Nowadays, you’re either an eye-test guy or stats guy, right? There seems to be no acceptable middle ground, especially to the side I’ll take a deep-dive into today, the “traditionalists.” The use of any intelligible statistic beyond the box score signals someone who skims a Basketball-Reference page to evaluate a player, a common notion set forth by some. I haven’t absorbed the entirety of the traditionalist standpoint; so, to gain insight on what it entails, I turned to Ekam Nagra, the face of the “Ball Don’t Stop” Instagram page, a platform he uses to host a podcast. He will often use his posts to share his ideas on how analytics are poor tools to evaluate players, at times to interview former and current NBA players with similar opinions.

Through my time spent learning the tendencies of his methods, I believe I’ve found a reasonable set of pillars that provide the structure for his evaluations, rankings, or any other process that requires player assessment:

• Because the rules of basketball dictate the value of a possession to rely on whether or not the ball is scored, the most valuable individual trait is also scoring.
• The previous view can be further defined to focus on volume scoring (although Nagra has expressed a distaste for the term in the past), with a crucial context of efficiency being the difficulty of the defensive scheming and the type of shot.
• Defense, on the player and team levels, is less of an individual quality than offense because the latter dictates the momentum on the defensive end (offense “creates” defense in a way).
• Statistics and analytics, in essence, lack context and are watered-down interpretations of court actions used as a flimsy replacement for “true” explanation.
• Lastly, basketball analysis is an esoteric field. It requires direct, firsthand experience as a competitive basketball athlete to attain a higher ability to comprehend game actions.

Anyone familiar with my previous work knows my evaluation tendencies, ones because of which Nagra would almost certainly dub me an “analytics boy” or a “stat fan.” I consistently use statistics and impact metrics in my end-of-season evaluations, so as a potential disclaimer, I am writing from a “progressive” perspective (i.e. in support of the analytics movement). Thus, I’ll dissect the meaning of Nagra’s explicit and implicit rationale to provide an analytical alternative, which will hopefully take the series an extra step further in the truer understanding of the psychology behind basketball.

Scoring Blindness

As I discussed in the first installment of the series, as defined by the Thinking Basketball book, “scoring blindness” is the tendency of a critic to overrate the contributions of a team’s highest-volume scorer (i.e. the player who leads his team in points per game). Although I don’t agree with the employment of such a method, I understand the concept behind it: if the point of a possession on offense is to score the basketball, then the best players will likely score most often. (This is something I generally agree with). However, Nagra takes it a step further in Episode 91 of the “Ball Don’t Stop” podcast, from which the following quote is derived:

“… It just drives me crazy when I hear this, and I laugh, and it’s usually people that never played basketball saying this, or people that never really scored when they did play basketball, saying, ‘Oh, he’s just a scorer.’ … ‘You know, he’s the best scorer, he’s not the best player.’ … Scoring isn’t everything in basketball, but I’ll be the first to tell you it is by far the main thing. The name of the game is getting buckets.” (Ekam Nagra – Episode 91 of the “Ball Don’t Stop” podcast)

I understand the train of thought behind Nagra’s beliefs. A team’s efficacy on offense is entirely dependent on how frequently they score the ball. This is why a team’s Offensive Rating is so widely used; it measures how good a team was at performing its offensive duties. Therefore, the “best” offensive players make the largest scoring contributions. However, I think it’s a misstep to connect scoring on the team level to individual scoring on the player level. This claim relies on the belief that individual scoring is not the only way to positively influence a team’s offense. High-level shot creation that unclogs the floor and opens more efficient attempts is, in fact, usually more effective to the team compared to consistent “hero-ball” and isolation possessions.

Scoring blindness is, as stated earlier, the propensity to rate players based on favorable points per game figures, and we see it in practice with the criteria Nagra uses the evaluate players:

“The number one thing in basketball, the foundation of the game is putting the ball in the basket. The guys that did that the best are the guys that shined the brightest in the history of the game. They’re the ones that moved arenas, they’re the ones that sold jerseys, they’re the ones put teams on their back, the guys that are, you know, making sh*t happen on the basketball court.” (Ekam Nagra – Episode 91 of the “Ball Don’t Stop” Podcast)

I’ll return to the tendency later, but Nagra consistently attributes questionable factors to a player’s “goodness,” such as the roar of the crowd, merchandising, and a touch of the “Lone Star Illusion” (the tendency to undervalue the effects of a supporting cast, another topic invoked by Thinking Basketball). A self-proclaimed former player, Nagra often relates the demanding environment of the NBA to street-ball or pick-up preferences:

“It’s… it’s simple, you know, common f*cking sense. If me and you were to walk into a court today and, er, open run or tryout or whatever, the first guy that would stand out, the first guy we’d look for if we were smart, is the guy that’s putting the ball in the hoop.” (Ekam Nagra – Episode 91 of the “Ball Don’t Stop” Podcast)

This reminds me of the mentality of middle or high-school roster selection, or the mindset of the young players, which pose the ultimate goal to be the best and brightest scorer. After all, almost everyone wanted to be the ones to hit the game winners and the clutch shots when they were young, and that means you would want to become the best scorer. As I dive deeper into Nagra’s evaluation style, I’ve become more convinced a lot of his rationale is based on his time as a player: who stood out and who appeared to be the best.

Nagra gives another opinion that provides insight on how he relates scoring to team performance later on in the episode used for the aforementioned quotes:

“But those teams that win, and these teams that are led, and teams that go far in the Playoffs, they’re the ones that have the best scorer on the floor… or the second-best scorer on the floor… at all times.” (Ekam Nagra – Episode 91 of the “Ball Don’t Stop” podcast)

Because he doesn’t give any specific examples, we could investigate this claim to create a “stepping-off” point to see whether or not his claims are based on facts or the internal, intuitive feelings he conveyed earlier in the episode. To either confirm or deny this claim, we can look at the league-leaders in points per game in recent history and connect them with which round their teams advanced to in the second season. (He doesn’t give a specific round or the number of postseason games played to constitute teams that “go far in the Playoffs,” so I’ll assume the lower fence is a conference championship appearance.)

If a (qualified) top-two finisher in points per game was on a team that advanced to the conference championship or further in the same season, he will receive a “Yes.” If a player’s team did not manage to reach the semi-finals, he will be denoted with a “No.”

• 2019-20: James Harden (No) and Bradley Beal (No)
• 2018-19: James Harden (No) and Paul George (No)
• 2017-18: James Harden (Yes) and Anthony Davis (No)
• 2016-17: Russell Westbrook (No) and James Harden (No)
• 2015-16: Stephen Curry (Yes) and James Harden (No)
• 2014-15: Russell Westbrook (No) and James Harden (Yes)
• 2013-14: Kevin Durant (Yes) and Carmelo Anthony (No)
• 2012-13: Carmelo Anthony (No) and Kevin Durant (No)
• 2011-12: Kevin Durant (Yes) and Kobe Bryant (No)
• 2010-11: Kevin Durant (Yes) and LeBron James (Yes)
• 2009-10: Kevin Durant (No) and LeBron James (No)
• 2008-09: Dwyane Wade (No) and LeBron James (Yes)
• 2007-08: LeBron James (No) and Allen Iverson (No)
• 2006-07: Kobe Bryant (No) and Carmelo Anthony (No)
• 2005-06: Kobe Bryant (No) and Allen Iverson (No)

During the last fifteen seasons, only 26.7% of either conference final series has sported one of the top-two finishers in points per game in the same season. Admittedly, this isn’t the largest sample there is, but it disproves that having one of the top-two volume scorers in the league guarantees a deep Playoff run. We’ve actually seen more of the opposite; teams seem to be more likely to appear in the conference finals without, say, a thirty points per game scorer. This isn’t to say a team’s leading scorer hurts his team (although he sometimes does), but that a dominating half-court “assassin” is not a prerequisite to a deep run in the postseason.

Defensive Ignorance

With the current state of information available at the hands of most, there was bound to be a lopsided partiality to offense compared to defense. Box scores will track the number of points, rebounds, assists, field-goals, and turnovers a player records in a given period, but defense is restricted to steals and blocks (personal fouls are usually associated with the defensive box score but also include offensive fouls). Nagra is no exception to this tendency, expressing a clear opinion in Episode 34 of his podcast, which covered the validity of the terminology given to “two-way players.”

He immediately provides insight on how he distinguishes the best players:

“You know, the foundation of this game, since day-one, will always be scoring. The defense, all that other stuff is a bonus.” (Ekam Nagra – Episode 34 of the “Ball Don’t Stop” podcast)

As discussed earlier, this relates to the connection Nagra established between scoring on the team level with individual scoring on the player level. We have already concluded this connection is mostly false and overlooks the larger expanse of offensive contributions that leads to scoring output among teams, so it’s safe to say this mindset is setting up all further opinions that build on this principle for some level of failure.

However, it seems the prioritizing of high-volume scorer extends further than the structure of the game Nagra lays out:

“The most feared thing in basketball, till this day, is a guy that can walk into a game and effortlessly get you thirty, forty, and fifty [points].” (Ekam Nagra – Episode 34 of the “Ball Don’t Stop” podcast)

—

“I’ve never seen a coach break his clipboard because of two-way players just being a two-way player.” (Ekam Nagra – Episode 34 of the “Ball Don’t Stop” podcast)

—

“You know, those are the guys [scorers] that people remember forever…” (Ekam Nagra – Episode 34 of the “Ball Don’t Stop” podcast)

The common denominator of each sentiment is the reactivity to surroundings, especially emotionally-driven ones: fear, anger, and remembrance. Yet, Nagra uses these elements to evaluate on-court impact. The individual perceptions of these game actions may not even roughly correlate to value as a player, but he continually treats them otherwise. This relates to my earlier inferences that suggest Nagra structures his knowledge of basketball around his experiences as a player. However, as we explore later, having played basketball at even the highest level does not guarantee a higher ability to evaluate players.

During the same segments, Nagra explores what he believes to be an inherent disparity between offensive and defensive contributions, the former of which drastically outweighs the latter:

“Carmelo Anthony on the Knicks… top-four player in the game. You know, I didn’t care if he played defense or not. The fact that he could walk into a game and singlehandedly change the outcome, and… you know, have an impact on the game with just his scoring ability – that right there is it for me.” (Ekam Nagra – Episode 34 of the “Ball Don’t Stop” podcast)

—

“Hey, if you’re a good defensive player, you’re a good defensive player. If you can score the hell out of the ball, you know, you’re a killer, you’re an assassin out there.” (Ekam Nagra – Episode 34 of the “Ball Don’t Stop” podcast)

—

“You know, Michael Jordan and Kobe Bryant were the same way [skilled on offense and defense]. Like, I never saw anyone dub them as ‘two-way players.’ They were just the best players in the game… Them playing defense was a bonus.” (Ekam Nagra – Episode 34 of the “Ball Don’t Stop” podcast)

Defense is consistently treated as a secondary trait, a bonus, to offense. Nagra suggested no other skill in basketball matters if, at the end of the day, the ball wasn’t going in the basket. He doesn’t hold this to be self-evident; rather, the rules of basketball (which require a bucket to be had) make it so. This is more antithetical to how basketball is played than Nagra gives credit for. Through the entirety of his segments that cover scoring or defense, he doesn’t address that the magnitude of a two-point basket on offense is equal to that of a two-point basket allowed on defense. If the world’s greatest offensive player is worth 1.3 points per team possession but allows 1.3 points per possession on defense, he’s not helping his team win at all!

“Correctness”

The last revealing quotation from Episode 34 comes with his disagreement on the soundness of dubbing “two-way” play:

“What the f*ck is a two-way player? Like, who came up with this concept? You know, if you look back at it, like… I never heard this in the ‘90s. I never heard this in the 2000s.” (Ekam Nagra – Episode 34 of the “Ball Don’t Stop” podcast)

Nagra displays a clear bias toward styles of basketball that align with the styles of play that were most prevalent in his youthhood, with a strong emphasis on jump shots. During Episode 36, he provides this excerpt on a tendency he observed upon LeBron James’s arrival in Miami:

“I didn’t like it when he [LeBron James] went to the Miami Heat and he was like, ‘Hey, I’m not gonna shoot threes anymore; or, I’m gonna shoot less jumpers.’ … I didn’t feel like that was pure. You know, you can’t really cheat the game. You can’t be… like, it is what it is. You gotta make jump shots. In basketball, the foundation is ‘get a bucket,’ score a jump shot, all these things. You know, they matter.” (Ekam Nagra – Episode 36 of the “Ball Don’t Stop” podcast)

During his discussion on why he believed Kawhi Leonard was the second-best player in basketball, Nagra heavily implies a “proper” or “correct” version of basketball exists that some of the league’s players have violated. He, again, refers to his foundation of the game (to get a bucket), the misinterpretation of which already dilutes the quality of any appendages, but then extends it to: “You gotta make jump shots.” To improve the team’s offense, it would be undeniably more effective for the unit to score fifty-five easy layups (if those were somehow available) than to score thirty, forty, or even fifty-four of the most difficult mid-range shots in league history. After all, the team that scores the most wins!

Nagra’s inclinations toward his most prized styles of play signal heavy biases, and ones that cloud the truth behind “effective” basketball play as ones that are flashy, memorable, and visually remarkable. Thus, I don’t immediately absorb the opinions he states as ones of proper consideration, rather ones driven by personal preferences that don’t relate to the “true” topic at hand, which makes these claims nothing more than opinions in a sea of truth and falsehood.

We’ll fast-forward to Episode 211 of the Ball Don’t Stop podcast, in which Nagra an All-Rookie and All-Defensive former NBA player in Josh Smith. When Nagra asks Smith what the latter’s reaction was upon the arrival of the analytics revolution, Smith replies with:

“It [the analytics revolution] felt weird because… you know, when you start playing basketball, you’re taught the game the right way. You know, like, you know, mid-range, layups, three-pointers, you know, like… getting your teammates the ball… You gotta start inside out.” (Josh Smith – Episode 211 of the “Ball Don’t Stop” podcast)

There’s a recurring theme in the “Ball Don’t Stop” podcasts that distinguish a “proper” way to play the game, one that paves the way for Nagra’s (and former players alike) distaste of basketball analytics and advanced statistics.

Authority

Among the pillars of traditional values stated earlier was the extreme value of having experienced the competitive and rigorous environment of professional or semi-professional basketball play. If such a trait doesn’t reside within the individual, he or she is automatically less able to discuss the evaluations of a player at a higher level.

During Episode 211, he interviews an All-Rookie and All-Defensive former NBA player in Josh Smith. Nagra wastes no time in expressing disinterest in the analytics revolution and modernized statistics:

“I feel like the game now, as talented as it is, as athletic as it is, they’ve [analytics and its supporters] kind of dumbed it down, the way it’s played, man; and like, it’s just weird to me.” (Ekam Nagra – Episode 211 of the “Ball Don’t Stop podcast)

As we’ll explore later, Nagra sees the analytics revolution and its associated forthcoming as having deteriorated the play of the game, crucial context for later excerpts. Smith follows later in the episode with his own take on how players approach analytics:

“As a player, how can you listen to a person that never played the game of basketball? ‘Cause most of those analytical guys… have never played a game of basketball, so they don’t have a feel of… what’s really going on… and time and situation and… the mental aspect of the game… You can’t put that in the analytics.” (Josh Smith – Episode 211 of the “Ball Don’t Stop” podcast)

Smith certainly doesn’t speak for all players, especially the recently-employed more familiar with the analytical setting (Smith played his last full season in 2015-16), but there seems to be some notable stigma towards the analytics departments of NBA teams among players. They feel, as few to none of the analysts were players themselves, the analysts are not as qualified to dictate the tendencies and playstyles of those that are or were experienced as NBA players. Smith said that the lack of hands-on experience prohibited analytics developers from comprehending and incorporating the necessary elements.

I’ve never played in the NBA, so perhaps there’s something that I’m missing, but no matter who you are or what your experience with basketball is, the exact same forty-eight minutes of play (barring overtime) is available to any and all who can watch. At the end of the day, all of the court actions that a player is involved in can be absorbed by an outside observer. Granted, the comprehension of these court actions is a skill, and one that requires great knowledge and practice; but the only aspect of the game that a non-player can’t directly recognize is the “mental” aspect: what flows through the players’ minds. The ability to experience these may add context to the triggers behind varying neurological patterns in certain moments; but as Nagra continuously states, victory is crowned by scoring more than the opponent. No aspect of a player’s impact is exclusive to former or current players.

As Nagra and Smith continue their conversation, the latter gives more of his thoughts on how analytics is changing the game and, more specifically, how it affects the NBA as a show business:

“It’s sad, because… I feel like it’s gonna eventually take… the ratings are gonna start going down because… like, the exciting part of the game was dunking on motherf*ckers… like, putting that sh*t in the rim, putting they *ss in the rim… It’s like, all these threes and layups and floaters and sh*t… It’s taking the excitement out of the game.” (Josh Smith – Episode 211 of the “Ball Don’t Stop” podcast)

Smith clearly connects his distaste of analytics to how it affects the style of the game, an aspect for which Nagra also expressed concern. It’s not unreasonable to say that the pace-and-space style of basketball spurred by analytics makes the viewing experience less exciting, but then again, analytics were not created to improve game ratings. Analytics were created to give players and teams the highest odds to win. Advanced statistics and impact metrics aim to quantify and structure a player’s or team’s impact, not either’s likelihood of increasing viewer count. Thus, I see Nagra and Smith’s concern with analytics as not only misguided but untrue to the nature of its creation. Advanced statistics are not boosters of the NBA as a show business; they aim to provide explanatory and predictive power to help players and teams understand what happens on the court and to improve for the future.

I went into this examination of “traditional” thinking with an open mind, even hoping to add a piece or two of its process to my own if I were to find the right evidence. Unfortunately, I don’t feel I’ve been given any more reason to revert to traditionalism given the alternatives (i.e. analytics). Nagra does not speak for all traditionalists and Smith doesn’t speak for all players, but the brief taste I had of their ideologies was nothing more than unimpressive. They aspire for a desirable style of play that, along with the growth of data and information, became obsolete. Now, there’s certainly nothing wrong with deprecating the current style of play as it pertains to the watching experience, but the same practice in evaluating players and teams is a method doomed for failure.

Nagra peddles the belief that experience, or the lack thereof, is the problem in today’s game. To me, it’s less an issue stemming from experience, but the unrelenting tendency to hold onto ideals and the inability to adapt to an evolving game.

I’ve had more discussions on how to properly evaluate basketball players more times than I can count. More often than not, I’ve been met with disagreement in those conversations. After a very recent one that argued the very principles and measurements that govern and quantify certain skills, I was inspired to “remaster” my player rankings list from 2020. The recent acquisition of some proprietary data from BBall Index was the perfect opportunity to use new and refreshing information to increase the accuracy of my evaluations, and I’d like to share the results here.

Criteria

It’s very easy to skip a “criteria” section in a player ranking and go directly to the list, but such segments are the perfect indicators for why certain players appear in the spots they occupy. Therefore, if I receive any comments along the lines of: “Why is [insert player name] ranked so low? He averaged this many points, rebounds, and assists with this field-goal percentage, and his team record was this!” I will probably not respond. After all, this is not a list of which players have the sexiest box scores or which players’ teams were the best. The former merely quantifies tendencies and the latter is unrelated to individual performance as a whole, so they aren’t devices I’m particularly comfortable using.

I’ve repeated my evaluation process in almost every post that pertains to the subject, and this one will be no exception. I follow a simple train of logic that, while not necessarily being an axiom of the process, is the “most likely” truth I’ve come across: 1) basketball is a team sport, and players are chosen to help improve the success of the team, 2) over the course of a whole season (the length of a “seasonal” evaluation), the ultimate team goal is to win the Finals, 3) therefore, the best players increase the likelihood of a championship the most. That chain of thought is often confused with prioritizing players whose teams performed the best or were the closest to winning a title in a given year. This is not the case. Players are seen as independent from their teams in these evaluations.

Namely, “situational” value is not the target of this ranking due to significant levels of confoundment for certain players (i.e. certain team constructions can dilute the “true” value of a player). Rather, these evaluations consider how a player would affect all types of teams, ranging from the worst to the best ever and everything in between. To measure the championship likelihood a player provides, I estimate a player’s per-game impact alongside average teammates in a theoretically “average” system (metrics like Adjusted Plus/Minus capture the “most likely” value of this). However, this “true” APM value changes as a player enters a new environment. As the team quality falls below an SRS of 0, the player becomes more important (thus, his “true” APM rises) and, inversely, as the team’s SRS exceeds 0, the player becomes less and less important. The deceleration of the latter is measured through “portability,” which uses five scaling curves to estimate the degree to which these diminishing returns occur.

To recap:

• I translate all my thoughts on a player to a numerical scale that estimates a player’s “true” Adjusted Plus/Minus, or per-game impact alongside average teammates and against average opponents.
• Portability ratings then measure the changes in “true” APM (which I call “Plus/Minus Rating,” or “PMR”) to estimate how a player impacts the more extreme team qualities.
• The team SRS with versus without the player and how it translates to championship equity is determined using a function, based on historical data, that estimates title odds.
• The weighted (for how likely a player would be on a given team) average of championship odds with and without a player is his Championship Probability Added (“CPA”) value.

Note: The distribution of team SRS is based on the last fifty seasons of team data / Portability is more of a spectrum than anything else, so if two or more players have the same CPA value, I opt for which one is more scalable, even if the two happen to be assigned to the same scaling curve.

With the criteria portion out of the way, let’s get into the juicier content: the rankings themselves. Today, I’ll kick off the series with the #16 to #25 players, which will be followed with a separate post for the #6 to #15 players and will conclude with the top-five. Let’s dive in!

—

The “just missed the cut” bunch, or players within half of a percent of making the list, includes but is not limited to De’Aaron Fox (2.1%) and Donovan Mitchell (2.1%).

25. Bradley Beal, Wizards (SG)

Although most saw his season highlighted by an outstanding 30.5 points per game, Beal’s real talent was his increasing scalability. The impact he provided off the ball, in screen action on the perimeter, and improved passing efficiency were the true upgrades to his offensive skill set. He lagged behind on this list due to a troubled defensive game, but its offensive counterpart mitigated any extreme effects.

Championship Probability Added: 2.5%

24. Pascal Siakam, Raptors (PF)

“Spicy P” was edging into a lot of people’s top-ten rankings approaching the end of the season due to leading the number-two seed in the East in scoring. However, similar to Beal, the traits Siakam exhibited that I valued more were his “portable” ones: extremely versatile defense (he guarded each one of the five positions during at least 13.9% of his possessions) and stronger movement off the ball. I attribute the changes in his postseason box score to an extreme matchup more than most.

Championship Probability Added: 2.7%

23. Kyle Lowry, Raptors (PG)

Lowry has always been a great option for Toronto on the perimeter, whether it be on the offensive or defensive side of the ball. He paired extremely strong playmaking and offensive screen action with active and attentive perimeter defense that captured the scrappy nature of his play. Lowry has yet to break through on either side of the ball to vault him into strong All-NBA candidacy, but the aggregate effects of his two-way play earn him a well-deserved ranking.

Championship Probability Added: 2.7%

22. Devin Booker, Suns (SG)

Devin Booker may be the most undervalued offensive engine to be drafted in the past six seasons. He’s developing into a master of nearly every offensive skill: scoring, shooting, passing, creation, off-ball movement, and even some post play. His 2020 campaign a more promising signal of his future than most will recognize. If his defense were only more effective, he would enter All-NBA territory right now. For now, he’s a strong All-Star level player.

Championship Probability Added: 2.7%

21. Bam Adebayo, Heat (PF)

He may not have the spicy scoring average or captivating outside shooting to woo fans like a lot of the on-ball engines on this list, Adebayo more than makes up for it with his strong secondary traits and game-changing defense. He was the Robin to Jimmy Butler’s Batman in Miami’s surprisingly good offensive scheme last season, and his finishing and rim rolling capabilities were perfect complementary skills to Butler’s playmaking.

Championship Probability Added: 2.9%

20. Kemba Walker, Celtics (PG)

After a strong offensive season in Charlotte, Walker made the unexpected transition to secondary star behind Jayson Tatum, but my best guess is that he was still the best offensive player on the team: more refined scoring, passing, and he provided a much-needed boost to convert Boston from an up-and-coming band to one of the league’s highest-performing offenses. Walker was not quite a top-ten offensive player in my eyes, but a diverse portfolio of defensive matchups and mild effectiveness earns him a top-twenty nod for me.

Championship Probability Added: 3.3%

19. Jrue Holiday, Pelicans (SG)

Holiday may seem to have been dwarfed under the rookie sensation that was Zion Williamson or the All-Star appearance of Brandon Ingram, but he was the clear driver of the New Orleans squad. He led the team in Box Creation and was a close second in offensive load to Ingram. The more impressive note on Holiday’s game to me is his sneaky good isolationism; he was in the 90th percentile in effective field-goal percentage on such possessions, which paired with exceptional passing and playmaking, enabled Holiday to act as one of the exclusive offensive engines in the NBA.

Championship Probability Added: 3.3%

18. Chris Paul, Thunder (PG)

Previously seen to be approaching the dusk of a luxurious NBA career, Paul sparked some life in his aging game by regaining status as a premier passer and playmaker and some of the most effective outside shooting in the league. His lack of efficacy off the ball and declining defense lead me to believe he was best suited as a number-one option, but this likely means none of his offenses would have ever eclipsed into greatness. Perhaps his stint with James Harden contests that, but I view Paul as a fairly neutral defender with strong offensive quarterbacking abilities.

Championship Probability Added: 3.6%

17. Jayson Tatum, Celtics (PF)

It wasn’t uncommon to see critics view Tatum as on the verge of a breakout season, and he finally materialized the possibility in 2020. A refined selection of shots that placed less emphasis on long twos was a crucial addition to his game; and, although his efficiency was less than league-average last year, an exceptional outside shooting portfolio, a strong one-on-one skill set, and developing finishing abilities signals promise. Tatum’s extreme versatility and perimeter engagement on the defensive end led to an All-NBA level season.

Championship Probability Added: 4.0%

16. Khris Middleton, SF (MIL)

Giannis Antetokounmpo’s partner in crime blossomed into one of the league’s most effective secondary options in 2020. He exhibited one of the best outside shooting campaigns of the year and, alongside his developing passing game and strong gravity, evolved into one of the very best offensive players in the sport. His defense was also a large positive. Middleton wasn’t the most active defender in the world, but he was among the most disruptive in the league and prohibited shots at the rim as well as any wing in the league.

Championship Probability Added: 4.3%

—

Stay tuned for the next two additions to the series, which will be released in the next few days!

Basketball is one of the most divisive sports in how its followers interpret the actions and results of the games. Enough of a mixture of ambiguity and clarity exists that allows viewers to, depending on their psychological tendencies, to either hold very assured or very doubtful opinions. Although the following is not a trend of any large population, in my own experience, I’ve frequently felt less confidence in my assertions as the quality of my research on a particular topic increases. This isn’t a knock on any type of analytical method; rather, over time, as I’ve learned more and more as to what information is worthwhile and which conventions aren’t, the insurmountable number of hindrances that prevent “correct” conclusions becomes more and more obvious. That’s why, without a proper framework to assess certain aspects of the game, a lot of the givens and guarantees set forth by critics and fans are doomed for failure.

Thinking Basketball was an invaluable read that does justice to its description: “a guide to being a responsible fan.” I was already acquainted with a lot of the concepts discussed in the book, yet it was nothing short of an eye-opening experience. It provides value from a psychological perspective, having been written by Ben Taylor, a cognitive scientist in addition to an NBA analyst. The self-proclaimed subtitle of the novel, per Taylor’s website, Backpicks, stresses “‘…Why our minds need help making sense of complexity.’” A foundational idea in the book is how the human brain is unable to properly categorize and store the hundreds of thousands of court actions from the tens of thousands of possessions that occur in an NBA season, and how negligence of that paves the way for a multitude of unconscious biases and misconceptions that dominate commercial and media analysis. 

This is a large reason for my skepticism of heavily eye-testing games. Unless the viewer is able to watch every single one of a player’s possessions over the course of a season, a lot of that “mental extrapolation” doesn’t act as a very representative picture of a player’s tendencies. Even if someone managed to watch every single possession, they’d also have to overcome faulty memory, which would require an extremely diligent note keeping system. Not even that guarantees “correct” representation, as the viewer needs to also be able to identify the causal actions of a play rather than the outcome alone, a skill that is surprisingly rare.

The concluding sentence of the previous paragraph is very significant in my emphasizing how important Thinking Basketball and its ideas are. The large majority (an understatement) of critics and fans are not aware of the biases and misconceptions that riddle their inferences, thus hindering the growth of these principles. I’ll return to the possibility of what may be the necessity for the continuation of the incorporation of these obstructions later, but on an individual level, the absorption of the techniques presented in the book will work wonders for the unexpecting reader. I’ll discuss the fallacies introduced by Taylor that pertain to certain examples I’ve encountered to, hopefully, give reason to purchase Thinking Basketball and consider its contents.

Scoring Blindness

It’s no secret the mass usually sees the “best” players as the best scorers. After all, if the player is managing to put the ball in the basket, he’s probably doing something right. This is hardly the case. Without delving too deep into the phenomenon that is “scoring blindness,” it’s simply the widespread propensity to overrate the effects of “individual scoring,” measured by statistics such as points per game and scoring rate. This causes a player who provides significant value in other aspects, such as creation and defense, to be disproportionally valued against the leading scorer on the same team, who might actually be hurting the offense’s efficiency depending on the circumstances.

The perfect modern example of how “scoring blindness” influences opinions lies in the current Utah Jazz, a team that features an ostensible race to be named the team’s best player between All-Stars Rudy Gobert and Donovan Mitchell. Any fan who’s paid even minor attention to the league in the past three years knows Gobert as the defensive titan who anchors Utah’s annually good team defenses and Mitchell as the volume scorer who acts as the Jazz’s offensive commander. Last week, I issued a poll on Hardwood Amino that simply asked the voter to choose which player they thought was the “best” in Utah between Mike Conley (an outside candidate), Gobert, and Mitchell. With 197 votes tallied, here are the results at the time of this writing:

The application allows the user who issues the poll to vote, which is needed to view the results. The green check denotes the player for whom I voted.

Even with my extra vote for Gobert, nearly sixty percent of participants saw Mitchell as the best player on the Jazz. This doesn’t necessarily mean the same proportion of the population feels the same way, but perhaps this signals the potential trend that would be in favor of Mitchell. I was able to gain insight from a “Mitchell supporter,” @paydaypayton (whom I’ll refer to as “Payton”) on Discuss TheGame, in an earlier discussion. A portion of his argument in favor of Mitchell relies on the following viewpoint:

“But we [are] really forgetting that Mitchell has been basically single-handedly the reason why the Jazz have had any success in the postseason…”

Payton may reminisce on this statement as a hyperbolic response, but let’s explore the possibility of Mitchell as, at the very least, the primary driver of Utah’s success in the postseason. Measurements I lean towards as painting a strong picture of a player’s per-individual-possession value to his team is the Oliver Rating System, typically referred to as Offensive and Defensive Ratings, which estimate the number of points a player produces and permits on either side of the ball per 100 individual possessions. Because no player is involved in 100 team possessions a game, these ratings can seem outlandish, but are, in fact, great estimates on the basis it uses – individual possessions. 

The portrayals of either player from Oliver’s ratings, which I happen to agree with, are that the very different styles of offense they play generate different results. Gobert, due to a healthy 78% of his field-goal attempts in the Playoffs coming within three feet of the rim, a high frequency of offensive rebounding, and low-risk offensive possessions, generates considerably more points per individual possession than most players (this is a large reason for Oliver’s emphasis on roles when using his ratings). Thus, it’s not abnormal to see his marks in this stat remain higher than Mitchell’s quite often. Truth be told, he generated more points per individual possession in every one of the Jazz’s last three Playoff runs. But, because this may yield more of an apples-to-oranges comparison than we’re hoping for, we also have to consider Mitchell’s role in improving Utah’s offensive efficiency. 

During the Playoffs, Mitchell doesn’t usually yield a highly-efficient style of offensive play. His rookie campaign, one marveled for its “achievement” of dethroning two All-NBA players in the first round (Paul George and Russell Westbrook), produced a measly 1.01 points per individual possession in the second season. Relative to the league average of 1.09 points per possession in 2018, Mitchell wasn’t truly helping his team succeed in the manner Payton suggested. Per Synergy Sports Technology, Mitchell averaged the sixth-most isolation attempts per-game in the 2018 Playoffs. However, unlike great and high-volume isolationists, Mitchell’s offensive play didn’t net many points. As it turns out, he was actually hurting Utah’s offense more than he was helping it. Mitchell’s isolation attempts yielded a woeful 0.95 points per attempt. If Utah replaced its entire offense with his postseason scoring “heroics,” they would have sported one of the worst Playoff offenses in league history.

The trend carries over to an even less successful run in 2019 that saw Mitchell produces a putrid 82 points per 100 individual possessions. Namely, if the above scenario were invoked again: if the Jazz allowed Mitchell to captain the entirety of the team’s offensive possessions (keep in mind the offensive rating measurement does not account for any late-game fatigue), they would very likely have the most inefficient offense in league history, when his isolation output made the ugly drop to 0.75 points per possession on only one fewer attempt per-game. The only season that defies this trend was last year’s first-round series versus the Denver Nuggets, in which Mitchell’s scoring numbers finally exploded. He averaged an incredible 37.8 points per 75 possession on a True Shooting percentage nearly 13% greater than league-average. He also averaged 1.11 points per isolation possession, a massive improvement from his previous two postseasons. What spurred this seemingly inexplicable change?

Variance

The widespread solution, one likely employed by those similarly-minded to Payton, is that Mitchell had a “scoring epiphany” of sorts that allowed him to “finally figure things out.” To treat Mitchell’s scoring burst in the previous Playoffs as an accurate representation, or even an adequate signal, of his true scoring abilities would be a product of multiple fallacies, all of which are explored in Thinking Basketball. The first relates to the time span in which Mitchell produced these numbers, a mere seven games, against a mediocre defense for that matter (Denver had the #16 ranked defense in 2020). Since the duration of Utah’s run was only seven games, it’s supposed to feel like a sufficient representation of Mitchell’s postseason value, except it isn’t. The second bias present relates to variance. Because Mitchell’s averages were notably good in the series, it’s easy to overlook the negative aspects in that timeframe.

He had arguably his best game in the series in Game 1, which saw him score a mind-boggling 57 points en route to a 45.1 Game Score, a composite metric that numerically evaluates a player’s performance in a game. Keep in mind, however, that Game 7 of the same series saw Mitchell score a dreadful 4.5 score in the same metric, scoring only 22 points as opposed to an average of 36.3 points-per-game in the series. The lesson to be learned from this case is that a large number of unexpected, unattainable leaps in numbers that will never be reached again by the same player are products of variance. Mitchell, given eighty-two games versus the exact same Denver roster, in the same setting, alongside the same teammates, and maintaining the same level of “true” value, would have seen his points-per-game fall much closer to his regular-season average of 24 points-per-game. 

Due to the prior information we have on Mitchell, addressing these numbed biases shows that he wasn’t “truly” the league’s best scorer in the postseason. Rather, he was one in a long line of players whose degree of variance in the right timeframe creates the illusion of boundless leaps in talent. Further fallacies with Mitchell’s title as Utah’s best player include the “Lone Star Illusion,” the unsound mitigation of supporting cast efforts to divvy team credit, which I’ll address later on. My goal with this and future posts in the series is to update these communities with their unconscious biases, ones no one is immune to, and hopefully tease out the internal questioning we all need. To everyone reading, and especially Payton (!), I encourage you to add Thinking Basketball to your reading list.