonLAA660
    Juan LagaresLAA660
    Kurt SuzukiLAA660
    Jose RojasLAA660
    Luis RengifoLAA660
    Austin BarnesLAD660
    Bryan De La CruzMIA550
    Willy AdamesTBR550
    Mike BrosseauTBR550
    Mike MoustakasCIN561
    Raimel TapiaCOL561
    Stephen PiscottyOAK550
    Starling MarteOAK550
    Yan GomesOAK550
    Danny SantanaBOS550
    Abraham ToroSEA550
    Kyle LewisSEA550
    J.D. DavisNYM550
    Billy McKinneyNYM550
    Stephen VogtARI550
    Kole CalhounARI550
    Nick AhmedARI550
    Victor ReyesDET550
    Andres GimenezCLE550
    Kyle GarlickMIN550
    Abraham AlmonteATL550
    Ehire AdrianzaATL550
    Guillermo HerediaATL550
    Robinson ChirinosCHC550
    Jake MarisnickCHC550
    Sergio AlcantaraCHC550
    Matt DuffyCHC550
    Andrew StevensonWSN550
    Charlie CulbersonTEX550
    Jose TrevinoTEX550
    Seby ZavalaCHW550
    Leury GarciaCHW550
    Adam EatonCHW550
    Mike TauchmanSFG54-1
    Clint FrazierNYY550
    Albert PujolsLAA550
    Justin WilliamsSTL54-1
    Freddy GalvisPHI550
    Adam FrazierPIT440
    Wilmer DifoPIT440
    Pat ValaikaBAL451
    Nick FortesMIA440
    Jorge AlfaroMIA440
    Jon BertiMIA440
    Isan DiazMIA440
    Sandy LeonMIA440
    Kevin KiermaierTBR440
    Jordan LuplowTBR440
    Shed Long Jr.SEA440
    Jose MarmolejosSEA440
    Brandon DruryNYM440
    Daz CameronDET440
    Renato NunezDET440
    Daniel JohnsonCLE440
    Owen MillerCLE440
    Nick GordonMIN440
    Jurickson ProfarSDP440
    Pablo SandovalATL440
    Jose SiriHOU440
    Trayce ThompsonCHC440
    Alcides EscobarWSN440
    Keston HiuraMIL440
    John HicksTEX440
    David DahlTEX440
    Corey DickersonTOR440
    Rowdy TellezTOR440
    Zack CollinsCHW440
    Aaron HicksNYY440
    Jo AdellLAA440
    Austin WynnsBAL440
    Jason VoslerSFG330
    Jose RondonSTL330
    Matt CarpenterSTL330
    Hoy ParkPIT330
    Alex JacksonMIA330
    Elvis AndrusOAK330
    Aramis GarciaOAK330
    Kevin PlaweckiBOS330
    Travis ShawBOS330
    Jonathan ArauzBOS330
    Tomas NidoNYM330
    Harold CastroDET330
    Derek HillDET330
    Nomar MazaraDET330
    Ernie ClementCLE330
    Andrelton SimmonsMIN330
    Jake CaveMIN330
    Ben RortvedtMIN330
    Marwin GonzalezHOU330
    Michael HermosilloCHC330
    Starlin CastroWSN330
    Billy McKinneyMIL330
    Leody TaverasTEX330
    Cesar HernandezCHW330
    Mike FordNYY330
    Sheldon NeuseLAD330
    Max SchrockCIN330
    Rio RuizBAL330
    Nicky LopezKCR220
    Hanser AlbertoKCR220
    Ka’ai TomPIT220
    Cole TuckerPIT220
    Erik GonzalezPIT220
    Corey DickersonMIA220
    Josh HarrisonOAK220
    Jarren DuranBOS220
    Marwin GonzalezBOS220
    Michael ChavisBOS220
    Jake BauersSEA220
    Donovan WaltonSEA220
    Sam HaggertySEA220
    Cal RaleighSEA220
    Evan WhiteSEA220
    Josh ReddickARI220
    Jake McCarthyARI220
    JaCoby JonesDET220
    Myles StrawCLE220
    Rene RiveraCLE220
    Wilson RamosCLE220
    Jake BauersCLE220
    Rob RefsnyderMIN220
    Gilberto CelestinoMIN220
    Luis ArraezMIN220
    Webster RivasSDP220
    Austin NolaSDP220
    Jorge MateoSDP220
    Brian O’GradySDP220
    Huascar YnoaATL220
    Ender InciarteATL220
    Orlando ArciaATL220
    Stephen VogtATL220
    Myles StrawHOU220
    Taylor JonesHOU220
    Keibert RuizWSN220
    Riley AdamsWSN220
    Tres BarreraWSN220
    Jordy MercerWSN220
    Gerardo ParraWSN220
    Victor RoblesWSN220
    Daniel RobertsonMIL220
    Adrian HouserMIL220
    Jacob NottinghamMIL220
    Brock HoltTEX220
    Khris DavisTEX220
    Santiago EspinalTOR220
    Joe PanikTOR220
    Danny MendickCHW220
    Nick MadrigalCHW220
    Billy HamiltonCHW220
    Ryan LaMarreNYY220
    David FletcherLAA220
    Brandon MarshLAA220
    Luke RaleyLAD220
    Andrew KnappPHI220
    Matt VierlingPHI220
    Matt JoycePHI220
    Nick MatonPHI220
    Scott HeinemanCIN220
    Kelvin GutierrezBAL220
    Jorge MateoBAL220
    Ryan McKennaBAL220
    Yonathan DazaCOL220
    Chadwick TrompSFG110
    Logan WebbSFG110
    Andrew KniznerSTL110
    Austin DeanSTL110
    Jack FlahertySTL110
    Kyle IsbelKCR110
    Cam GallagherKCR110
    Kelvin GutierrezKCR110
    Emmanuel RiveraKCR110
    Tyler AndersonPIT110
    Michael ChavisPIT110
    Oneil CruzPIT110
    John NogowskiPIT110
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    Ralph GarzaHOU000
    Trevor MegillCHC000
    Skye BoltSFG000
    Nabil CrismattSDP000
    Evan PhillipsTBR000
    Evan PhillipsLAD000
    Tyler AlexanderDET000
    Brody KoernerNYY000
    Drew SmithNYM000
    Angel PerdomoMIL000
    Jose AlvaradoPHI000
    Jake BrentzKCR000
    Patrick WeigelMIL000
    Dillon TateBAL000
    Daniel ZamoraSEA000
    Ian GibautMIN000
    Nestor CortesNYY000
    Dennis SantanaTEX000
    Dennis SantanaLAD000
    Tayler SaucedoTOR000
    Cody PoncePIT000
    Trent ThorntonTOR000
    Tyler GilbertARI000
    Peter LambertCOL000
    Pete FairbanksTBR000
    Hoy ParkNYY000
    Tanner AndersonPIT000
    Phil MatonCLE000
    Phil MatonHOU000
    Cody StashakMIN000
    Ryan MeisingerCHC000
    Ryan HelsleySTL000
    Jay FlaaBAL000
    Jay FlaaATL000
    Anthony MisiewiczSEA000
    Art WarrenCIN000
    Jarlin GarciaSFG000
    Tyler KinleyCOL000
    Carson FulmerCIN000
    Josh SborzTEX000
    Taylor HearnTEX000
    Eric HanholdBAL000
    Alex YoungCLE000
    Alex YoungARI000
    Josh StaumontKCR000
    Nick AndersonTBR000
    Julian FernandezCOL000
    Brandon WaddellBAL000
    Brandon WaddellMIN000
    Brandon WaddellSTL000
    Jaime BarriaLAA000
    Thomas EshelmanBAL000
    Connor OvertonTOR000
    Scott EffrossCHC000
    Travis BlankenhornMIN000
    Enyel De Los SantosPHI000
    Enyel De Los SantosPIT000
    Trey AmburgeyNYY000
    Shea SpitzbarthPIT000
    Junior FernandezSTL000
    Josh RogersWSN000
    Phil BickfordMIL000
    Sam HentgesCLE000
    Logan AllenCLE000
    Travis BergenTOR000
    Travis Lakins Sr.BAL000
    A.J. MinterATL000
    Kolby AllardTEX000
    Nick NeidertMIA000
    Dillon PetersPIT000
    Miguel Del PozoDET000
    Drew SteckenriderSEA000
    Miguel DiazSDP000
    Pedro AvilaSDP000
    Beau BurrowsDET000
    Beau BurrowsMIN000
    Kyle KellerPIT000
    Zack BurdiBAL000
    Zack BurdiCHW000
    Corey RayMIL000
    Tommy NanceCHC000
    Kirby SneadTOR000
    Robert DuggerSEA000
    Brandyn SittingerARI000
    Conner MenezSFG000
    Jose QuijadaLAA000
    Mitch WhiteLAD000
    Caleb BaragarSFG000
    Wes BenjaminTEX000
    Miguel AguilarARI000
    Seranthony DominguezPHI000
    Thomas HatchTOR000
    T.J. ZeuchTOR000
    Jacob WebbATL000
    Edgar GarciaMIN000
    Edgar GarciaCIN000
    Trevor RichardsTBR000
    Trevor RichardsTOR000
    Trevor RichardsMIL000
    Joey LucchesiNYM000
    Richard LoveladyKCR000
    A.J. PukOAK000
    Daulton JefferiesOAK000
    Dean KremerBAL000
    Keegan AkinBAL000
    Shane BieberCLE000
    Yohan RamirezSEA000
    Shaun AndersonBAL000
    Shaun AndersonMIN000
    Shaun AndersonSDP000
    Michael RuckerCHC000
    Kyle FunkhouserDET000
    Bryan GarciaDET000
    Jorge AlcalaMIN000
    Aaron CivaleCLE000
    Ryan HartmanHOU000
    Andrew VasquezLAD000
    Devin SmeltzerMIN000
    Raynel EspinalBOS000
    Jacob RobsonDET000
    Jason FoleyDET000
    Ryan HendrixCIN000
    Jimmy LambertCHW000
    Stephen NogosekNYM000
    Jon DuplantierARI000
    J.D. HammerPHI000
    Geoff HartliebNYM000
    Geoff HartliebPIT000
    Bernardo Flores Jr.STL000
    David BednarPIT000
    JoJo RomeroPHI000
    Sean PoppenTBR000
    Sean PoppenARI000
    Sean PoppenPIT000
    Otto LopezTOR000
    Cionel PerezCIN000
    Jordan HicksSTL000
    Jack KrugerLAA000
    Matt FosterCHW000
    Zach ReksLAD000
    Gregory SotoDET000
    Jordan SheffieldCOL000
    Kyle CodyTEX000
    Connor SeaboldBOS000
    Patrick MurphyTOR000
    Patrick MurphyWSN000
    Dustin MayLAD000
    Kodi WhitleySTL000
    Anthony BenderMIA000
    Jonathan LoaisigaNYY000
    Reiver SanmartinCIN000
    Seth ElledgeSTL000
    Will VestSEA000
    Justin GarzaCLE000
    Anthony CastroTOR000
    Bryan BakerTOR000
    Daniel CastanoMIA000
    Riley SmithARI000
    Alexander WellsBAL000
    Darwinzon HernandezBOS000
    Michael KingNYY000
    Charlie BarnesMIN000
    Brandon BielakHOU000
    Griffin CanningLAA000
    Drew CarltonDET000
    Tucker DavidsonATL000
    Kevin GinkelARI000
    Tanner HouckBOS000
    Gabe KlobositsWSN000
    Alex LangeDET000
    Clarke SchmidtNYY000
    Adonis MedinaPHI000
    Jose SuarezLAA000
    Trevor StephanCLE000
    Jhon RomeroWSN000
    Ljay NewsomeSEA000
    Jesus LuzardoOAK000
    Jesus LuzardoMIA000
    Zach PlesacCLE000
    Bryse WilsonPIT000
    Dylan LeeATL000
    Tyler WellsBAL000
    Wyatt MillsSEA000
    John SchreiberBOS000
    Hunter OwenPIT000
    Darren McCaughanSEA000
    Adrian MorejonSDP000
    Ronald BolanosKCR000
    Sean GuentherMIA000
    Kaleb OrtBOS000
    Bailey FalterPHI000
    Miguel SanchezMIL000
    Mauricio LloveraPHI000
    James KarinchakCLE000
    Nick NelsonNYY000
    Nate PearsonTOR000
    Ty TiceTOR000
    Ty TiceATL000
    Zach PopMIA000
    Ben BowdenCOL000
    Eli MorganCLE000
    Jeremy BeasleyTOR000
    Mike BaumannBAL000
    Yennsy DiazNYM000
    Tyler IveyHOU000
    Zac LowtherBAL000
    Griffin JaxMIN000
    Mario FelicianoMIL000
    Deivi GarciaNYY000
    Jhonathan DiazLAA000
    Alec BettingerMIL000
    Andre ScrubbHOU000
    Kyle DohyPHI000
    Riley O’BrienCIN000
    Brusdar GraterolLAD000
    Ramon RossoPHI000
    Matt ManningDET000
    Bruce ZimmermannBAL000
    Andres MunozSEA000
    Tyler ZuberKCR000
    Antonio SantosCOL000
    Isaac MattsonBAL000
    Anthony KayTOR000
    Nick MargeviciusSEA000
    Luis MaderoMIA000
    Josh FlemingTBR000
    Jovani MoranMIN000
    Rony GarciaDET000
    Enoli ParedesHOU000
    Peter SolomonHOU000
    J.B. BukauskasARI000
    Casey MizeDET000
    Scott HurstSTL000
    Kyle NelsonCLE000
    Nick SandlinCLE000
    Kutter CrawfordBOS000
    JC MejiaCLE000
    Yusei KikuchiSEA000
    Cristopher SanchezPHI000
    Manuel RodriguezCHC000
    Eduard BazardoBOS000
    Francisco PerezCLE000
    Oliver OrtegaLAA000
    Emmanuel ClaseCLE000
    Seth MartinezHOU000
    Luis GilNYY000
    Thomas SzapuckiNYM000
    Demarcus EvansTEX000
    Dauri MoretaCIN000
    Miguel YajurePIT000
    Tyler PayneCHC000
    Brooks KriskeBAL000
    Brooks KriskeNYY000
    Darien NunezLAD000
    Connor BrogdonPHI000
    Lucas GilbreathCOL000
    Andre JacksonLAD000
    Jake LatzTEX000
    Glenn OttoTEX000
    Joe RyanMIN000
    Ryan FeltnerCOL000
    Kris BubicKCR000
    Shane McClanahanTBR000
    Daniel LynchKCR000
    Reiss KnehrSDP000
    Jackson KowarKCR000
    Jake CousinsMIL000
    Elvis PegueroLAA000
    Kervin CastroSFG000
    Luis OviedoPIT000
    Jose MarteLAA000
    Mason ThompsonWSN000
    Mason ThompsonSDP000
    Gregory SantosSFG000
    Camilo DovalSFG000
    Luis FriasARI000
    Angel RondonSTL000
    John KingTEX000
    Aaron FletcherSEA000
    Kyle TylerLAA000
    Ryan ViladeCOL000
    Max KranickPIT000
    Hans CrousePHI000
    Mac ScerolerBAL000
    Jon HeasleyKCR000
    Paul CampbellMIA000
    Logan GilbertSEA000
    Shane BazTBR000
    Tarik SkubalDET000
    Dylan ColemanKCR000
    Joe BarlowTEX000
    Chris RodriguezLAA000
    A.J. AlexyTEX000
    Stephen RidingsNYY000
    Nivaldo RodriguezHOU000
    Carlos HernandezKCR000
    Angel ZerpaKCR000
    Roansy ContrerasPIT000
    Luis PatinoTBR000
    Joan AdonWSN000
    Spencer HowardTEX000
    Packy NaughtonLAA000
    Codi HeuerCHW000
    Codi HeuerCHC000
    Janson JunkLAA000
    Damon JonesPHI000
    Nick SnyderTEX000
    Aaron AshbyMIL000
    Ryan DorowTEX000
    Brett de GeusTEX000
    Brett de GeusARI000
    Luis GarciaHOU000
    Justin BruihlLAD000
    Randy DobnakMIN000
    Nick AllgeyerTOR000
    Josiah GrayLAD000
    Jonathan StieverCHW000
    Cody WilsonWSN000
    Andrew WantzLAA000
    Austin WarrenLAA000
    Cooper CriswellLAA000
    Alex VesiaLAD000
    Nick MearsPIT000
    Brady SingerKCR000
    Drew RasmussenMIL000
    Alek ManoahTOR000
    Garrett CrochetCHW000
    Reid DetmersLAA000
    Spencer StriderATL000
    Kohei AriharaTEX000
    Hyeon-jong YangTEX000
    Hirokazu SawamuraBOS000

    The above table represents five properties: 1) the player’s name; 2) the team for which the player accrued his statistics; 3) his “adjusted” home run total, which uses his team’s park factor; 4) his raw home run total, which does not use his team’s park factor; and 5) “luck,” which measures the difference between his actual home run total and his adjusted home run total. If we sort the table to reflect the luckiest and unluckiest batters, their listed teams will start to reflect which ballparks have the greatest promotion for home runs and which ones do not. It’s no surprise that there are numerous Rockies players near the top of this list:

    • C.J. Cron +3 (7th)
    • Trevor Story +3 (10th)
    • Ryan McMahon +3 (12th)
    • Elias Diaz +2 (23rd)
    • Brendan Rodgers +2 (30th)
    • Sam Hilliard +2 (34th)
    • Charlie Blackmon +2 (37th)

    Interestingly enough, Colorado has the lowest home-run park factor in 2021, which means the Coors Field environment has the most positive effect on homers compared to other ballparks! Conversely, we can sort to find the “unluckiest” batsmen in the league. Here, we (expectantly) see a whole new crop of players and teams:

    1. Salvador Perez -7 (KCR)
    2. Tyler O’Neill -5 (STL)
    3. Nolan Arenado -5 (STL)
    4. Brandon Belt -5 (SFG)
    5. Paul Goldschmidt -5 (STL)
    6. Mike Yastrzemski -4 (SFG)
    7. Brandon Crawford -4 (SFG)
    8. Brandon Lowe -4 (TBR)
    9. Mike Zunino -3 (TBR)
    10. Buster Posey -3 (SFG)

    The analytical approach does seem to corroborate the traditional ideas about Coors Field while also revealing insights into which ballparks don’t induce more home runs than usual. Here are the top-5 and bottom-5 teams in this 2021 version of the home-run park factor (higher meaning worse home-run environment, lower meaning better):

    • San Francisco Giants (1st)
    • St. Louis Cardinals (2nd)
    • Kansas City Royals (3rd)
    • Pittsburgh Pirates (4th)
    • Miami Marlins (5th)
    • Los Angeles Dodgers (26th)
    • Philadelphia Phillies (27th)
    • Cincinnati Reds (28th)
    • Baltimore Orioles (29th)
    • Colorado Rockies (30th)

    [1] https://www.colorado.edu/today/2021/07/07/its-outta-here-physics-baseball-mile-high

  • NBA All-Star Power Rankings (1/2/22)

    NBA All-Star Power Rankings (1/2/22)

    (📸 ClevelandSports.org)

    Ladies and gentlemen, welcome back to another edition of my NBA All-Star Power Rankings series! Here, I’ll detail the current landscape of the league’s best players and condense my thoughts into one All-Star ballot. Players will be arranged into tiers based on their level of play, which is entirely based on the degree of impact the player provides by helping teams gain higher seeding and home-court advantage for the Playoffs. To add a final disclaimer:

    This list is not my “prediction” of the league’s actual All-Star teams lineups. This is a reflection of my evaluations of players, and where that would place them in the NBA’s award hierarchy.


    Within the league’s top-25 or so players is a group that has fully established itself as All-Star-caliber (or better). There’s little to no doubt in my mind that these players should be headed to Cleveland in February.

    • Giannis Antetokounmpo (East)
    • Jimmy Butler (East)
    • DeMar DeRozan (East)
    • Kevin Durant (East)
    • Joel Embiid (East)
    • James Harden (East)
    • Zach LaVine (East)
    • Trae Young (East)
    • Mike Conley (West)
    • Stephen Curry (West)
    • Anthony Davis* (West)
    • Luka Doncic (West)
    • Paul George* (West)
    • Rudy Gobert (West)
    • Draymond Green (West)
    • LeBron James (West)
    • Nikola Jokic (West)
    • Donovan Mitchell (West)
    • Ja Morant (West)
    • Chris Paul (West)
    • Karl-Anthony Towns (West)

    Because these are players whom I firmly believe are All-Stars, I won’t belabor any points. I will address the two asterisks next to Anthony Davis’s and Paul George’s names, which were added due to uncertainty surrounding their injury status come February. Davis and George are both currently out with a sprained MCL and a torn elbow ligament, respectively. Both will be re-evaluated in a few weeks’ time.


    The “Probably” group includes players I currently believe are All-Star-level, but I may not be entirely sold on their status. A lot of these players will make my final ballot, but whether it be injuries, a lack of signals, or general uncertainty about their playstyle, I distinguish them from those I wholeheartedly believe are All-Stars.

    • Bam Adebayo (East)

    A unique mold of player for the modern game, Adebayo is on track to replicate his All-Star success from the last two seasons. He provides a level of spacing as a threat from the mid-range (39% on 8.6 attempts per 75) to tie into a strong overall scoring package, which he pairs with elite defense that has remained on the perennial DPOY watch.

    • Bradley Beal (East)

    Beal’s outside shooting slumped out of the gate, which was an attribute of his that was crucially valuable to an elite off-ball package. Resultantly, I can’t view him as an All-Star lock, but his passing and shot creation have been trending upward, which boosts his value in a floor-raising role, also helping to mask his questionable defensive play.

    • Jrue Holiday (East)

    Holiday is a player that provides a ton of value that the box score doesn’t capture. Adjusted Plus/Minus models have always looked fondly upon his shooting, passing, and tough brand of point-of-attack defense. After a while, it becomes hard to ignore what the analytics are communicating.

    • Khris Middleton (East)

    Minutes played alongside the Greek Freak have depleted Middleton’s scoring punch so far, (Middleton averages 28.5 points per 75 on 59% True Shooting as the lone star.) but his skills have still carried over: spacing, passing, and shot creation. My only setback is how those skills map to his overall impact; the metrics are concerningly low on Middleton thus far.

    • Jayson Tatum (East)

    After a rapid ascension in 2020, Tatum seems to have stagnated slightly. His shooting and scoring have been in decline, and he doesn’t have the playmaking chops like Lillard to make up for some of that value. He’s otherwise adding some on defense. This is one of many “Playoffs will tell” cases.

    • Damian Lillard (West)

    Lillard feels like an obvious choice most other seasons. The only reason I put him here is his early-season shooting slump. He’s quickly picked up where he left off previously, but his struggles have dented his scoring effectiveness and have slightly offset the continued value he brings as an elite playmaker.


    The league’s talent pool is stacked, and as a result I nearly overwhelmed the nominal threshold of an All-Star player being within the sport’s top-25. Since there’s a numerous amount of other players that provide impact in similar vain to these “probable” stars, the “maybe” talents serve as high sub-All-Stars and potential injury replacements.

    • Jarrett Allen (East)
    • LaMelo Ball (East)
    • Darius Garland (East)
    • Fred VanVleet (East)
    • Devin Booker (West)

    Again, perhaps these players could be viewed as legitimate All-Stars. The 2022 season hasn’t fully taken shape yet, which could mean rearrangement among the previous two tiers. I’m on the absolute fence with some of these players (e.g. Booker and Conley), whether it be more lack of indicators or too little room in the actual ballot.

    Note: I’m omitting the “Not Quite There” tier from previous editions of this series due to the influx of information that comes as the season progresses.

    Final Ballot

    Once again, we reach the trickiest part of this exercise. It was difficult enough to sort the closest calls, but the task extends when putting these players into their All-Star slots. Similar to the previous editions, the ballots are structured to include two teams for either conference: five starters with two backcourt and three frontcourt players, five reserves (same restrictions), and two “wild cards” (no positional restrictions).

    Eastern Conference


    • G: James Harden
    • G: Trae Young
    • F: Giannis Antetokounmpo
    • F: Kevin Durant
    • C: Joel Embiid


    • G: DeMar DeRozan
    • G: Zach LaVine
    • F: Jimmy Butler
    • F: Jayson Tatum
    • C: Bam Adebayo

    Wild Cards:

    • W: Jrue Holiday
    • W: Khris Middleton

    The two closest calls here were 1) whether to give the last frontcourt reserves slot to Adebayo or Middleton and 2) whether to give the second wild-card slot to Middleton or Bradley Beal.

    Western Conference


    • G: Stephen Curry
    • G: Luka Doncic
    • F: LeBron James
    • C: Rudy Gobert
    • C: Nikola Jokic


    • G: Damian Lillard
    • G: Donovan Mitchell
    • F: Anthony Davis*
    • F: Paul George*
    • C: Karl-Anthony Towns

    Wild Cards:

    • W: Chris Paul
    • W: Draymond Green

    Given the injuries to Davis and George persist, the roster would be rearranged to bump Draymond Green up to a frontcourt reserve slot, give Mike Conley the vacant wild card slot, and move Kristaps Porzingis into the second frontcourt reserve slot.

    Data from:

    • Backpicks.com
    • PBPStats.com

  • Introduction to Cryptbeam Plus/Minus (CrPM)

    Introduction to Cryptbeam Plus/Minus (CrPM)

    I talk about NBA impact metrics a lot; and as most familiar with my content know, I’ve even created a few. Now, I’m ready to add another player to the mix: “Cryptbeam Plus/Minus” (CrPM). With the metric’s namesake being the title of this website, CrPM joins a long line of composite metrics that aim to identify the difference-makers of the sport through quantitative analysis. Why? As the creator of Player-Tracking Plus/Minus (PTPM), Andrew Johnson, once said: “Because what the world needs now is another all in one basketball player metric.” [1]


    CrPM draws most of its inspiration from two established impact metrics in Jacob Goldstein’s Player Impact Plus/Minus (PIPM) and Ben Taylor’s Augmented Plus/Minus (AuPM). The commonality of these three metrics is the shared “branch” of metric: I tend to differentiate between impact metrics by one of three types:

    • Box: a composite metric that is calculated using the box score only
    • Hybrid: a composite metric that is calculated using counting statistics outside the box score (e.g. player tracking and on-off data) — but may also include the box score
    • APMs: a composite metric that uses ridge-regressed lineup data as the basis of its calculation

    As for where CrPM falls under these categories, it’s formally a hybrid metric. The metric can be broken into two major component: a box score term and a plus-minus term. The box score “version” of CrPM can function as an impact metric on its own as an estimator of impact via traditionally-recorded counting stats: points, rebounds, assists, etc. Using Regularized Adjusted Plus/Minus (RAPM) as a basis, the box score is regressed onto this target to estimate a player’s per-possession impact on his team’s point differential.

    Two plus-minus statistics are then added to the box-score estimate to create CrPM: on-court Plus/Minus, which is a team’s per-100 point differential (Net Rating) with a player on the floor, and on-off Plus/Minus, which subtracts the team’s Net Rating with a given player off the floor from his on-court rating. The goal of the plus-minus component is to fill in some of the gaps left in the immeasurable, e.g. what the box score can’t capture. Testing of the model did later reinforce this idea.

    Regression Details

    The regressions for both the box score and plus-minus variants of the metric were based on fourteen years of player-seasons from Jeremias Engelmann’s xRAPM model. (This means that, in the RAPM calculation, a player’s score is pushed toward the previous season rather than zero.) This provided a more stable base for the regression to capture a wider variety of player efficacy in shorter RAPM stints. Additionally, the penalization term for each season was homogenized to improve season-to-season interpretability.

    The box score is not manipulated in the CrPM calculation outside of setting the stats as relative to league averages. The raw plus-minus terms, however, use minutes played as a stabilizer to draw results closer towards zero as playing time decreases. While some incorrectly label a larger sample size as more accurate, this adjustment serves to reduce variance in smaller samples to decrease the odds for larger error in these spots. These two branches of NBA statistics combine to create CrPM. [2]

    The regressions, especially the box score ones, were all able to explain the variability in the target RAPM with a surprising degree of accuracy. The R^2 values for combined in and out-of-sample RAPM ranged from 0.725 to 0.750 for the box-score metrics and bumped up to 0.825 with plus-minus included. A large concern for most ordinary linear regressions is heteroskedasticity, which is when error rates become larger as the predicted variable increases. The breadth of performance captured in the RAPM target mitigated this, and CrPM serves as an accurate indicator of impact for average players and MVP players alike.

    • Target response of RAPM was collected from Jeremias Engelmann’s website.
    • Box scores and plus-minus data were collected from Basketball-Reference.

    Current MVP Ladder

    Because CrPM serves as an indicator of a player’s impact, it can do a solid job of identifying viable candidates for the league’s MVP award. Through the 18th of December, here are the top-10 players of the 2022 season with at least 540 minutes played per the metric:

    1. Nikola Jokic (+11.8)

    The reigning MVP has started his follow-up campaign with a bang, and looks to be the frontrunner to snag the award again. His placement in the box-score component of the metric (+12.4 per 100) would be first among all players since 1997 with at least 1,500 minutes played during the season. Jokic’s overall score of +11.8 in CrPM is tied with LeBron James’s legendary 2009 seasons for the best regular season on record.

    2. Giannis Antetokounmpo (+9.4)

    Voter fatigue in recent seasons has downplayed the regular-season greatness of the Greek Freak, but he seems as good as ever according to CrPM. Each of his last three seasons are top-45 seasons since 1997 per the metric, with his dominating MVP run in 2020 ranking fourth (+10.8) among all high-minute players on record.

    3. Joel Embiid (+6.6)

    The metric suggests Embiid provides massive two-way value, with his marks on offense and defense being nearly identical in both the box-score and plus-minus versions of the metric. Despite a shooting slump to start the season that followed an exceptional mid-range campaign for the big man, he still adds a ton of value in Philadelphia. So far, the 76ers outscore opponents by +4.3 per 100 when Embiid is on the floor and are +9.2 points better with him in the lineup.

    4. Rudy Gobert (+6.5)

    It’s not an NBA regular season nowadays without the analytics looking upon Rudy Gobert with perhaps a little too much enthusiasm. Regardless, he’s still a surefire candidate for the DPOY Award and a probable finalist, if not winner. Almost all of his impact comes from the defense end, adding only +0.4 points per 100 in the offensive component of CrPM, with the remaining +6.1 points coming from his all-time-level rim protection and paint anchoring.

    5. Stephen Curry (+5.7)

    Curry’s plus-minus portfolio isn’t as transcendent as it was during his all-time seasons in the mid-2010s, but a revamped Golden State roster that amplifies his strengths boosts those numbers into the upper echelon of NBA superstars once more. The Warriors outscore opponents by +14.1 points per 100 with Curry on the floor and are +12 points better with him on the court. While the box score can’t capture all of the value he brings to the table, Curry still looks like one of the best players in the league according to CrPM.

    6. Kevin Durant (+5.5)

    Curry’s former teammate is another shooting savant who continues to string together legendary offensive seasons with league-best marks in both scoring volume and efficiency. He’s arguably the greatest mid-range shooter in NBA history and makes Brooklyn’s offense great with these shots alone. Speaking of, the Nets look like a surefire title contender with Durant on the floor, when they outscore opponents by +7.3 points per 100 possessions.

    7. Clint Capela (+4.6)

    While I don’t think Clint Capela has been a top-10 player in the league this year, his placement illustrates arguably the biggest sign of caution I’d address when using CrPM. Because the box-score component receives a whole lot of the weight in the plus-minus-included formula, the metric is overly sensitive to defensive rebounds and stocks. Especially in the modern league, when spacing affects statistics like rebounds to a higher degree than ever, Capela is a case of, while being a valuable player nonetheless, a stylistic disadvantage of CrPM.

    8. Jarrett Allen (+4.5)

    I’m similarly not as fond with Allen’s actual ranking among the league’s best, but he’s been sneakily good this season. The Cavaliers have clearly stocked up on Michael’s Secret Stuff for 2022, because they’ve been better than the Nets with Durant when they put Jarrett Allen in the game. Cleveland outscores its opponents by +8.4 points per 100 with Allen in the lineup! One of the sport’s emerging two-way talents receives well-deserved credit in the analytics.

    9. Karl-Anthony Towns (+4.4)

    Towns has played like an all-league star for a while, and now that his longtime home of Minnesota has found its footing in 2022, his value is more evident than ever. CrPM views Towns as a clear value-add on offense and defense, and this is enough to make him look like a candidate for the All-NBA second team this year. He’s one of the On-Off kings so far, posting a +13.1 Net Plus/Minus in a similar vain to other top-player candidates.

    10. Jimmy Butler (+4.4)

    If this list were box-score only, Butler would be several spots higher with his +6.3 score in Box CrPM; however, plus-minus doesn’t look upon him like the other players on this list entering 2022. Miami plays like a surefire postseason team with him on the court, but they actually perform +2.4 points better with him off the floor! While this is very, very likely just noise that accompanies most plus-minus data like this, it doesn’t serve as a very good indicator of his impact, thus compressing his score in plus-minus-included CrPM.

    Full 2022 Leaderboard

    Nikola JokićDEN247837.93.911.8
    Giannis AntetokounmpoMIL268495.53.99.4
    Joel EmbiidPHI196293.33.36.6
    Rudy GobertUTA299200.46.16.5
    Stephen CurryGSW289625.20.55.7
    Kevin DurantBRK2710005.30.25.5
    Clint CapelaATL298640.24.44.6
    Jarrett AllenCLE289131.92.64.5
    Karl-Anthony TownsMIN289633.01.44.4
    Jimmy ButlerMIA186074.20.24.4
    LaMelo BallCHO258283.21.24.4
    DeMar DeRozanCHI248464.9-0.64.3
    Trae YoungATL299906.2-2.04.2
    Montrezl HarrellWAS317993.30.94.2
    Myles TurnerIND30887-
    John CollinsATL299392.41.53.9
    LeBron JamesLAL186673.10.73.8
    Chris PaulPHO289063.60.33.8
    Jusuf NurkićPOR307520.63.23.8
    Dejounte MurraySAS289632.21.63.8
    Donovan MitchellUTA289104.1-0.63.5
    Jonas ValančiūnasNOP319801.52.03.5
    Domantas SabonisIND3110571.91.53.4
    Jrue HolidayMIL258193.00.43.3
    Kristaps PorziņģisDAL216341.41.93.3
    Jaren Jackson Jr.MEM297870.23.13.3
    Jayson TatumBOS3010952.30.93.2
    Andre DrummondPHI29571-
    Fred VanVleetTOR2810602.70.43.1
    Anthony DavisLAL279551.02.23.1
    LaMarcus AldridgeBRK255901.81.33.1
    D’Angelo RussellMIN247812.90.13.0
    Miles BridgesCHO3111351.91.02.9
    James HardenBRK269421.90.92.8
    Richaun HolmesSAC225961.51.32.8
    Al HorfordBOS247110.22.42.6
    Luka DončićDAL217352.30.22.5
    Bobby PortisMIL257110.52.02.5
    Alperen ŞengünHOU29538-
    Jarred VanderbiltMIN28682-
    Daniel GaffordWAS28601-
    Damian LillardPOR248733.9-1.62.3
    Patrick BeverleyMIN215521.30.82.1
    Ja MorantMEM196193.0-0.92.1
    Wendell Carter Jr.ORL308650.21.82.0
    Evan MobleyCLE25840-
    Mike ConleyUTA267273.2-1.21.9
    Deandre AytonPHO206250.51.51.9
    Malcolm BrogdonIND258832.7-0.81.9
    Devin BookerPHO216763.1-1.21.9
    Robert WilliamsBOS23638-
    Derrick RoseNYK266362.2-0.41.8
    Paul GeorgeLAC248610.61.21.8
    Darius GarlandCLE299913.0-1.31.8
    Draymond GreenGSW28849-
    Jakob PoeltlSAS216060.51.11.6
    Nikola VučevićCHI20663-
    De’Anthony MeltonMEM27654-
    Bam AdebayoMIA185920.41.11.5
    Ricky RubioCLE318801.20.31.5
    Brandon IngramNOP248552.3-0.81.5
    Cole AnthonyORL237851.9-0.51.4
    Alex CarusoCHI24685-
    Mo BambaORL27774-
    Monte MorrisDEN298752.4-1.11.3
    Zach LaVineCHI279483.1-1.81.3
    Shai Gilgeous-AlexanderOKC258731.9-0.61.2
    Jalen BrunsonDAL277952.7-1.51.2
    Pascal SiakamTOR175861.10.11.2
    Mitchell RobinsonNYK27653-
    Aaron GordonDEN299451.2-0.21.1
    Devin VassellSAS235590.11.01.1
    Tyus JonesMEM306341.8-0.71.1
    Cedi OsmanCLE255521.3-0.21.1
    Kevon LooneyGSW30564-
    Deni AvdijaWAS31672-
    Christian WoodHOU28888-
    Desmond BaneMEM308631.3-0.31.0
    Anthony EdwardsMIN2810060.50.51.0
    Khris MiddletonMIL216501.00.01.0
    Immanuel QuickleyNYK296331.8-0.90.9
    Andrew WigginsGSW299011.7-0.80.9
    Gary Trent Jr.TOR279330.60.20.8
    Ivica ZubacLAC30742-
    Kyle AndersonMEM26565-
    Alec BurksNYK297740.40.30.6
    Caris LeVertIND236681.3-0.70.6
    Cody MartinCHO297990.10.40.6
    Tyrese HaliburtonSAC289300.10.40.5
    Larry Nance Jr.POR30650-
    Scottie BarnesTOR279730.10.40.5
    Tyrese MaxeyPHI289691.9-1.50.5
    Josh HartNOP247560.10.30.4
    Gordon HaywardCHO3110521.4-1.00.4
    CJ McCollumPOR248481.4-1.10.3
    Mikal BridgesPHO289630.30.00.3
    Tobias HarrisPHI217200.7-0.40.3
    Lonzo BallCHI27958-
    Jordan ClarksonUTA297301.3-1.00.2
    Steven AdamsMEM30754-
    OG AnunobyTOR165880.5-0.30.2
    Marcus SmartBOS29991-
    Kelly Oubre Jr.CHO319031.1-1.00.1
    Lauri MarkkanenCLE226640.3-0.20.1
    Kyle LowryMIA289620.9-0.90.1
    Devonte’ GrahamNOP288761.3-1.20.1
    Will BartonDEN258250.6-0.60.0
    Grayson AllenMIL308750.7-0.70.0
    Derrick WhiteSAS288750.3-0.4-0.1
    T.J. McConnellIND245810.4-0.5-0.1
    Franz WagnerORL319940.3-0.4-0.1
    Jerami GrantDET24797-0.10.0-0.2
    Danny GreenPHI23558-2.01.8-0.2
    Russell WestbrookLAL3010780.2-0.4-0.2
    Mason PlumleeCHO22562-1.61.5-0.2
    Patty MillsBRK309051.6-1.8-0.2
    Luke KennardLAC308700.8-1.1-0.2
    Herb JonesNOP28766-1.71.4-0.3
    Bradley BealWAS2810051.6-1.9-0.3
    Jae CrowderPHO28789-1.51.2-0.3
    Nassir LittlePOR26603-1.61.3-0.3
    Keldon JohnsonSAS278290.1-0.5-0.4
    George HillMIL266860.1-0.5-0.4
    Royce O’NealeUTA27833-1.40.9-0.5
    Bojan BogdanovićUTA298641.8-2.2-0.5
    Jordan PooleGSW288600.8-1.2-0.5
    Reggie JacksonLAC309921.1-1.6-0.5
    Gabe VincentMIA275320.3-0.8-0.5
    Jae’Sean TateHOU30844-0.80.2-0.6
    Cade CunninghamDET23745-1.71.1-0.6
    Lonnie WalkerSAS27611-0.1-0.5-0.6
    Norman PowellPOR268251.0-1.6-0.6
    Terry RozierCHO227080.5-1.1-0.7
    Bogdan BogdanovićATL205640.5-1.2-0.7
    Josh RichardsonBOS225550.1-0.8-0.7
    Shake MiltonPHI256360.0-0.8-0.8
    Carmelo AnthonyLAL30828-0.3-0.5-0.8
    Georges NiangPHI286620.2-1.0-0.8
    Anfernee SimonsPOR266181.3-2.2-0.8
    Harrison BarnesSAC258360.2-1.1-0.9
    De’Aaron FoxSAC299930.5-1.4-0.9
    Matisse ThybullePHI23555-3.62.7-0.9
    Danilo GallinariATL26568-0.1-0.8-0.9
    Isaiah StewartDET26668-2.71.8-0.9
    Cameron JohnsonPHO28682-0.6-0.3-0.9
    Raul NetoWAS30607-0.2-0.8-0.9
    Seth CurryPHI279291.1-2.0-1.0
    Pat ConnaughtonMIL32929-0.4-0.6-1.0
    Dennis SchröderBOS278940.8-1.9-1.0
    Kevin HuerterATL287740.2-1.3-1.0
    Malik MonkLAL28666-0.2-0.8-1.0
    Tyler HerroMIA258220.5-1.5-1.1
    Buddy HieldSAC308570.0-1.1-1.2
    Julius RandleNYK301063-1.30.0-1.2
    Joe InglesUTA297180.2-1.5-1.3
    Spencer DinwiddieWAS267620.2-1.4-1.3
    Grant WilliamsBOS28618-1.0-0.3-1.3
    Tim Hardaway Jr.DAL28878-0.1-1.2-1.3
    Precious AchiuwaTOR22579-2.81.3-1.4
    Terance MannLAC29829-0.5-1.1-1.6
    Josh GiddeyOKC26779-2.20.6-1.6
    Chris DuarteIND29843-1.0-0.6-1.6
    P.J. TuckerMIA30856-1.2-0.5-1.6
    Bruce BrownBRK24539-2.50.9-1.7
    Kyle KuzmaWAS29937-2.30.6-1.7
    Dorian Finney-SmithDAL28899-1.80.0-1.8
    Davion MitchellSAC29744-0.3-1.6-1.9
    Robert CovingtonPOR30822-4.22.2-2.0
    Luguentz DortOKC26841-0.3-1.7-2.0
    Eric GordonHOU257450.0-2.0-2.0
    Furkan KorkmazPHI27600-1.0-1.1-2.1
    Nickeil Alexander-WalkerNOP31877-1.3-0.8-2.1
    Kevin Porter Jr.HOU19574-2.20.1-2.2
    Dwight PowellDAL28536-1.8-0.4-2.2
    Duncan RobinsonMIA30849-1.3-0.9-2.2
    Isaac OkoroCLE23663-1.3-1.0-2.3
    Jeff GreenDEN29732-0.9-1.4-2.3
    Eric BledsoeLAC30787-2.4-0.1-2.5
    Chuma OkekeORL25562-3.51.1-2.5
    Cam ReddishATL25558-1.5-1.0-2.5
    Landry ShametPHO27562-0.6-2.0-2.5
    RJ BarrettNYK25784-1.4-1.2-2.5
    Kentavious Caldwell-PopeWAS31906-2.0-0.5-2.6
    Justin HolidayIND25693-1.2-1.5-2.7
    Darius BazleyOKC28765-4.51.9-2.7
    Frank JacksonDET28620-0.6-2.1-2.7
    Killian HayesDET23602-2.90.1-2.9
    Facundo CampazzoDEN28567-1.9-1.0-2.9
    Jeremiah Robinson-EarlOKC28608-2.90.0-3.0
    Jalen SuggsORL21583-3.1-0.1-3.2
    Saddiq BeyDET28897-2.2-1.1-3.3
    Evan FournierNYK30857-1.6-1.8-3.4
    Jaden McDanielsMIN26646-3.90.5-3.4
    Malik BeasleyMIN29750-1.7-1.8-3.4
    R.J. HamptonORL29536-2.4-1.1-3.5
    Garrett TempleNOP29532-4.20.5-3.6
    Avery BradleyLAL26599-3.1-0.9-4.0
    Gary HarrisORL24651-2.1-2.2-4.3
    Terrence RossORL28715-2.4-2.3-4.8
    Reggie BullockDAL27646-3.1-1.7-4.8
    Jalen GreenHOU18555-3.9-2.6-6.5

    Updated Dec. 18, 2021

    [1] Read Johnson’s article, a primer on PTPM, here.

    [2] Because Basketball-Reference doesn’t report plus-minus for offense and defense as liberally as it does combined plus-minus, the offensive / defensive splits for CrPM are slightly less accurate than its total version.

  • NBA All-Star Power Rankings (11/8/21)

    NBA All-Star Power Rankings (11/8/21)

    (📸 ClevelandSports.org)

    As the dust of the early season starts to settle, albeit to a degree that leaves lots to be desired, it’s around the time we begin to think about how the upcoming All-Star teams will take shape. With twelve spots to fill in each conference, the following excerpts will detail my current selections for the teams based on how these players are providing material, observable impact that helps teams win basketball games.

    Similar to my previous All-Star post for last season, players will be sorted into tiers based on my evaluations of their degree of impact, with “better” players being more likely to make the final ballot while some players may be on the fringe, fighting with similarly valuable players for the final spots.


    The tier of “absolutely” consists of players for whom I have minimal doubt are playing at an All-Star level or better. Namely, if they either sustain strongly resemble their current level of play, they will continue to make my succeeding ballots.

    • Giannis Antetokounmpo (East)
    • Jimmy Butler (East)
    • Stephen Curry (West)
    • Luka Doncic (West)
    • Kevin Durant (East)
    • Joel Embiid (East)
    • Paul George (West)
    • Rudy Gobert (West)
    • Nikola Jokic (West)
    • Donovan Mitchell (West)
    • Karl-Anthony Towns (West)
    • Trae Young (East)

    I pegged all of these players as All-Stars or better last year, meaning there are no newcomers so far. Compared to last year’s All-Star post (about a month into the season), this tier is thinned out, which is consistent with staying wary of the early season; the target of this exercise is to recognize tangible value that players provide to basketball teams, and each of these players provides established All-Star value.


    The “probably” tier is interesting. I’ve described many of these players as All-Star level or better in the past before, and will also likely remain All-Star-type players or better in my evaluations at the end of the season. However, there’s something to be missed in their performance so far, whether it’s aging, slumps, or uncertainties about their impact.

    • Bam Adebayo (East)
    • Devin Booker (West)
    • Mike Conley (West)
    • Anthony Davis (West)
    • James Harden (East)
    • LeBron James (West)
    • Zach LaVine (East)
    • Damian Lillard (West)
    • Ja Morant (West)
    • Chris Paul (West)
    • Jayson Tatum (East)

    The sore spot of this tier is clearly LeBron James. After nearly two decades of MVP-level play, we’re very likely partway through the beginning of the end of his reign of terror. Aging isn’t on his side, and thus he’s not pressuring the rim or attacking defenses through his passing in the same manner he would during his annual Playoff ascensions.

    A few of these names are mostly obvious All-Star performers, such as Anthony Davis and Damian Lillard. Whether it be rustiness due to injury or unlucky shooting, there’s that small degree of uncertainty that loosens their cases for the 2022 season. The remainder of the tier consists of either lower-level All-Star players or strong fringe members, such as Zach LaVine and Ja Morant.


    While some of these players are of comparable value to those in the tier above, most of these players are closer to injury replacements than legitimate All-Star contributors.

    • LaMelo Ball (East)
    • Bradley Beal (East)
    • Shai Gilgeous-Alexander (West)
    • Montrezl Harrell (East)
    • Tobias Harris (East)
    • Jrue Holiday (East)
    • Kyle Lowry (East)
    • Khris Middleton (East)
    • Domantas Sabonis (East)

    A name I feel the need to address here is Montrezl Harrell. With the benefit of hindsight that will come in the following months, I heavily doubt he will stay in this tier, but there are intriguing signals. He’s not overly dependent on perimeter creation as a finisher, and he’s had a significant spike in free-throw rate, drawing fouls and providing hyper-efficient scoring. It also doesn’t hurt that the impact metrics absolutely adore him.

    LaMelo Ball is a player I felt quite comfortable placing in this tier. I don’t think his outside shooting is sustainable, but his passing is off the charts and his shot creation has steadily improved from last season. As he continues to add value in the big-two skill sets of scoring and playmaking, he’ll develop into a viable offensive engine who can quarterback strong efforts in the Playoffs.

    “Not quite there”

    As the name suggests, this tier recognizes players that are more of honorable mentions that serious All-Star candidates. Namely, these are the players who I felt the need to consider in the process of creating my ballot; but after further research, decided they were more appropriately pegged closer to sub-All-Star level.

    • Miles Bridges (East)
    • Jaylen Brown (East)
    • John Collins (East)
    • DeMar DeRozan (East)
    • Draymond Green (West)
    • Brandon Ingram (West)
    • Dejounte Murray (West)
    • Julius Randle (East)
    • Russell Westbrook (West)

    Draymond Green is a player I’ve praised in the past for his heroic defensive efforts, snappy decision-making, and crafty transition passing, but I struggle to the see the confirmation that his impact is surely All-Star level. I suspect it’s probable he’s bumped up as the season goes on (even so far, his scoring has been somewhat adequate), but for now I’ll rank him as a very strong sub-All-Star-type player.

    Dejounte Murray was a player I was encouraged to stack up against Ja Morant in recent games, and while I see evidence that his passing and manipulation of the defense has grown, he doesn’t have the scoring punch and resulting threat to generate lots of offense for his teammates. I’ve also grown less fond of his defensive rotations and overarching off-ball defense. Regardless, Murray is a surefire candidate for a sub-All-Star team.

    Final Ballot

    Here’s the tricky part: condensing all of these tiers into the structure of an All-Star ballot. As stated earlier, there will be twelve players in each conference: five starters (two frontcourt and three backcourt players), five reserves (two frontcourt and three backcourt players), and two wildcards.



    • G: James Harden
    • G: Trae Young
    • F: Giannis Antetokounmpo
    • F: Kevin Durant
    • C: Joel Embiid


    • G: LaMelo Ball
    • G: Zach LaVine
    • F: Jimmy Butler
    • F: Jayson Tatum
    • C: Bam Adebayo


    • W: Kyle Lowry
    • W: Khris Middleton

    The hardest cut for me to make was the last guard slot on the reserves, which I gave to LaMelo Ball. The obvious candidates in his place were Bradley Beal and Kyle Lowry, the latter of which I gave a wildcard spot. I don’t think it’s impossible for Bradley Beal to rise on this ballot, but I’m concerned by his continuously declining outside shooting and lack of playmaking prowess next to players like Ball and Lowry.



    • G: Stephen Curry
    • G: Luka Doncic
    • F: Paul George
    • C: Rudy Gobert
    • C: Nikola Jokic


    • G: Damian Lillard
    • G: Donovan Mitchell
    • F: Anthony Davis
    • F: LeBron James
    • C: Karl-Anthony Towns


    • W: Ja Morant
    • W: Chris Paul

    Leaving Devin Booker and Mike Conley off my final ballot was a tough choice to make; the guard position in the West is simply too stacked for enough room to be made available. (And like I said, Booker and Conley are probably All-Star guys.) Aside from this debacle, I was pleased with my selections for the West. Gobert and Jokic as centers in the starting lineup felt slightly awkward, but a player like Gobert has way too much regular-season defensive value to leave off this type of ballot.