(Picture courtesy of FiveThirtyEight)

If one were to indulge in the scope of NBA analysis, they’d be met by an increasingly large number of methods, ranging from visual to analytic tools. Film study is a relevant, predominant element of modern NBA analysis, and one that when properly wielded can draw strong inferences. However, the foremost option in a player analysis lies in advanced statistics. Despite various suppositions from a subset of questioning individuals, advanced stats are great at estimating a player’s value. They’re widely cited in a broad range of places: social platforms (Discuss TheGame and Instagram) to professional sports analysis and networking (APBRmetrics and ESPN). The widespread trust and utilization in and of advanced stats corroborate the validity of the underlying premises. The succeeding descriptions exist to explicate the calculations and identify the proper use of advanced stats.

[1] Prerequisite Knowledge

The complexity of NBA statistics varies depending on the statistic at hand. Martin Manley’s “Efficiency” (EFF) stat, which measures individual player efficiency, goes as follows:

EFF = [(PTS + TRB + AST + STL + BLK – Missed FG – Missed FT – TO) / GP]

The general grounds of EFF are apparent to most, with positive contributions weighed positively, negative contributions weighed negatively, and the sum eventually adjusted for playing time. The most valid player statistics incorporate more complex subdivisions of mathematics, often superseding traditional arithmetics. To maximize the retention of the formulas for the selected advanced stats, a rudimentary knowledge of linear algebra and regression analysis is highly recommended. The principles of “holy grail” statistics are largely dependent on these two math subdivisions.

To encapsulate the use of matrices and the least-squares solution in advanced stats, the formulation of Adjusted Plus/Minus (APM) will act as the primary step in the manipulation of matrices. Traditional Plus/Minus measures the Net Rating (point differential per 100 possessions) of a given player’s team when he is on the floor. If “Player A” is on the court for twenty possessions and his team outscored the opponent by a single point, “Player A” is credited with a +5 Plus/Minus. Plus/Minus is notoriously biased toward players on great teams playing against inferior competition, as Plus/Minus is insensitive to the quality of teammates surrounding a player and the difficulty of the opposition. Depending on these external factors, a player’s Plus/Minus could either inflate or deflate, straying away from the player’s true value.

The calculation^[1] of APM employs an n (number of possessions) x m (number of players involved in the possessions) matrix X. The following step consists of classifying whether a player is on offense, defense, or off the floor with the numerical denotations 1, -1, and 0, respectively. The matrix equation to solve for the beta-values that give the point differential Y goes as follows: 

The beta-values are yielded through the least-squares solution, a method used to minimize the sum of square residuals, fitting the data closer to the mean. The approximated β coefficients are players’ APM scores. APM, in theory, is the “holy grail” basketball metric, providing a suppositionally true value score. APM does suffer from phenomena like multicollinearity, begetting the formulation of Regularized Adjusted Plus/Minus (RAPM), which combats the extreme variance in APM and reduces standard errors. As implied through its name, RAPM employs a ridge regression, more specifically an L2 regularization, to reduce variance and center the results from APM. The modification of the least-squares solution to fit the diagonal perturbation matrix λI, a methodology that approximates solutions starting at simpler problems, goes as follows:

The resulting beta-values act as the RAPM scores for players. RAPM garnered recognition as the foremost basketball metric due to its strong foundation and the underlying premise. RAPM isn’t perfect, often taking several years to stabilize, after which the metric was largely taken in three-to-five-year samples. Despite the less positive noise surrounding it, RAPM serves as the base regression for the cream of the crop of one-number metrics. The understanding of RAPM is crucial in also retaining the formulations of its “offspring.”

• Click here to see the RAPM leaders for the current season
• Click here to see the RAPM leaders from a three-year sample

[2] Regression Models

Elementary grasps of regression analysis are additionally applicable in calculating regression models. The bases of several widespread metrics are multiple regressions on multi-year samples of RAPM. NBA Shot Charts is often recognized as the primary RAPM distributor and the most frequent base for an RAPM regression, but similar models (see Jacob Cutter‘s and Simon Zou‘s open-source RAPM models) could also act as strong bases. It’s worth noting the variety of models to determine a player’s RAPM. There isn’t a definitive solution, creating room for a variety of methods. The overarching premise of these stats is to assign coefficients to certain values (box score, On-Off ratings, etc.) based on their correlation to RAPM. These one-number metrics are differentiated based on the chosen values (box score/On-Off ratings, etc.) and the variation/length of the RAPM data set. 

Box Plus/Minus (BPM)

Developer: Daniel Myers

Box Plus/Minus consists of a predominant pair of counterparts, provided through Basketball-Reference (Myers’s model) and Backpicks (Ben Taylor’s model). The former includes a detailed description of its methodology and calculation process, two elements the Backpicks model lacks, hence its appearance. BPM estimates the number of points relative to league-average a player contributes every 100 possessions. The statistic is calculated with solely box score values. BPM is based on a twenty-year sample of “Bayesian Era” RAPM, which uses a prior probability distribution that considers team quality and minutes per game in its seasons. The regression includes sets of coefficients for cumulative BPM and its offensive half (OBPM), with variances based on position (e.g. steals are weighed more for centers than point guards due to positional difficulties). BPM builds on the regression coefficients with a series of adjustments based on team quality (in BPM and OBPM) and position. Multiple regression coefficients typically remain stagnant due to optimized data, but BPM’s weaker correlation to RAPM (compared to other one-number metrics) allows for more flexibility in adjustments.

BPM is limited by a solely box-oriented calculation but remains one of the stronger metrics in player analysis. Retrodiction testing, the process of predicting team equity (in Simple Rating System (SRS)) based on a rosters’ players’ previous stats, paints it as on par with play-by-play informed stats. From 1978 to 2019, compared to Win Shares, Backpicks BPM, and Player Impact Plus/Minus (PIPM), BPM had an SRS error (the absolute difference between predicted and actual SRS) of approximately 3.8 with a lineup continuity (the percent of the remaining roster from the previous year) of 60% (third) and an SRS error of approximately 2.5 with a lineup continuity of 95% (first), solidifying its status as a highly indicative stat. BPM’s predictive power transcends its descriptive power, however. Its descriptiveness is summarized through its Pearson correlation to RAPM, which stands at a rounded 0.66^[2]. Despite restrictions due to an exclusively box-score formulation, BPM serves mostly well as an indicator of player value, and an even stronger prospective evaluation.

• Click here to see an in-depth overview of Box Plus/Minus
• Click here to see a walk-through calculation of Box Plus/Minus

Robust Algorithm using Player Tracking and On/Off Ratings (RAPTOR)

Developer: FiveThirtyEight

FiveThirtyEight‘s “RAPTOR” metric is the newest of the popular one-number metrics (released in October of 2019), but makes a case as the most descriptive and predictive. The site previously employed an Elo-based projection that garnered a reputation as one of the most accurate NBA projection models, most notably predicting the Toronto Raptors’ championship run last season. FiveThirtyEight implied it created the RAPTOR metric for one overarching reason: modernization. RAPTOR employs more modern NBA data (player tracking and play-by-play) and models the preferences of NBA teams. The stat only uses data available to the public. RAPTOR measures the number of points relative to league-average a player contributes per 100 possessions. RAPTOR is based on a six-year RAPM sample, including components from an expanded box score and luck-adjusted On-Off ratings. Evidently, the coefficients for the enhanced elementary stats are determined through the aforementioned regression, which was based on a six-year sample of RAPM. The predictive power of RAPTOR hasn’t yet been tested, but the metric serves as the foundation of FiveThirtyEight‘s projection model. Although denoted as a descriptive stat, RAPTOR’s correlation to the base regression isn’t explicitly stated. The premise of RAPTOR is inherently strong, and time will tell the individual proficiencies and deficiencies of the metric.

• Click here to see an in-depth overview of RAPTOR
• Click here to see the GitHub for the RAPTOR data

Real Plus/Minus (RPM)

Developers: Jeremias Engelmann / Steve Ilardi

Real Plus/Minus, the featured statistic of ESPN, is similarly modeled to the preceding statistics in BPM and RAPTOR. The base regression is on a set of xRAPM (Engelmann’s RAPM model) to estimate a player’s contributions on the offensive and defensive ends in a Net point differential. RPM is the most exclusively engineered statistic among the widespread set, with little to no light shed on its calculations. The stat is most noted for its predictive power, acting as the driving force of ESPN‘s NBA projections. If one were to make suppositions of RPM, it may consist of a variety of points. Engelmann, the co-developer of RPM as well as xRAPM, garners a strong reputation for building world-class valuation models, and the assumption that RPM likely holds a high Pearson correlation to xRAPM is rational. The number is an estimated 0.71^[3], making it of statistical significance against RAPM. RPM is as proprietary as a publicly-available metric could be. Ideally, a more informed methodology and rough calculation would be published. Limited validity testing is available to determine the numerical equity of the stat. However, due to the developer, distributor, and the given premise of RPM, it’s a foremost citation to estimate player value.

• Click here to see ESPN‘s (brief) overview of RPM
• Click here to see a related Engelmann lecture

Player Impact Plus/Minus (PIPM)

Developer: Jacob Goldstein

Player Impact Plus/Minus is the primary impact metric of Basketball Index. PIPM estimates the number of points a player contributes on offense and defense per 100 possessions, mirroring its predecessors. The stat employs two similar components to RAPTOR: an expanded box score and luck-adjusted On-Off ratings. The box-score prior and On-Off ratings incorporate “pace-adjusted per 36 minutes” stats and relative luck-adjusted On-Off ratings, respectively. Although it’s not explicitly stated, it could be inferred the “luck-adjusted” component of the On-Off ratings accounts for team and opponent context: the quality of teammates and opposition, perhaps. Luck adjustments in these contexts also relate to a player’s unexpected progressions or regressions, for which career numbers are substituted (a concept used in luck-adjusted RAPM). The former may have more likelihood, but either description is possible. The coefficients for PIPM were determined through a regression on a fifteen-year sample of Engelmann’s RAPM. PIPM holds unprecedented accuracy, maintaining a Pearson correlation of 0.875 to the base regression. The communal precision of PIPM creates one of, if not, the foremost metrics in player analysis.

PIPM’s validity is maintained in retrodiction testing. As a part of the initial group of tested metrics stated earlier, PIPM held the lowest SRS error between 60% and 90% lineup continuity, permitting a marginal difference to Box Plus/Minus nearing 95%. The evidence suggesting PIPM’s clarity makes it a contender for the most valid one-number metric in the NBA.

• Click here to see an in-depth overview of PIPM
• Click here to see seasonal and multi-year PIPM leaders

[3] Conclusion

The premises of the aforementioned statistics – BPM, RAPTOR, RPM, and PIPM – imply an important principle: metrics are great indicators of player value. The term “value” initially garnered negative noise as it was generally used in a situational context, but the establishments of these one-number metrics created the “isolated” value measurements. They account for the quality of teammates and opponents to confidently estimate player impact on a leveled playing field. These ideas have been expanded on to create the concepts of portability (how well a player’s skills scale alongside great teammates) and diminishing returns (lessened situational value alongside greater teammates). Advanced statistics clear a lot of noise around traditional stats, introducing analytic concepts and new calculation approaches to (very precisely) estimate a player’s value. Advanced statistics will remain a principal tool in player analysis, and a firm grasp of their processes and measurements is a fundamental step in analytic retention.

Determining the most valuable player in NBA history is one of the foremost prompts in the game’s historic contexts. It’s a multidimensional dispute that remains largely unoptimized. The existence of varying criteria is increasingly prevalent, especially during the imminent rise of reference points to base claims on. Although an opinionated nature to the aforementioned topic isn’t anomalous, these types of reasoning blur the more grounded viewpoints that garner stronger results. Employing the most sound rationale and assimilating the most valid measurements paints a relatively clear picture of the value of basketball’s all-time greats. These adjustments to the traditional techniques create one variation of determining the most valuable basketball player in history.

[1] Rationale

Similar to the aggregate of sports analysis, the following determination will and won’t include some of the more widespread elements in likewise analyses. The criteria aren’t intended to act as a definitive, ultimate rationale toward the topic, rather one singular approach to answering one of basketball’s most intriguing questions.

[1.1] Exclusions from Convention

If one were to examine the prototypical rationale of one singular player determination, it would include that player’s legacy as a highly-weighed factor. The number of MVPs and championships won, All-NBA and All-Star appearances, and transcending influence create a fraction of the components in a player’s legacy. The following determination will not place stock in a player’s legacy. Single-season awards aren’t perfectly assigned, nor do they point toward the true value of a player. Similarly, winning championships isn’t an individual operation. Backpicks‘s primary player valuation model estimates the very greatest player seasons provide the players’ given teams an extra 30% odds to win the title that season, illustrating the importance of the supporting cast in title-winning teams. Despite the mind-boggling performances of LeBron James and Kawhi Leonard in the Playoffs during the past seasons, one player isn’t expected to provide a team with an entire championship’s worth of equity. Team success is a commonly-referenced element in determining player value, but its large dependence on circumstance and situational conditions eliminates it from the criteria of the following assessment. 

For example, Player A was credited with an increment of 20% title odds and Player B was credited with an increment of 16% title odds in the same season (including equal health). The latter player’s team won the championship that year. It could appear that Player B was worth more championship equity because of his team’s success, but Player A had truly been the more valuable player that season.

The additional point of disparity from tradition is in the types of statistics worth referencing. Standard points of statistical reference include the traditional recordings of points, rebounds, assists, steals, and blocks per game and the shooting slash line in which field goal, three-point, and free-throw percentages are included. The former statistics create a tenuous model to statistically evaluate a player, especially in answering the aforementioned question, considering the cross-referencing between different eras (on a side note, players won’t receive less credit due to playing during a certain time). Although the presence of pace-adjusted (per 75 possession) and cumulative efficiency (eFG%, rTS%) statistics are primary reference points in individual player analyses, the following determination will rely on skill-oriented statistics as secondary credentials to make way for compound player valuations (the most valid one-number metrics) as primary components. The statistics referenced in the following assessment were selected due to the quality of sources, validity in year-to-year consistency (especially with lower lineup continuity), and positive retrodiction testing.

[1.2] Inclusions from Resource Pool

Although it’s a commonly untapped subject in the assimilation of a list related to the aforementioned question, visual elements will act as an important aspect in determining the most valuable player in history. Film study acts as a foremost consideration in current and historic player analysis, adding needed context to the scores displayed through statistics. Skills including on and off-ball defense and quality of passing aren’t audaciously measured, and thus prompt the need for visual analysis. 

“Film study” as it’s defined in the following assessment isn’t intended to correlate with the “eye test,” the latter of which uses visual methods to determine worth, while the former simply adds context to the measurable and supplies the immeasurable

The primary framework in the following determination is the positive equity a given player had in a variety of lineups. Similar to the “The Backpicks GOAT” list, the following assessment is partially based on CORP, a model that determines the odds a player provides a random team with winning an NBA championship, through which the term “value” is defined for the following assessment. The primary aim of a team is the win an NBA championship, and the most valuable players are the ones who provide their teams with the greatest odds to win. CORP will serve as an important, but not definitive, factor in the determination. It’s the most valid widespread metric to determine random title odds, but is largely afflicted by injuries in the Playoffs, which slightly hinders some of the results. Stephen Curry was credited with one of the five most proficient peaks in league history, but his championship odds during the 2015-16 season were limited to a strong MVP level campaign (as opposed to an all-time level) due to an injury in the second season. Player seasons without health hindrances create for the clearest view of true CORP scores, and the premise of the metric vaults it into consideration.

The bulk of the remaining metrics cited throughout the following evaluation consists of Plus/Minus and “With or Without You” (WOWY) scores. The former category will encompass the more widespread Box Plus/Minus (BPM) statistics. Basketball-Reference‘s model will act as the primary resource for BPM scores, but the Backpicks version of the metric will also include citations throughout the assessment. The remaining Plus/Minus statistics will include the versions enhanced with play-by-play data: scaled Adjusted Plus/Minus (APM), Regularized Adjusted Plus/Minus (RAPM), and likewise metrics. APM models were created to contextualize and expand On/Off Plus/Minus totals, the latter of which often provided poor results. APM statistics factor in teammates played (a generally weaker supporting cast with one great player on the floor could skew On/Off results) and the quality of opponents to measure a player’s impact on his team within a game. RAPM models were spawned through APM but employed the Bayesian technique, the usage of which eliminates the irregularities of anomalous data in APM models. RAPM acts under the same premise of APM, but wields a ridge regression to garner stronger results.

Although APM and RAPM models are widely recognized as the most valid one-number metrics to evaluate basketball players. they aren’t the only statistics worth considering. As stated earlier, WOWY models will be employed to examine a similar construct through a slightly varying viewpoint. They estimate a player’s impact with a Plus/Minus-esque format but operate on a game-to-game basis rather than within a game. APM models work with data representing a player’s impact within a game, or how the team performs when a player is on and off the floor and shuffled into different lineups. WOWY measures a player’s impact from game-to-game, acting as a pure tool to approximate a player’s value on his roster. The grounds of WOWY removes the prospects of needing to cut highly correlated factors either manually or through employing regression methods to eliminate multicollinearity, which is more common in lineup-oriented statistics. APM and WOWY possess their strengths, but the former metric will act as more primarily cited, as the foremost principle in answering the sport’s most prevalent question is to provide a highly isolated estimate of impact. Consideration will be placed in APM and WOWY models, but with the underlying premise as to the true measurements of either metric.

Additional advanced statistics including Win Shares or WS/48 may/will be cited solely to cross-reference between eras. The validity of the Win Shares metric is surpassed by the bulk of impact metrics; the most foolproof of them were explained earlier. See Nylon Calculus‘s article for an explanation as to which advanced statistics are most valid in player analysis

The assimilation of impact measurements begets the necessity to balance longevity and peak. For example, Player C was worth a 2.01 career valuation with a peak of 24% title odds, while Player D receives a career valuation of 2.09 with a peak of 20% title odds. The determination between Player C and Player D largely depends on a preference of either longevity or peak. The following assessment will consider either side of a player’s résumé to determine the most valuable player in history (i.e. total career contributions won’t outweigh similarly lengthy careers with superior peaks, or vice versa). There doesn’t exist a direct template to compare longevity and peak in the following exercise, and neither aspect will act as the definitive factor in the determination.

[2] Determination

The employment of the criteria in the preceding texts drew forth one perspective’s answer to determine the most valuable player in NBA history. The player performed exceptionally well according to visual and analytic principles, outlined some of the most optimal odds to provide a random team with winning a championship, and portrayed some of the highest impact metric scores to support his title as the most valuable player in history: Michael Jordan.

Although a wide variety of players was examined for the exercise, the criteria slimmed candidacy down to three strong potential recipients: Kareem Abdul-Jabbar, LeBron James, and Michael Jordan. The prospect of comparisons will include the trio of these contenders.

[2.1] Input

Jordan’s most valuable asset was his historic engrossment in scoring with efficiency and volume. Jordan concluded his career with an average of 30.3 points per 75 possessions and a +3.5 rTS% (rTS% is referenced in place of TS% to account for variances in scoring efficiency across eras). His world-class athleticism and verticality created one of the greatest inside scoring threats in league history. The majority of basketball viewers are familiar with his up-and-under layups and free-throw line dunks, both of which exemplified the lengths through which Jordan could score the basketball. The post wasn’t Jordan’s primary scoring range, as he attempted far more shots from the mid-range during the shot tracking era. Limited shot tracking data allows for range examination in the concluding years of Jordan’s career. During the 1996-97 regular season, he made more than 50% of his shots from mid-range except from ten to sixteen feet, in which Jordan remained close to the aforementioned mark (49.2%). Jordan was extremely proficient inside the arc, in which he made 51% of his attempts. Conversely, Jordan’s career three-point efficiency was 1.3% lower than the cumulative (from every season in which Jordan played) league-average. His scoring remains unparalleled in league history.

Backpicks compared Jordan to several all-time greats (Bird, Bryant, Curry, James, Johnson, and Nash) in terms of the site’s “Big 4” offensive factors: scoring efficiency, creation, scoring volume, and retention, adjusted to mirror Playoffs-scaled three-year peaks. Jordan placed fourth in scoring efficiency (rTS%), third in creation (Box Creation), first in scoring volume (PTS/75), and first in retention (TOV%). 

Jordan’s impact on the opposite side was among the most proficient displays of defense from a shooting guard in league history. Backpicks revealed Jordan record six of the two-hundred highest steal percentage (STL%) scores, the foremost of which was the forty-second highest score in the history of its tracking. His steals garnered high praise at surface value, but Jordan’s defense wasn’t consistently elite. During the first three seasons of his career, Jordan received a reputation as a defensive gambler. He’d steal the basketball at an extremely high rate, but at a price that often manifesting as points for the opposition. Jordan’s defense massively improved in his fourth season, during which he conveyed career-highs in STL%, block percentage (BLK%), and DBPM. His on-ball defense was the asset that created Jordan’s elite defensive campaigns, through which he opted for smarter defensive rotations and superior paint protection. He recorded the seventh-highest single-season BBR DBPM score that season. Jordan evolved into a less active defender during the 1990s, a time in which he’d seemed to reserve energy for stronger efforts on the offensive end. 

[2.2] Output

The bulk of widespread one-number metrics point toward Jordan as one of the greatest players in basketball history. He holds the career record for BBR BPM (9.22), maintaining a mild advantage over the second-place recipient. Jordan’s prime consisted of the second-highest single-season BBR BPM score (12.96 in 1987-88) and the highest single-season BBR VORP (a BPM-based metric that accounts for playing time) score (12.47 in the same season), creating one of, if not, the greatest primes in basketball history from box-oriented view. Jordan’s productivity translated to the Playoffs at the highest level of any player, posting the highest career BBR BPM of the second season (11.14) in league history. Jordan was also credited with the second-greatest postseason run since the Box Era, conveying a 14.63 BBR BPM during the 1991 Playoffs. Although the bulk of Backpicks data remains proprietary, it is known Jordan holds the second-highest peak BP BPM score (12.6) in basketball history. Despite the recurring retirements throughout his tenure in the NBA, Jordan’s impact on the court creates one of the greatest careers in league history.

Jordan’s impact on his teams transcends the box score, exhibiting an equally strong résumé in play-by-play-oriented advanced statistics. Backpicks‘s manipulations of WOWY created the WOWYR metric, the latter of which employs a ridge regression. RAPM’s claim to fame revolves around the same modeling, placing stock in the rationality of certain ranges. Jordan was exceptionally valuable through the WOWYR keyhole, posting a prime (1985-1998) WOWYR of +9.0 and a career WOWYR of +8.2, materializing the impact he had on his rosters. Backpicks followed its commencement of WOWYR with “GPM,” a set of game-scaled APM statistics, through which a ten-year scaled version was created. Jordan had one of basketball’s greatest decade-long runs according to GPM measurements, in which he recorded a +7.6 average game value. Limited seasons of APM, RAPM, and Augmented Plus/Minus (AuPM) – which mimics APM without the direct play-by-play data used in APM – paint a clear picture as to Jordan’s value near the tail end of his tenure in Chicago. Backpicks estimates if APM calculations were attainable during the earlier stages of his career, Jordan could’ve marked some of, if not, the greatest APM/RAPM scores of any player in league history. 

Backpicks‘s statistical profile of Michael Jordan unveils a set of data to largely support his candidacy as the game’s most valuable player ever. He holds the fourth-highest prime WOWYR score (higher than Abdul-Jabbar and James) and the ninth-highest three-year AuPM/RAPM score (which doesn’t act as a true representation of his prime, as the seasons in which the scores were recorded ranged from 1996-98), but points toward the potential greatest-ever status of Jordan’s prime.

The data that truly exemplifies Jordan’s historic prime lies in the Backpicks CORP model, represented through “Title Odds on a Random Team.” As the title suggests, CORP measures the odds a player would provide a random team of winning an NBA championship. The “CORP calculator” draws a proprietary “SIO” measurement: the impact a player would make on a 0 SRS team. The SIO value is plugged into an “SIO curve” that accounts for diminishing returns among stronger lineups (a player’s impact is lessened on a greater team compared to a weaker supporting cast). The CORP grading scale includes eight tiers to categorize variances of title odds, ranging from a role player to a “goat” season. Following the 2018-19 season, Jordan contributed the third-greatest cumulative title odds, preceding competitors in Abdul-Jabbar and James. The balancing of longevity and peak was the concluding factor of Jordan’s relevance in the statistic, through which mean title odds during a career were more highly weighed compared to total career valuations. Among the top three candidates, Jordan was the sole player to contribute title odds of more than or equal to thirty percent, a feat he accomplished in three consecutive seasons (1989-1991). 

Abdul-Jabbar, James, and Jordan conveyed varying title odds according to the Backpicks CORP model. Abdul-Jabbar received a career valuation of 2.99, which stands as the highest score in league history through the 2018 season. The seasonal value of the aforementioned score, which will be referred to as “CPS” during the exercise, grants Abdul-Jabbar a mean score of 0.15 per season. His absolute peak in 1977 recorded title odds of around 26%. James receives a career valuation of 2.79, which doesn’t account for his performance in the last two seasons, but places second among players in history. His average CPS of 0.17 suggests James could pass Abdul-Jabbar this season and will likely retire as the most valuable player in purely cumulative value. James peaked in the 2012-13 season according to the CORP model, displaying title odds marginally exceeding 28%. Jordan concludes the trio as the foremost player in terms of “rate CORP.” He garnered a career valuation of 2.81 during the fifteen seasons of his career, which can be extrapolated to display a 0.19 CPS. Jordan peaked between the 1989 and 1991 seasons, during which he maintained title odds around 31%, cementing his status as the greatest peak player in league history.

[3] Conclusion

The most important and sole constant to retain during G.O.A.T. or B.O.A.T. or “MVP of all-time” discussions is that there is neither a correct nor incorrect answer. Advanced statistical models including APM and CORP don’t paint a definitive picture of a given player’s value but strongly estimate it, through which grounded decisions are still drawn. Additional and varying evidence can be interpreted as stronger value components, which begets the variance in criteria (e.g. the balance of longevity and peak). The determination of Michael Jordan as the most valuable player in league history is one viewpoint on the topic using a singular rationale. Foremost basketball questions including the “MVP of all-time” discussion are intentionally open-ended to account for the differences in modeling to incorporate new rationale when it comes to view. Despite the multidimensional options to answer the aforementioned question, the employment of the preceding criteria results in a singular point of optimal player value: Michael Jordan.