This study has been featured in SB Nation and Forbes.

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I enjoy watching sports shows like ESPN First Take, The Starters, Undisputed, Inside the NBA, Pardon the Interruption, ESPN around the Horn, Fox Sports Live, NBA The Jump and so on, where the timeless debate of who is the best basketball player of all time periodically comes up as a subject for debate. I also enjoy reading sports blogs, and the different attempts by analysts to answer this question. So I decided to throw my hat in the ring, and take an objective data driven approach to answer this question! I’m going to not only answer who is the best basketball player (mid 1970s — early 2000s) from a playoff performance perspective, but also show how various players rank against each other using a weighted ranking system I created and named the GOAT (GT) Index. Big shout out to my buddy Chris Carter, who gave up some of his time to be a second pair of eyes and review my work! As a disclaimer I will be doing my analysis in excel, and pulling my source data from www.basketball-reference.com. I audited the numbers at www.basketball-reference.com by matching them against ESPN.com, and they checked out. Before we get into the math and fun stuff, we have to call out a few caveats.

Legendary players who never won championships.

Everybody’s Welcome!

In G.O.A.T conversations, many analysts cite having at least one championship as a requirement to be considered in the discussion. I must admit, I used to think this way as well, but I’ve recently come to my senses thanks to some soul searching. Using championships won as a “membership card” automatically disqualifies many amazing players who may have missed winning a championship for a variety of reasons (e.g. injuries, team chemistry, a bad agent or bad management, a key play that could have gone either way, etc.). On the flip side of the equation, there are players like Robert Horry, who was not the best player on any of the teams he played on, but he’s hit big shots in key games, and he’s also been on the lineup of a few championship teams; and as a result has won 7 rings. Championships speak to overall team performance, not necessarily to how good an individual player is. Individual players don’t win championships in the NBA, no — teams win championships. So, taking all that into consideration, I made the painful decision to leave championships won out of this analysis. I’m also going to consider every star player from the late 70s, 80s, 90s till present that has played 10 or more seasons. This means we’ll now be able to see how Kobe stacks up against Jordan, how younger LeBron ranks against Dr. J, how Malone stacks up against Rodman, how Stockton stacks up against Nash, and many more combinations!

Metrics

For this analysis, I will only focus on playoff, on the court stats. Just like championships, I will not be considering MVP awards in this analysis as those are subjective and based on the opinions and votes of media panelists. I will also not be giving special consideration to attributes like a players marketability, popularity, how many times a player made the highlight reel, scoring titles broken, All Star appearances, player-coach relationships and so on. I’ll be strictly looking at in game playoff metrics on a weighted per 36 minute basis (i.e. statistic divided by minutes played and multiplied by 36) for each individual player so we look at the quality of each players performance without being limited to rotation minutes (by coincidence, 36 minutes also matches up with the average playoff minutes played by our selection pool). I considered many different statistical formulas as I planned this assessment: I like the Performance Index Rating that teams in leagues across Europe use for MVP ratings; the only issue I have with the PIR formula is that it doesn’t account for pacing (the speed/rate at which different teams execute plays, nor does it account for rotation minutes). In the same vein, when it comes to American basketball, I will not be using the PER stat, as many basketball experts agree that it’s not an ideal way to measure player performance as it unfairly favors offense over defense. Instead, I’ve decided to use these metrics to evaluate the players:

Points per 36 minutes (PPG) : This metric assesses the average number of points scored by each player in the game, or in the case of this study, scored per 36 minutes. This is a very basic stat that speaks to volume and assesses how often a player scores. It does not however assess how efficient a player is at scoring.

: This metric assesses the average number of points scored by each player in the game, or in the case of this study, scored per 36 minutes. This is a very basic stat that speaks to volume and assesses how often a player scores. It does not however assess how efficient a player is at scoring. Effective Field Goal % per game: This stat addresses player scoring efficiency. I like the eFG formula because it captures the full picture as it features Field Goal attempts, and it also adjusts for 3 pointers (eFG% = (FGM + (0.5 x 3PTM)) / FGA). The only minor issue I had with this formula was that it credits 3 pointers twice (threes are baked into FGM in the numerator), which makes some sense if we consider the general theory that shots are harder to make the further away we move from the basket. You might ask, why didn’t I use the True Shooting % metric instead? The formula for True Shooting % is Total Points Scored / (2 * True Shooting Attempts) where True Shooting Attempts is Field Goal Attempts + (0.44 * Free Throw Attempts). My biggest beef with the TS formula besides the random 44% Free Throw Attempts weighting is that it doesn’t account for threes, but rather lumps everything together. I can live with the subjective 50%+numerator % weighting for three’s in the eFG formula, so I stuck with that. We’ll be including this metric on a per game basis.

This stat addresses player scoring efficiency. I like the eFG formula because it captures the full picture as it features Field Goal attempts, and it also adjusts for 3 pointers (eFG% = (FGM + (0.5 x 3PTM)) / FGA). The only minor issue I had with this formula was that it credits 3 pointers twice (threes are baked into FGM in the numerator), which makes some sense if we consider the general theory that shots are harder to make the further away we move from the basket. You might ask, why didn’t I use the True Shooting % metric instead? The formula for True Shooting % is Total Points Scored / (2 * True Shooting Attempts) where True Shooting Attempts is Field Goal Attempts + (0.44 * Free Throw Attempts). My biggest beef with the TS formula besides the random 44% Free Throw Attempts weighting is that it doesn’t account for threes, but rather lumps everything together. I can live with the subjective 50%+numerator % weighting for three’s in the eFG formula, so I stuck with that. We’ll be including this metric on a per game basis. Free Throw % per 36 minutes : This is the missing component in the eFG formula, so I’ll measure it as a standalone.

: This is the missing component in the eFG formula, so I’ll measure it as a standalone. Total Rebounds per 36 minutes: I will be looking at total rebounds. Rebounds prevent the other team from scoring and also grant second chance points.

I will be looking at total rebounds. Rebounds prevent the other team from scoring and also grant second chance points. Assists per 36 minutes: Dropping dimes allows teammates to score. Now it would have been nice to know the breakout (worth/weight) between 2pt and 3pt assists, but I couldn’t find any metrics or advanced formula that represented holistic assists.

Dropping dimes allows teammates to score. Now it would have been nice to know the breakout (worth/weight) between 2pt and 3pt assists, but I couldn’t find any metrics or advanced formula that represented holistic assists. Steals per 36 minutes: Steals factor in greatly when it comes to on the ball defense. They speak to a players ability to strip the ball from their opponent, and the ability to anticipate passes.

Steals factor in greatly when it comes to on the ball defense. They speak to a players ability to strip the ball from their opponent, and the ability to anticipate passes. Blocks per 36 minutes: Like the steal, the block is also a key defensive metric, and speaks to how well a player can swat/deny a field goal attempt without fouling.

Like the steal, the block is also a key defensive metric, and speaks to how well a player can swat/deny a field goal attempt without fouling. Turnovers per 36 minutes: This speaks to a players ability to handle the ball. As a standard rule, less is best with this metric.

This speaks to a players ability to handle the ball. As a standard rule, less is best with this metric. Usage Rate: This advanced metric speaks to the percentage of team plays used by a player while he was/is on the floor.

This advanced metric speaks to the percentage of team plays used by a player while he was/is on the floor. Personal Fouls per 36 minutes: Personal fouls are liabilities for teams as they lead to bonus free throws, ejections, etc. We’ll look at this from a paced view. As a standard rule, less is best with this metric.

Personal fouls are liabilities for teams as they lead to bonus free throws, ejections, etc. We’ll look at this from a paced view. As a standard rule, less is best with this metric. Win Shares per 48 minutes: The Win Share metric is designed to estimate a player’s contribution to the team in terms of wins. It is an advanced statistic used to determine how much credit to give to each player for every win. It follows the same methodology as stocks: a team is given “shares” for each game won, and these shares are distributed across the roster based on each players contributions. The shares are broken up into offensive and defensive categories. The variables in win share formulas include possessions, points, league averages per possessions and marginal ratings. You can check out the detailed formulas for offensive and defensive win shares HERE. *Defensive and offensive awareness metrics like setting screens, help defense etc will be baked into the win share metric.

Weighting

This was the toughest part of this exercise. What value should be attributed to each basketball stat? I spent quite a bit of time scouring through coach playbooks and reading through some journals. I learned about studies done by coaches like Tom Crean of the Indiana Hoosiers (college basketball, D.1) that called out the top 4 stats as FGs, FTs, RBs and PFs. Perhaps the most popular white paper on this subject is the case made by sports statistician Dean Oliver in his book Basketball on Paper, where he ranks what he calls “The Four Factors of Basketball Success” in this order:

•Shooting (40%)

•Turnovers (25%)

•Rebounding (20%)

•Free Throws (15%)

I’m not really a fan of applying Dean Oliver’s approach to individual performance because doing so would favor big men (big men generally handle the ball less than guards meaning they are less prone to turnovers, and they play closer to the rim, meaning more rebounds and potentially more fouls/free throws). So after searching the internet for a few days and not finding any weighting system to my liking for ranking metrics across different positions, I decided to keep everything equal:

•Points per 36 minutes % — 9.09% (9.1%)

•Effective Field Goal % — 9.09% (9.1%)

•Turnovers per 36 minutes — 9.09% (9.1%)

•Total Rebounds per 36 minutes — 9.09% (9.1%)

•Free Throw % per 36 minutes — 9.09% (9.1%)

•Assists per 36 minutes — 9.09% (9.1%)

•Steals per 36 minutes — 9.09% (9.1%)

•Blocks per 36 minutes — 9.09% (9.1%)

•Personal Fouls per 36 minutes — 9.09% (9.1%)

•Usage Rate — 9.09% (9.1%)

•Win Shares per 48 minutes — 9.09% (9.1%)

Given my personal experience playing basketball I like this breakout as it reflects what I’ve seen on the court. It’s also not disproportionately skewed toward one metric. I also like this approach because different player roles generally skew toward certain metrics. To illustrate, I sorted the data in my selection pool and found that smaller players tend to be better at assists, steals and generally have better free throw %’s while bigger players tend to be better at blocks, rebounding and tend to have better eFG%’s as they play closer to the basket. Metrics like personal fouls tended to favor guards (they generally commit less than big men as they spend less time in the lane guarding the basket). Turnovers, points per 36, win shares per 48 and usage rates all had pretty even spreads. This all means that there was a pretty even distribution in our pool when it came to how each metric favored different player types. By keeping all our weights even and using the GT index, no one particular type of player will be favored, and the player/s with the best all around game will rank highest.

The Players

Below are the players I will be evaluating in the analysis:

Honorable Exclusions: Unfortunately, metrics like blocks, steals and turnovers were not officially recognized and recorded by the NBA and ABA leagues until the early 70s. This means that 50s and 60s legends like Wilt Chamberlain, Bill Russell, Oscar Robertson, Elgin Baylor, John Havlicek and Jerry West will be excluded from this analysis, as no record of their performance in these areas exists.

It’s worth noting that some of the players in the selection pool are still active (e.g. LeBron, Wade) which is why these kinds of studies are done periodically to account for aggregate changes in metric stats.

Setting Expectations

Which is more important: Jordan winning 6 titles in a row vs LeBron making a legendary comeback against a powerful Warriors team to win a championship? Allen Iverson offensively carrying the 76ers team on his back to the finals vs Kobe Bryant scoring 81pts in a game? These are all great debate topics that have no definitive quantitative answers. They are also subjective topics: Both Jordan and LeBron did not win championships on their own (basketball is a team sport). Allen Iverson may have carried the 76ers with offense, but Raja Bell and Mutombo were the defensive cornerstones for that 2001 team. Kobe had his 81 point game against the second worst defensive team in the NBA during the 05–06 season. I’m not taking anything away from these amazing players, but I just want you to see why it’s important to have context and set the right expectations. This analysis is NOT designed to assess subjective debate themes. What it will answer is which player was the best performer on the court based on holistic, objective weighted scores I will generate for each player based on career playoff stats. We’re looking at performance data, and nothing else.

Historical Context

It’s also important that we set historical context: “Rule changes” are a popular argument with most analysts who argue that one generation has it easier than the next. It is true that rule changes occur frequently over the years (e.g. hand checking in 2004, defensive three second rules in 2001, technical fouls for false timeouts in 1996, and so on). But we have to acknowledge the fact that players adapt to their eras. Hand checking may have had an impact on one-on-one scenarios in the 80s and 90s, but a player from those eras may struggle in todays game as coaches over the years have innovated, improving team defensive schemes and help defense rotations. Everything is relative. So on that note, we are not going to account for rule changes in this analysis.

Now we are ready to build our index.

The GT Index

For the first step, I got all our players together in a nice excel sheet (Figure 1). Notice how the average minutes played for the players in our pool comes to 36.78. Remember we adjusted all the raw performance stats against 36mins, so we are in good shape. eFG% and Win Shares are the only two metrics we are assessing per game and per 48mins respectively.

Figure 1. Our complete player pool w/playoff metrics. Click to enlarge.

For the next step I indexed and ranked the individual categories (Figure 2). You can see the weights I assigned on the top row. Personal fouls and turnovers are negative metrics (e.g. the less turnovers that a player commits, the better) so I used a reverse index formula for these two metrics. To illustrate, Robert Horry ranked 1st in the turnover metric. This ranking means that he performed the best with turnovers, not that he had the most turnovers! In fact it’s quite the opposite — he had the lowest turnover rate! This is why his index in that category is the highest.

Figure 2. Indexed table, sorted by turnovers. Click to enlarge.

When I sorted the table by Win Shares/48 (Figure 3), Michael Jordan as expected rose to the top. If my weighting scheme is correct (setting all weights the same at 9.1%), I expect the top players in the final rankings to be similar to the top players in the Win Share sort.

Figure 3. Table sorted by Win Shares/48. Click to enlarge.

Next I created scores for each player’s categories by matching up the indices with the weights, and then combined the metric scores to come up the final scores. This table (Figure 4) represents the final player performance rankings from our pool. Hakeem Olajuwon emerged as the top ranked player. Let’s dig deeper.

Figure 4. Final Player Performance Rankings. Click to enlarge.

Sorry Mike.

Michael Jordan is not the G.O.A.T???

Let’s get right to the big question: how come Michael Jordan is not #1 on the list? Jordan and Hakeem were almost dead even, with Hakeem edging out Jordan by a fraction of a point. As a disclaimer, Michael Jordan is my favorite player of all time (although that might change in the near future as LeBron James is competing for MJ’s affections in my heart). Winning six rings back to back, all the crazy offensive and defensive plays, coming back and winning a championship after losing his father, sealing his career with a game winning shot, the two handed block when he returned as an older player with the Wizards…all these memories cement the mystique that is the Jordan brand. I bring all this up to say that I myself was rooting for him to take the top spot. So how come MJ is not #1 on the list? To answer that question, we’ll have to take a surgical view. This is where things get really interesting. Check out the below table (Figure 5) where I’ve grouped the top 10 players. Surprisingly, Jordan ranks 20th in the eFG category:

Figure 5. Top 10 players. Click to enlarge.

Breaking down the eFG metric

Ben, you’re crazy, don’t you know Jordan won the most scoring titles in the NBA (10)???

Yes, he definitely did, which reflects clearly in his PPG/Points per 36min ranking (1st). Scoring titles are awarded to players with the highest points per game in a season. The eFG on the other hand is a more well rounded scoring metric, in that it not only considers points per game, but it also considers field goal attempts! Winning scoring titles does not necessarily correlate with a players scoring effectiveness. It just simply refers to players that scored the most points. This has been one of the major criticisms of players like Kobe Bryant: taking too many shots, many of them being low quality/bad shots. So yes, a player can win a scoring title based on volume, but that does not necessarily make them an effective scorer. Some may argue that power forwards and centers tend to have higher eFGs as they play closer to the basket and are rewarded with higher percentage shots. This may be true in a directional sense, but then check out Figure 6 below, which I’ve sorted by the best eFG performers. How do you explain Bernard King (SF), James Worthy (SF), Chris Mullin (SF) and perhaps most importantly, Ray Allen (SG, same as Jordan) being on that list?

Figure 6. Top 10 eFG performers.

The eFG ranking was the biggest X factor in Jordan’s final ranking of #2 on the overall list.

We’ve been looking at overall rankings, let’s dive into the best of the best for each position.

Top 5 Point Guards

Figure 7. Top Point Guard Rankings (6th — Steve Nash; 7th — Chauncey Billups; 8th — Gary Payton). Click to enlarge.

Magic Johnson topped the list with point guards, and he led this group in the effective scoring, assists, free throws and win shares categories. John Stockton was #2, and he came in second or third in almost every category. Isiah Thomas came in at #3, with his strongest performance in the steals category, which makes sense when you think of the Bad Boy Pistons from the 80s/90s. Iverson comes in at #4 among the top point guards. Iverson was the consistent scoring option on the 76ers (as you can see from the chart above his usage rate was #1 out of every player in our pool) which meant more attempts and in essence lower shooting percentages. Iverson also did not rank well with assists (the top 5 assist guys in order are Magic Johnson, John Stockton, Steve Nash, Isiah Thomas and Jason Kidd). However, Iverson still carried his team offensively in the 2001 Finals against the Lakers. And what more can be said about Jason Kidd at #5. He was the centerpiece for the New Jersey Nets during their electrifying 01–02 season that saw them make it to the Finals. Jason Kidd, in my humble opinion is perhaps the player with the best court vision to ever play the game (followed in close second by John Stockton). It should be noted that both Jason Kid and Iverson had almost identical final GT scores (0.989 to 1).

Top 5 Shooting Guards

Figure 8. Top Shooting Guard Rankings (6th — George Gervin; 7th — Reggie Miller; 8th — Ray Allen). Click to enlarge.

Michael Jordan ranked #1 among shooting guards, no argument there. Tracy McGrady, at least for me, was the surprise in this category, coming in at #2. Despite having the worst eFG%, out of our top shooting guards McGrady came in second with his rebound and assist rates, topped the list when it came to blocks, and topped the list when it came to personal fouls and usage rate. Many people forget that once upon a time McGrady was in the national conversation when it came to Jordan “successors.” And had he not left the Magic prematurely, he would have teamed up with a rookie called Dwight Howard, and potentially formed a one-two punch similar to what Kobe and Shaq established in Los Angeles during the early 2000s.

Speaking of Kobe; Mr. Bryant was another surprise on this list. Out of the top 5, the Black Mamba had the lowest rankings when it came to rebounds, assists and blocks. And despite being known as an electrifying scorer (he had the second place ranking with PPG/per 36 out of our top shooting guard list), Kobe could not pass Dwyane Wade when it came to field goal effectiveness or defense. So is Kobe an overrated player? I don’t think so. The Black Mamba may not have been the best all around player, but he is one of a few players in NBA history to remain loyal to an organization for his entire career (through good and bad times), he is one of the best buzzer beaters of all time, he was arguably the best player on at least two of his 5 championship teams, and from my analysis, he made the top 5 shooting guards list of all time. I would pick Kobe any day as a teammate.

Top 5 Small Forwards

Figure 9. Top Small Forwards Rankings (6th — George Gervin; 7th — Reggie Miller; 8th — Ray Allen). Click to enlarge.

LeBron James ranked #1 among small forwards, no argument there. We should note that LeBron is a one of a kind player that can play effectively at a number of positions, but I placed him as a small forward as that is what he normally defaults to. From his rankings we can see why so many people view King James as the most complete player in today’s era: he leads the small forward pack in almost every single category, and it’s scary to think that his career is not over yet. In fact, when I run this analysis again in a year or two (waiting for Steph Curry to hit his 10 year anniversary) I suspect LeBron will move up in the overall rankings! Dr. J came in second with his strongest performances in the defensive categories (steals and blocks). Larry Bird torched the free throw, assist and rebound rankings, while Scottie Pippen dominated defensively with steals. James Worthy emerged as the player with the highest eFG from the group, and he also topped the list when it came to taking care of the ball (turnovers).

Top 5 Power Forwards

Figure 10. Top Power Forwards Rankings (6th — Kevin McHale; 7th — Karl Malone; 8th — Robert Horry). Click to enlarge.

Tim Duncan came out on top for the Power Forward position. Duncan was the centerpiece of the San Antonio Spurs from the moment he arrived in 1997 to his retirement 19 seasons later in 2016. Within our top 5 Duncan had the second best eFG%, had the best usage rate and win shares, and also led in the blocking category. Charles Barkley ranked at #2 among power forwards, leading in the eFG%, rebounds, assists and steals categories. Dennis Rodman was a very crafty player, the enforcer for Jordan’s Bulls who was a master at getting into the heads of every teams star players. And Rodman was effective at what he specialized in: rebounds, defense and hustle plays. But Rodman did not have an all around game, and he also fouled out quite often. I have to say that I was surprised Shawn Kemp at #5 ranked above players like Karl Malone, Chris Webber and Dennis Rodman. Shawn Kemp’s strong categories were with rebounds, free throws and defensively with steals and blocks. Shawn Kemp was a force to be reckoned with in his prime, and even an overweight Shawn Kemp played a significant role in getting the Cavs to the playoffs in the 97–98 season. This begs the question: WHY HASN’T SHAWN KEMP BEEN HONORED IN THE HALL OF FAME??? Something to think about.

Top 5 Centers

Figure 11. Top Centers Rankings (6th — Dikembe Mutombo; 7th — Moses Malone; 8th — Bob Lanier). Click to enlarge.

The fight for the top Center spot was a tough one between Olajuwon and Kareem Abdul-Jabbar, with Olajuwon edging out Jabbar by just a fraction of a point when it came to the final scores. In all honesty playoff stat-wise they are both very even players. Both of them were extremely skilled big men, and they both topped the assists and free throw categories. The difference came with rebounds and blocks, with Hakeem edging out Kareem in both those categories. David Robinson ranked at #3; a phenom that reminds me in many respects of LeBron James perhaps minus the guard skills. I make that comparison because David Robinson was an athletic center that played like a small forward. The Admiral came in second in the defensive categories with steals and blocks, and was the best among centers at taking care of the ball. Artis Gilmore, an effective scorer and rebounder, came in at 4th. And finally, Shaq, without a doubt the most powerful and physically imposing center in the modern era, ranked at #5. Shaq logged the best eFG rating in the group, which is to be expected based on how close he played to the rim and how easily he dunked on other players in his prime years. He also ranked second in the rebound category, and ranked second when it came to usage rates. But when it came to other categories, specifically free throws and steals, Shaq’s rankings were disappointing to say the least.

Conclusion

Hi! I’m Ben!

When I set out to run this analysis, I wanted the data to speak, and not my opinion or emotions. To accomplish my goal, I had to look at the full picture, and assess each players performance against a pool of their counterparts from a holistic perspective. The results demonstrate how important overlooked points can be. In sports we tend to place a lot of emphasis on offense and milestones like championships as the main value drivers of a players worth, but in this analysis we demonstrated how little such accomplishments have to do with a players holistic profile. We also stressed the importance of looking at the full picture, and one of the key highlights was how different things look when we employ the effective field goal statistic in tandem with points per game. The eFG stat allows us to consider not just points scored per game, but it factors in field goals attempted which gives us a more complete picture of a players scoring ability. Together with PPG, which allows us to see the volume of points a player is capable of putting up, and free throw ratings, we get the full scoring picture. This analysis also brings to the fore another very important point:

Size matters. We have to come to terms with the fact that basketball is a game of giants. As with anything in life, there are always a few rare exceptions to this rule (e.g. Iverson, Spudd Webb, Nate Robinson, etc). There are very few things more valuable to an NBA team than a big man with skill and power to match. That’s what made the Russell, Chamberlain and Abdul Jabbar era’s so special. That’s why Dirk Nowitzki throughout his career has been such a nightmare match up for opposing teams. A big man who can shoot from any range automatically spaces the floor (think Kevin Love). A big man with passing skills has an advantage because he (or she) can see over most players (think Chris Webber). Add ball handling skills, defensive and rebounding prowess with a touch of basketball IQ, and you have a force to be reckoned with (think LeBron James). In fact, historically, skilled big men have determined the balance of power in the NBA (think LeBron going to Miami, think Durant going to the Warriors, think Joel Embiid with the Sixers). These are the kinds of factors that made Hakeem Olajuwon so special.

The G.O.A.T

Olajuwon is the only player in NBA history to record over 200 blocks and 200 steals in the same season (88/89 season). He could defend any position. He was a big man with power that could also play a little man’s game. And this is why he edged out Jordan by a fraction of a point. The most dominant center ever, Shaq, frequently cites Olajuwon as the toughest opponent he ever faced. Even Jordan himself had this to say about Olajuwon:

Hakeem Olajuwon emerged as the #1 ranked player from the GOAT Index (mid 70s — early 00s). Perhaps now we can have a more informed debate of who would have won if he and Jordan had met in the Finals when they were both in their primes.

Playoffs GTS (GOAT Score) Formula

I created a formula based on this analysis that will enable you to calculate GT Scores for the playoffs and enable you to properly score and rank players. You can also use the same formula for the regular season, just make sure to swap out the relevant metrics:

Playoffs GTS (GT Score) formula. Click to enlarge.

If you want the formula for Total GTS (regular season + playoffs performance baked into one score that represents a players complete performance profile), read this article.