• MVP Power Rankings | Volume I

    MVP Power Rankings | Volume I
    Premise

    Historically, the MVP has been chosen arbitrarily—a mingling of analysis and intuition. This can be great by promoting varying styles of analysis. Broader conversations can launch new names into conversations. Different ideas challenge norms. For this list, I adhere to a strict criteria I’ve developed over the years—an amalgam of analyzing film, statistics, and value-theory. The overarching question I try to answer with these rankings is how well a player sets up a random team to win the championship. (This approach is derivative of the works of analysts like Seth Partnow and Ben Taylor.)

    This still limits consideration to the regular season. Expectations for Playoff risers and fallers is irrelevant. I solely care about how well a player sets the team up to succeed in the Playoffs (where “things matter most”). This means actual seeding is less important, insofar as home-court advantage doesn’t play a crucial role in later rounds. To balance these factors, I concocted a championship odds calculator that inputs estimates of player value and games played. (The impact estimates are based on analysis and interpretation.)

    The Ladder

    10. Ten

    The tenth spot is such a toss-up that I may as well treat it like an Honorable Mentions! Names considered for this spot include, as alphabetized:

      • Devin Booker
      • Jimmy Butler
      • Paul George
      • Shai Gilgeous-Alexander
      • James Harden
      • Ja Morant
      • Karl-Anthony Towns

    9. Donovan Mitchell

    Mitchell has upped his offense to a new level. The stylistic aspect of his game is consistent, but its efficiency is unbelievable. He attacks the rim like a madman and creates tons of offense for the corners. (Now we know Gobert wasn’t the problem.) There’s his shooting… my goodness. His defense has also been serviceable too; after multiple seasons of questionable play on that end, his anticipation and ability to clog passing lanes look better than last year. I don’t see evidence that he’s a clear negative.

    8. Anthony Davis

    Anthony Davis is… almost back? Not quite there. He’s somewhat close to his 2020 form, though it’s unlikely he reaches it. Any semblance of a jump shot he had during the bubble has recessed, which diminishes his spacing value. He’s also held back on the passing punch he had a few years ago. Regardless, he’s the Defensive Player of the Year in my eyes. Davis has an unmatched combination of valuable and scalable traits on that end, and… my gosh. Name any defensive skill and he’s got it.

    7. Jayson Tatum

    Tatum is having the most efficient season of his career despite a slight drop in three-point shooting! His shot selection and isolationism are both upgraded from last season. He looks better as an athlete, and that physical aid may help him ascend to “obligatory wing defender in DPOY conversations” territory! (No, seriously, his ball-pressure is some of the most impressive defensive work in the league.) Tatum has a chance to win the actual award because he plays for the game’s best team. On my list, he lands seventh.

    6. Giannis Antetokounmpo

    This is… weird. He’s still a prolific scorer and playmaker with Defensive Player of the Year chops on the other end. Those things alone make him a mainstay on the upper end of a list like this. However… his offense has come with a lot of opportunity cost so far. Antetokounmpo’s shot outside the paint is lagging hard to start the season, and it drags his overall efficiency below the league average. That’s enough for the major impact metrics to peg him in this range rather than finalist territory.

    5. Joel Embiid

    Embiid is probably not in the uppermost echelon of scorers in my book. (Even though he’s an incomprehensible isolation scorer.) But as his passing ability trickles in from more opportunities to create, his overall offense looks stronger. He’s not what I’d consider a really “good” passer, but he has enough range on his deliveries to be the primary force on a contending offense. Embiid is still a monster rebounder and low-post defender whose skills check more boxes than most other players in the world.

    4. Luka Doncic

    Doncic is probably (?) the favorite to win the award, so this may be a surprising placement. I’m not sold on “Luka-Ball” as the next model for great offense. The term “heliocentrism” is thrown around like pennies into a wishing well, but Doncic’s role in the current Mavericks roster demonstrates a drawback. Dallas tends to wear down late in games. Doncic is the most prolific decision-maker in the league, and it seems his relative lack of conditioning weakens the entire attack down the stretch. Regardless, he’s still fourth for me due to an unheralded mix of volume scoring and creation for teammates.

    3. Kevin Durant

    Durant’s relative True Shooting percentage is 7.7% ahead of the league; once his three-point percentage is up to speed, that number is going to skyrocket his name into contention for the best overall scoring numbers in the league. His un-guardable jump shot is still intact. He’s having the best passing season of his career to my eye—slicing out holes in perimeter coverages with more anticipation and stronger deliveries than ever. Add that to a positive defensive package, and you’ve got a strong MVP candidate.

    2. Nikola Jokic

    Jokic was the clear-cut MVP of the last two years. His scoring volume isn’t quite up to par yet, and voter fatigue is due to trickle in from the public and voters alike. That’s my estimation as to why he isn’t seen as an obvious finalist. Jokic is the most efficient of the game’s volume scorers, and he’s the best passer on the planet. He’s arguably the most complete offensive player in history. His rim protection as Denver’s primary backline defender still lags behind, which is the only thing holding him back from the top.

    1. Stephen Curry

    My, oh my… Where to start? Curry is “re-peaking” as the league’s top scorer and shooter, with more than a little airspace above the contenders. His leading spot is probably dependent on a permanent upgrade in his scoring around the rim, where he’s converting at a 76% rate. He has the craft and guile, but likely not the durability and positive aging signals to maintain it for long. (But I’m partial to streakier two-point than three-point improvements.) He’s otherwise Curry doing Curry things, and he’s at the top of virtually every major one-number metric.

    Resources

    [1] Data from Basketball-Reference, BBall-Index, Thinking Basketball


  • The Lauri Markkanen Corollary

    The Lauri Markkanen Corollary

    The Utah Jazz have received a lot of press in these past four weeks. The most surprising team to start the season, are they not? Perhaps. They are (as of the time of this writing) the fourth seed in the West with a positive point differential—worth a double take considering they were seen as a contender to land Victor Wembanyama. “But what does this have to do with Lauri Markkanen?” you ask. That’s a decent question—and the answer has everything to do with All-Star voting! Let’s take a dive into what in the Julius Randle is going on.

    The Premise

    Historically, All-Stars are selected through a mingling of their performances and their teams’ standings. This is great news for Lauri Markkanen. Why? Let me begin with an assertion—Lauri Markkanen is not an All-Star-level player, but… because of the Jazz’s record and preseason expectations, he might stand a chance to make the All-Star Game. “But… but…” you ask, “if Lauri Markkanen isn’t an All-Star-level player, why does he deserve to make the All-Star Game?”

    I’m so glad you asked.

    From the perspective that players should be rewarded for their ability or skill, the boost that some receive from being in the right situation is stupid. I’ve already fleshed this out (read here) so I won’t belabor any points. How this is relevant has lots to do with Lauri Markkanen’s case because, like I said, he doesn’t demonstrate All-Star skill but his name is littered in All-Star conversations. This was obvious when I published my first All-Star ballot of the season. My omission of Markkanen from either tier of All-Star consideration implied I did not consider him an All-Star-level player. This is correct. Resisting comments were quick to defend Markkanen’s case:

    I clearly disagree with this, don’t I? And what’s the best way to further a point in a caring and considerable manner?—Making the opposing argument as strong as could possibly be.

    To the best of my ability, I am going to lay out Lauri Markkanen’s All-Star case—but with a twist. I still only care about his efficacy as a player. Team, teammates, and attributes of the Utah Jazz’s system are irrelevant. I believe All-Stars deserve their recognition for playing like All-Stars, and Markkanen won’t receive a special treatment. So let’s get into it!

    Scouting Report

    Markkanen has demonstrated solid three-point shooting during his career. His three-point percentage of 36.5% is unspectacular, and right in line with his career average of 36.4%. To me, he provides value as a shooter away from the ball—a stretch four who can catch and shoot at an above-average clip (40.2%), mostly in pick-and-pop situations. On the other hand, he’s an infrequent and inefficient pull-up shooter—a skill crucial to a player’s ability to create offense for teammates in a spaced-out offense. The Jazz have methodically had him work from the corners, spots (in which his three-point percentage is 45.8%) that are valuable real estate. The corners are also outlets for his attacks to the paint.

    Markkanen is tall, strong, and sturdy with a fine-tuning of footwork that creates separation between defenders in the paint. He has spins, twists, and twirls that carve out floater-range shots. (Markkanen’s percentage of attempts that are floaters had nearly doubled from its previous high.) Farther downhill, he’s a solid finisher who can draw fouls and convert at high rates—driving about five times per game and finishing at a 76% rate. Markkanen does require “assistance” from teammates’ passes at times; he has burst as a big man, but not enough to get to the rim at will. (He often picks up his dribble only a few steps into the paint.) His bruising and jostling allows him to contend with formidable big men like Bam Adebayo and Anthony Davis close to the basket.

    This is where his All-Star case becomes tricky. His scoring punch has been good—not great, for all intents and purposes—without a strong isolation package or slashing ability. His style is suited to play alongside a more demanding offensive force, a truer “number one” who can leverage the pick-and-pop and make timely passes when Markkanen cuts baseline. That’s a pretty good scoring package, but where it falls short is in its ability to boost the value of his lackluster passing. He’s had flashes of range and accuracy, but nothing that—when sifted through—indicates he’s growing into the role of a playmaker. (For instance, the percentage of Markkanen’s assists that end in layups is 21% below the league average.)

    His defensive skills are slightly fuzzier to me. Markkanen defends a fair amount of shots at the rim, inducing misses without a high block rate. But any skills he has a rim protector have yet to translate to latent value, such as deterring shots at the rim. Teams are content to attack the rim with Markkanen on the floor, which could be a problem due to his nonexistent presence as a perimeter defender. He exemplifies the Jazz’s inconsistent switch tactics, so he doesn’t content many threes nor is he an avid helper. Without brushing up on his defensive range—which seems unlikely to happen in Utah—Markkanen’s argument as a clearly positive to strong defender seems weak.

    The Jazz offense is a suitable place for him to mimic his ideal offensive role: a pick-and-pop, bruising, floater-range specialist who can score at two levels. In Utah, he’s a semi-frequent but undesirable pick-and-roll ball-handler, which is an action teams would want to avoid to build a strong offense. Paired with weak passing, I see Markkanen as a solid third-to-fourth option on a contending offense. Defensively, he’s going to need backline help from a stronger, rangier rim protector; and if he’s the primary rim protector, his team will need to bank on strong defensive play from guards to prevent open three-pointers.

    Here are Markkanen’s ranks in high-level impact metrics [1]:

      • Backpicks BPM +2.6 (48th)
      • Basketball-Reference BPM +2.8 (36th)
      • Box RAPTOR +1.7 (80th)
      • EPM +4.7 (23rd)
    The Strongest Case

    What is Markkanen’s upper bound?—the highest extent to which I can evaluate his impact. If that estimate doesn’t match All-Star level, by the rules of this exercise, Markkanen has no case to be an All-Star. I’ll view each of skills through the rosiest of glasses, give him the benefit of the doubt in all reasonable areas (considering the trade-offs between skill interactions). To start, here are Markkanen’s strengths as a player, by my scouting report:

      • Floater-range footwork
      • Bullying in the paint
      • C&S and screening at the corners
      • Complementary rim protection

    I can’t reasonably upgrade his passing, nor is his off-ball package enough for me to say he’d be a “number two” on a contending offense. For that, he’d need rangy, connective passing to and from his corner spots. (In theory, these could lead to more layup passes.) His footwork and physical attributes can dismiss the notion that his scoring near the basket “will eventually cool down.” This version of his scoring—high volume on high efficiency—could be here to stay.

    Defensively, I’m still convinced he needs backline help. His opponents are finishing at a low clip when he defends at the hoop, but it doesn’t justify Markkanen’s sedentary defensive role. He could probably help keep a poorer defensive afloat—but without a flank of perimeter defenders or a better rim protector as a failsafe, Markkanen’s defensive package is neither good nor bad. This all goes to say I see a limited ceiling on how highly I can evaluate his defense.

    Markkanen’s impact metrics are inconclusive. EPM, which includes tracking data and plus-minus, pegs him at an All-Star level. But RAPTOR also includes these parts (in a varied fashion) and indicates he’s not close to contention! The box-score metrics both agree he’s outside contention. These metrics, in which force-fits to team performance can overstate players on teams that are greater than the sum of their parts (the Jazz), are reluctant to launch Markkanen into All-Star territory. This signal works against his “strongest” case.

    Conclusion

    Markkanen is not an All-Star.

    Returning to the article’s title—what is the Lauri Markkanen Corollary? To my estimation, it’s when a team (the Jazz) jumps out with unexpected success. That team, whoever it may be (the Jazz), plays an egalitarian style, and its success is the function of many contributions from solid or good players, rather than fewer contributions from great players. But this explanation is unsatisfying—it’s too long, takes up too much headspace to put all the pieces together. Thus, the instinct is to look to one player (Markkanen) for the majority or all of his team’s success—the Lauri Markkanen Corollary.

    Footnotes

    [1] Box RAPTOR is my preferred variant of RAPTOR for all players. Especially early in the season, the plus-minus component is unstable. Markkanen ranks 66th in total RAPTOR.

    [2] Data collected from Backpicks, Basketball-Reference, BBall-Index, DunksAndThrees, FiveThirtyEight, NBA, PBPStats.


  • 2023 NBA Power Rankings | Volume II

    2023 NBA Power Rankings | Volume II

    Every few weeks, I power rank teams based on their likelihood to win the 2023 championship, as decided by me! Last month’s edition was a success by my standards considering I didn’t lose sleep over it. But a lot has changed in the NBA landscape. A lot. Listed alongside each team is its change in rankings from the preseason edition. (NB: The gaps between teams means less in the lower ranks. After the “Good” teams, all odds are essentially zero.)

    Let’s Not Talk About It
    30. San Antonio Spurs (-3)

    Already forgot them.

    29. Houston Rockets (-)
    28. Detroit Pistons (-3)
    27. Orlando Magic (-4)

    Weird aesthetics.

    26. Charlotte Hornets (-5)
    25. Oklahoma City Thunder (+3)

    Giddy for Giddey.

    Average-ish
    24. New York Knicks (-4)

    Greek Yogurt.

    23. Washington Wizards (-1)
    22. Chicago Bulls (-3)

    Prototypically average.

    21. Utah Jazz (+10)

    They have a “salty” flavor to them.

    20. Atlanta Hawks (-2)
    19. Brooklyn Nets (-9)
    18. Los Angeles Lakers (-3)

    Deepest of the deepest sleepers.

    17. Portland Trail Blazers (+1)

    Called it I guess…

    Good
    16. Indiana Pacers (+10)
    15. Minnesota Timberwolves (-3)
    14. Miami Heat (-7)
    13. Sacramento Kings (+11)

    LOLOLOLOLOLOL.

    12. Toronto Raptors (+1)
    Pretenders
    11. Memphis Grizzlies (+3)
    10. Denver Nuggets (-2)

    Eh.

    9. New Orleans Pelicans (+7)

    Wow! Cool!

    8. Dallas Mavericks (+3)

    Strange. Very strange…

    7. Philadelphia 76ers (-1)
    6. LA Clippers (-2)

    They might be a contender. Kawhi. I don’t know.

    Contenders
    5. Cleveland Cavaliers (+6)
    4. Golden State Warriors (-3)

    Don’t ask.

    3. Phoenix Suns (+3)
    2. Milwaukee Bucks ()

    Two of arguably the three strongest DPOY candidates (Antetokounmpo, Lopez) defending the backline; and that doesn’t even begin to scrape Holiday’s defensive impact. The offense will hopefully stop lagging when All-Star Khris Middleton returns.

    1. Boston Celtics (+2)

    The best offensive team in the NBA designed for repetition and sustainability, poised for even greater success when all the elements of their defensive core return from injury. The clear-cut frontrunner for the 2023 NBA championship in my book.

    Nice job?


  • 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
    Thoughts

    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

    Yikes.

    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

    Average

    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

    Hmm…

    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.

    Good

    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.

    Pretenders

    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.

    Contenders

    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] http://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:

    Criteria

    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
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    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