Player Career Score: A Full Explanation

I started what would eventually become the Player Career Score (PCS) around 2010 for two reasons. First, I wanted to boil a all the accomplishments and achievements of a player’s career into a single number, which could then be compared to other players throughout the history of the NBA. Second, I wanted to determine who should be in the Hall of Fame. At the time I started this Steve Nash had something like a 15% chance of getting into the Hall of Fame based on www.basketball-reference.com and their Hall of Fame Probability Calculator. They have since adjusted their formula – he’s now at a 98.3%, but I still dislike the factors that they have identified as being statistically significant for getting into the Hall of Fame. (I do realize they’re using logistical regression and not their own subjective judgment to determine these, so they can’t be helped even if they disagree with them.) The categories are height, NBA championships, NBA Leaderboard Points (basically being top ten in a season in either points, assists, rebounds, steals, blocks, or minutes played), NBA Peak Win Shares, and All-Star Game Selections. I don’t have any doubts in their methodology that determined these are the significant factors in determining who gets into the Hall of Fame as it’s currently constructed. But I didn’t want who would get into the current Hall of Fame. I wanted who should get into a perfect Hall of Fame.

I threw height out right away – I don’t want any Hall of Fame where that matters even a little bit. NBA Leaderboard points were tossed out next, the potential for good stats on bad teams and vice versa is just too high for my liking. It might not happen in the top ten very often, but the potential problem is there, so I decided just to avoid it. I tweaked NBA championships (more on that in a minute). I changed NBA Peak Win Shares to Career Win Shares to measure a player’s longevity as well as his impact on his teams throughout his career. And I kept NBA All-Star Game Selections.

The things that PCS considers are: MVP Award Shares, All-NBA First and Second Team Selections, All-Star Selections, Career Win Shares, Championship Win Shares, Finals Win Shares, and Conference Finals Win Shares.

To explain a few of those: MVP Award Shares are a reflection of how many votes a player receives for the MVP award. Not only does the MVP of a given year win points, but every player who receives votes for MVP receives points as well. I thought it was important to take into account every MVP-caliber season, rather than just the MVP winners – especially since some wins were so much closer than others. For example, in 2016, Stephen Curry received 50 points in PCS for winning unanimously. In second place, Kawhi Leonard earned 24.2 points. 2006 was an extraordinarily tight race between Steve Nash and Shaquille O’Neal. As a result of the vote being so close/split, Nash earned 41.95 points and Shaq earned 40.65 points. As you can see, how dominant a player is during a season matters, and not all MVPs are created equal.

Win Shares can be a bit tricky to understand. For the full, in-depth, and awesome explanation of that stat, check out: http://www.basketball-reference.com/about/ws.html. The short version though, is it’s a stat that divvies up a team’s wins amongst the team’s players according to each player’s contributions. For example, in 2009, perhaps LeBron James’s best season, he totaled 20.3 win shares – good for about a third of his team’s 66 wins. (The second best player on that team was Mo Williams…) In 2016, playing with much better teammates, James got 13.6 win shares of his team’s 57 wins.

Career Win Shares is just the total amount of win shares a player has accumulated during his career. When a player wins the championship, all of his postseason win shares, for that season in which he won, are counted as Championship Win Shares. If a player loses in the Finals, his postseason win shares become Finals Win Shares. And a loss in the Conference Finals designates a player’s postseason win shares as Conference Finals Win Shares. Getting knocked out any earlier than that and the postseason win shares revert to zero. This is to control for the fact that the playoffs have continually expanded throughout the years, resulting in more teams/players getting in. We can safely say there has been a final four since the beginning of the BAA, and having something consistent from the beginning is very important when comparing across history.

All-NBA First and Second Team Selections and All-Star Selections continue to add up throughout a player’s career. Kareem Abdul-Jabbar had 19 All-Star selections – those are worth five points each in my formula, so he therefore scores 95 points just from those. Eddie Jones had three All-Star selections, so he gets 15 points there, and so on. All-NBA selections work the same way with different coefficient values.

I did not include any award that hadn’t been present since the beginning or very near the beginning of the NBA/BAA. And I left out Rookie of the Year because the quality of the rookie classes vary wildly from year to year. Even using ROY Award Shares doesn’t help the problem much.

Anyway, the formula breaks down like this:

Win Shares + (MVP Award Shares*50) + (All-NBA First Team Selections*15) + (All-NBA Second Team Selections*10) + (All-Star Selections*5) + (Championship Win Shares*12) + (Finals Win Shares*5) + (Conference Finals Win Shares*2.5)

The results are then adjusted so that 1000 is the highest score possible.

Here’s how the top 50 shake out as of today, so you can judge for yourself how well this works:

Michael Jordan 1,000.0
Kareem Abdul-Jabbar 934.9
LeBron James 874.8
Tim Duncan 786.1
Bill Russell 770.0
Kobe Bryant 747.6
Magic Johnson 734.2
Wilt Chamberlain 699.2
Shaquille O’Neal 689.8
Larry Bird 689.3
Karl Malone 637.3
Jerry West 536.2
Hakeem Olajuwon 490.7
Oscar Robertson 485.0
Kevin Garnett 476.6
Dirk Nowitzki 454.1
David Robinson 452.2
Bob Pettit 450.2
Moses Malone 448.8
John Havlicek 429.1
Charles Barkley 428.9
Julius Erving 417.4
Scottie Pippen 386.6
Elgin Baylor 384.2
Dolph Schayes 380.0
Bob Cousy 367.4
Kevin Durant 365.0
Dwyane Wade 354.4
George Mikan 345.0
John Stockton 332.7
Steve Nash 316.5
Jason Kidd 308.5
Chris Paul 307.7
Patrick Ewing 298.7
Gary Payton 291.2
Elvin Hayes 287.5
Walt Frazier 283.0
Clyde Drexler 279.3
Dwight Howard 277.9
Rick Barry 277.8
Allen Iverson 269.3
Sam Jones 260.6
Isiah Thomas 253.3
George Gervin 252.1
Pau Gasol 250.9
Robert Parish 247.3
Stephen Curry 245.1
Bill Sharman 243.6
Ray Allen 243.6
Kevin McHale 242.6

You can think of this as breaking down into tiers:

Tier I: No Questions Asked (650-1000). Examples: Michael Jordan, Kareem Abdul-Jabbar, Bill Russell

Tier II: Still No Doubt, but Not Quite Tier I (350-649). Examples: David Robinson, Moses Malone, Charles Barkley

Tier III: First-Ballot, but I Guess You Could Try to Make an Argument Against if You’re Kind of a Jerk (250-349). Examples: George Mikan, Elvin Hayes, Allen Iverson

Tier IV: In, but You’d Think about It (200-249). Examples: Bill Sharman, Dominique Wilkins, James Worthy

Tier V: Borderline (150-199). Examples: Bob McAdoo, Jerry Lucas, Alonzo Mourning

Tier VI: You Have to Convince Me (125-149). Examples: Bernard King, Spencer Haywood, Walt Bellamy

Tier VII: You REALLY Have to Convince Me (100-124). Examples: Jamaal Wilkes, Mitch Richmond, Yao Ming

Tier VIII: Extenuating Circumstances Only (Below 100): Examples: Maurice Stokes, Roger Brown, Arvydas Sabonis

(***Every player with a PCS of 200 or higher who is eligible for the Hall of Fame is in the Hall of Fame.)

A few final caveats about the formula itself. First, ABA accomplishments count for 40% of NBA accomplishments. The player pool was a lot smaller and the competition wasn’t nearly as high compared to the ABA. For example, Swen Nater made two All-ABA Second Teams, but didn’t get anywhere close to that award when he moved over to the NBA despite still being in his prime. I didn’t have any scientific reason for picking 40%, it just seemed fair after looking at how some of the ABA players stacked up to the NBA players of their era.

Secondly, players have to make certain benchmarks to receive full credit for their PCS score. This is to prevent players who won a plethora of titles as role players from scoring disproportionately high compared to their skill levels. A player has to make three All-Star teams, two All-NBA Third Teams, or one All-NBA First or Second team to earn full credit. If not, they score one-half credit for hitting none of those benchmarks, two-thirds credit for one All-Star team, or three-fourths credit for two All-Star teams or one All-NBA third team.

Finally, and this is perhaps the most important thing to remember when looking at PCS, this is not designed to say certain players were better than other players. John Havlicek has a higher score than Elgin Baylor. This is not to say that Havlicek was a better player than Baylor. He might have been, and that’s open to debate, depending on if you’re looking at skill level or in-their-prime-ness or some other comparison. But PCS does say that Havlicek had a better and more accomplished career than Baylor. I hope that distinction is clear to people, but let me know if that needs clarification yet.

Also, this is not intended to be a strict ranking of players. Hakeem Olajuwon, Oscar Robertson, and Kevin Garnett basically have the 13th to 15th best careers in some order. Olajuwon is five points higher than Robertson. Their career achievements are basically interchangeable at that point. That five point difference is almost negligible when dealing with PCS numbers at almost 500. The ten points separating Bob Lanier (157) from Vince Carter (147) is much more significant. I should write a post devoted only to this concept at some point, but just think of the difference between players becoming exponentially more important as the score goes down. If you want to think of it as a ranking, just realize it’s the slots should be considered a little fluid when the numbers are close. The primary purpose is not to rank players, but to see who would and should be in the platonic ideal of a Professional Basketball Hall of Fame.

I hope this is something that will be enjoyable to everyone. Try not to get too worked up about it if some players rate higher or lower than you’d imagine. It’s strictly what the numbers objectively say without any subjective input on my part. This stat is a tool like any other to help with debates and conversations. But please let me know if you have any questions, comments, or feedback for me regarding PCS or the site in general.

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2 thoughts on “Player Career Score: A Full Explanation

  1. Thank you for explaining this even more, and seems to make more sense now evaluating players’ careers overall and not necessarily who’s better, but I think they go hand-in-hand. However, I see additional problems with it, regardless if the conclusions are fairly close to reality, though it’s quite interesting.

    1. I’m not sure how to incorporate height as a measure, but this is a great example how it’s impossible to rate players like this and think it’s reality or even close to reality. There’s so many other variables. Size(height) matters a lot in most sports, especially basketball. I don’t think Iverson would ever be as good as Kobe or Jordan if he was 6 inches taller, but if he was, he’d be a lot better of player. His quickness would decrease some, but his improvement most other areas would improve a lot more. Just think if KD was only 6-6 instead 7-0, how much worse he would be. The examples are endless. Sure, tiny players can be good players, but the taller you are generally the better you are.

    2. I thought 3rd team all-nba selections were counted, as you mentioned in another post.(Oh, I see it mentioned later, but not initially). I don’t agree 2nd team all-nba should count the same as 1st team though.

    3. Are defensive team selections not counted, doesn’t seem to be? I understand that you need to be consistent with all the eras, and this presents a big challenge, but this still needs to count. More on this in #4.

    4. While there were only 2 rounds in the playoffs early on, we just can’t eliminate the 1st/2nd rounds that have been going on for a long time now. Take RW for example, who’s the best player in the nba this season. He’ll be lucky to make the 2nd round, and he won’t make the WCF. Which presents other issues with this, as we have great players on weak teams relative to the top teams anyway, and this happens every year. RW will be missing out a lot.

    5. Any use of win shares isn’t a good idea really, which I know we disagree on. But, you’re relying on this way too much. The problem is how to distinguish each player then if you don’t use a stat like WS. You really can’t. But if you look at each season, you can probably see huge problems with WS. Even if it’s fairly accurate, you’re incorporating another advanced stat not just for one of your criteria but several of your criteria into your own advanced stat.

    6. I don’t understand the credits fully. Why would you give 1/2 credit for not hitting a benchmark? However, if you need 3 AS to earn a full credit, 1 to earn 2/3(4/6) credit, then why is 2 AS only 3/4 credit? Shouldn’t it be 5/6 credit to stay accurate?

    7. Putting the ABA at 40% is way too low. This might affect only Dr. J as far as elite players go, but it’ll affect fringe HOFers a lot. And Dr. J was at his best in the ABA, too. Yes, his stats were inflated some, but you could be saying the same thing about his whole era compared today or different eras. The league changes. Offense is picking up quite a bit lately. Defense was huge in the early 2000s.

    It’s interesting you use Swen Nater as your example. After looking over his career, he seemed better in the nba. He made AS team in 74 and 75 and all-aba 2nd teams both years. He goes from averaging 15 and 16 on .542 shooting in 1975 to averaging 10 and 10 on . 492 shooting in 1976(his last season in the ABA). He then averages 13 and 12 on .528 shooting in 1977 in the nba. 3 seasons after that he leads in the nba in rebounding, which he did once in the ABA, too. If anything, he was better in the nba. Sure, more players to compete against so harder to get awards, but no way knowing how close he was to 2nd team though. I bet he was very close at least once. And while Erving’s stats decreased some in the nba, Gervin’s increased a lot. Sure, Gervin matured more as a player, but that much? He made 0 1st-team all-aba, but 5 1st team all-nba.

    8. PCS is an objective stat only in the sense that every player is rated with the same formula. However, all the components that make it up are subjective(either someone voting or the person who made up WS), just like any other advanced stat that specifically rates players. All of your criteria are subjective, though not by you. However, the way you weight each criteria you deem important is subjective according to you. I understand your disclaimer what you’re saying, but I think you’re getting too caught up in it. This is very subjective. Just take the 40% ABA part for example. Even if we say you’re right about that for argument’s sake, there’s nothing objective about that.

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    1. I do see now that 2nd team all-nba counts less than 1st team all-nba according to your formula. I was reading the 4th-to-last paragraph about credits where it says 1st or 2nd team all-nba receives full credit, that’s a bit confusing.

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