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Thinking Basketball

#50: Top scorers in NBA history revisited

Thinking Basketball

Thinking Basketball

Sports

4.9897 Ratings

🗓️ 24 March 2020

⏱️ 88 minutes

🧾️ Download transcript

Summary

I reboot the inaugural episode with new data and a deeper look at the all-time best scorers in NBA history. What makes someone a great scorer? What misconceptions are common about volume vs. efficiency, on-ball vs. off-ball scoring and finishing vs. dependency. Support at www.patreon.com/thinkingbasketball --- Support this podcast: https://podcasters.spotify.com/pod/show/thinking-basketball/support

Transcript

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0:00.0

Thinking Basketball Podcast, my name is Ben.

0:07.0

Thank you Basketball Podcast, my name is Ben.

0:09.8

Welcome back to another episode and today it is the 50th episode of the Thinking Basketball

0:17.6

podcast and for that I wanted to do something fun, something special.

0:22.2

It seemed given the Corona break that we are on, the way things are

0:27.9

going on in the outside world these days where there's no sports, it would be fitting

0:31.9

to go back to the very first episode that launched this

0:35.0

podcast in 2018 around the top scores ever.

0:42.0

And that podcast for those who listen to it and thank you. By the way if you've

0:46.0

been listening since the beginning 50 episodes in really really you all make

0:52.0

this possible the growth of this possible so the idea was in

0:57.1

that episode I had played with and debuted this idea of a scoring value metric that attempted to balance the value of

1:08.6

volume and efficiency. We often think of scores as well as this guy like a 25 point per game score is he a 32 point

1:15.3

per game score and then within that context how efficiently is he scoring his

1:20.8

points and there's there's of course way more to it that I've discussed before that we've all talked about and we'll get to in this episode.

1:28.0

But in that case it was really about trying to look at this question of scoring and scoring numbers through a different

1:37.0

lens and so I constructed a list heavily based on that idea and that metric. Today is going to be a revisiting of that and

1:46.2

something a little more nuanced, something a little more advanced. I'm going to

1:49.7

attempt to use a new version of that, to use some new concepts, and really blend the data with the context that the players played in, to think about if we had to only choose guys based on their

2:05.9

scoring prowess who would we want what would that list look like in other words

2:10.3

who created the most value, or even the most, you know, championship equity

2:16.4

in the playoffs or in a playoff type setting by virtue of their scoring? That's the question we're asking today.

...

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