The pointzz argument is stupid as well because it is so utterly one-sided.
37 vs. 15
27 vs. 17
39 vs. 8
35 vs. 13
36 vs. 7
20 vs. 10
31 vs. 11
15 vs. 15
22 vs. 17
42 vs. 21
22 vs. 8
26 v. 16
27 vs. 10
22 vs. 7
30 vs. 30
28 vs. 7
39 v. 9

Oh, btw, Kobe shot .472 in those games. Manu shot .449.

Case closed.

There are good arguments for Kobe. I’d even go as far as saying I could potentially be swayed. But we’re not going to get anywhere if the response to Manu’s starter numbers not suffering is something about the nuances of horse racing.

But then you would have to also acknowledge that Manu, while he faced weaker opposition coming off the bench, also played in lineups with overall worse players, as his minutes were often staggered early in games with Duncan and Parker. He was playing against worst players, but also with worst teammates, and yet his production remained excellent in both scenarios.
I would also argue the actual difference is in fact minimal because of two factors: Manu always closed games for the Spurs as a de facto starter, and the years he came into the NBA was a time when superstars played major minutes. Kobe himself averaged 40+ mpg many times in his career, just like Iverson, Pierce, McGrady, Carter, pretty much all of the elite guards / wings Manu faced. Even a guy like Raja Bell averaged around 37-38 minutes in those years. Considering all of those guys played crunch time minutes and Manu did too, which brings a lot of overlap, the actual window where he was facing weaker players is probably around 4-7 minutes at the most. I agree it could be significant but all things considered, it’s far from the most important thing (as also, opposing superstar guards would have a 5-6 minute window at the start of games where they wouldn’t have to defend Manu).

The Kobe hype train was so huge by his supporters that it created a backlash where now a lot of people I think underrate him.

I agree with the hype train aspect of it, but I also think the advanced stats revolution played a big part in the negative perceptions. People started looking at the results of their favorite boxscore model and because he didn’t score as well as perceptions, it created backlash.

IMO, most boxscore models underrate him because they don’t measure some of the nuances of scoring properly. Most of them set some “break even point” and then it becomes more about your efficiency above the break even point than other factors. I don’t think that’s right. There is no fixed break even point. It varies depending on the conditions. I think efficiency, usage, who you play with, how you are defended, the system you are in etc.. all matter.

It’s messy, but I would guess the reality is that he was better than the boxscore models but not as good as the hype train.

Manu actually played over 12,000 minutes as a starter in his regular season and playoff career. His per 36 numbers and efficiency in those games are statistically identical (actually modestly better) to his numbers coming off the bench. Evidence beckons.

Interesting.

I’m making the case there are lots of things that impact boxscore stats “marginally”. They are difficult to isolate & measure because there are offsets and complex inter-relationships.

In racing, if a horse runs wide on the turn, there’s no doubt he ran further, but there will be days when those outside paths are fastest, some horses prefer running outside, the pace matters etc…

I know for certain that ground loss matters in racing. The problem is it could be swamped by other factors for a given day/week/month, at a given racetrack, for a specific horse etc… The data will sometimes show ground loss not only doesn’t matter, but that it’s an advantage. It will obviously be wrong. We at least know the math for ground loss.

There are similar things going on in basketball. I personally think there are many advantages and disadvantages to playing with better or worse teammates that impact boxscore stats.

Typically there are marginally better players in the starting lineup. So it’s more likely you’ll be defended by a better player playing against starters than bench players. This is the case you were also making about Burke and the G league. The difference being that the G league impact is large enough to swamp other factors and stand out more often.

Unfortunately, there’s a 0% chance I can isolate everything that was going on with Manu that impacted his stats as a starter vs. bench player. But I’m not going to suspend my belief that he benefited on offense from being defended by bench players because it’s not easy to find in the data any more than I’d abandon my ground loss example. Impact is just small enough to be hidden by other factors.