Those aren't stats. That's counting.

[wigglestrue]

What would the current stats look like if you did count offensive rebounds as new possessions?

Would love an answer on this one. Maybe this is worthy of a new thread?

[Nivek]

The Celtics won 11 out of 13 titles in the era being re-evaluated, so of course they're the franchise getting the biggest shaft, the franchise with the most players being written down. While some of you are more positive about Russell, others are more negative regarding his FG%. The point is, what makes sense today as basketball philosophy shouldn't necessarily be retroactively applied. I see some effort to contextualize here and there, but there still seems to be this idea that the Celtics' offense was incompetent because it was inefficient. But if Red was effectively giving his players orders/license to score inefficiently, and it resulted one way or another in 11 out of 13 titles, then #1 - why even care about their scoring efficiency, because whatever they did, it worked and #2 - players like Cousy and Heinsohn and Russell shouldn't be judged by FG% on its face, ever, and metrics like ORTG shouldn't be used as a significant measure of Cousy's PG ability.

Look, if someone living in 1970 but with today's glossary of stats had tried to find the highest correlation between certain stats and winning, then he might have concluded that inefficient scoring was a key to winning, because that was a prime characteristic of the majority of champions up to that point. Would he have been wrong? No, because that's what his primordial Excel-ish program would have told him. But today that wouldn't make any sense. There's a problem here. I'm not versed in statistics enough to name it or point a finger at it, but I can smell it.

And yes, I'm motivated to smell it because it might dramatically affect the statistical legacy of my favorite franchise. Homerism can be a potent impetus for statistical introspection.

The more efficient team wins even in an era of general inefficiency. If you get 85 points per 100 possessions and hold your opponent to 80 pts100, by definition you're the more efficient team. Correlations from the 70s or 60s (if we had the necessary stats to calculate efficiency stats) would show similar relationships between efficiency and winning percentage. What might vary a bit are relationships between individual components of the four factors.

I haven't been reading whatever folks have been posting to knock Bill Russell's FG%, but I suspect it's wrong-headed "analysis." It's just plain wrong to judge players of one era by the norms of another. Players need to be evaluated within the context of their own time. Russell's FG% looks bad to the modern eye, but he had a higher FG% than his team's in 11 of his 13 seasons. The only two where he shot worse -- his 10th and 12th seasons (ages 31 and 33). He shot worse by .002 and .015, respectively.

When I do era translations, FG% is one of the stats I adjust. The only era translation I have handy in a spreadsheet is actually Hakeem back to Russell's time. Hakeem shot .512 from the floor for his career on teams that collectively shot .471. Translate that to 1958-59 (Russell's 3rd season) -- the Celts won the title that season shooting .395 from the floor -- and the equivalent is .430. That season, Russell shot .457.

[QuantMisleads]

Sorry, Mufasa, but group B does not exist. Those are people who are thinking they understand stats, but in reality they don't. If someone really understands stats, he will use it, if the context fits. The unfortunate thing is: Most people don't understand stats, they are using them in the wrong way. I also think that group C is rather small, really small. Most people still arguing with points, rebounds, etc.

Took the words out of my mouth. It makes absolutely no sense to understand statistics and them not use - it would be like closing your eyes while you drive a car.

I'm sorry, but this is complete and utter nonsense, the type of nonsense someone uses when fooling himself into thinking he's doing scientific analysis.

Some (very few) of us know that many people cannot separate "objective knowledge", that is, how the world REALLY is, from our personal beliefs, while some of us (like yourself) believe in positivism. So you throw your statistics around (cherry picked of course) and show it as a sort of objective analysis. I then throw you my statistics, and then you say your statistics are more valid than mine.

But I understand that statistics is meant to complement your analysis, not BECOME your analysis. IE, I make theory from the facts (some facts being W/L record and individual statistics) while you deduce (deductive analysis) facts from your theory regarding the statistics. Qualitative analysis is analysis that can be tested, and doesn't hide silly assumptions. On the other hand, quantiative analysis (the favorite tool of the positivist) cannot be tested, and certain assumptions have to be made to make the numbers say what you want them to say. Guess what that means? That your analysis is no better than mine, and in fact is worse, because you give it the veneer of scientific analysis when it is nothing of the sort. Not only that, but it's dangerous because it makes others believe the same thing. Out of this our contemporary society has created an entire set of falsehoods which unfortunately cannot be debunked but through education. You, my friend, need to become educated.

[EvanZ]

Qualitative analysis is analysis that can be tested, and doesn't hide silly assumptions. On the other hand, quantiative analysis (the favorite tool of the positivist) cannot be tested, and certain assumptions have to be made to make the numbers say what you want them to say.

This is gobbledy-gook to me. Can you explain in English words what point you are trying to make?

[Dr Positivity]

Quantitative analysis can't be tested as much as qualitative? Where is that coming from? Isn't quantitative analysis even a better fit for testing because it has specific numbers (thus giving a clear "right" or "wrong" verdict). I agree with the sentiment otherwise about qualitative analysis and theory getting my vote over statistics that can be doubted and that the more specific to an area and farther away from "the whole picture" of a player's impact, the more I treat the stat as useful. The number of variables is usually negatively correlated with how trustworthy the stat is and any stat trying to pack an entire player's impact into 1 is going to be overflowing with variables

[QuantMisleads]

What I meant by "tested" is that the assumptions made in order to make the statistics cannot be verified (particularly when we make estimations from data pre-mid 70s), and hence their validity is not only questionable but entirely invalid from a scientific point of view. Even worse, the validity of the statistics cannot all be pinned down to one player as some would like to do. For example, some like to examine the impact of a team when a specific player is playing and when he's not. There are so many things wrong with doing this, but people use it as a measure and then say "well, we can't do any better than this!". yes we can! We can see what newspapers said, what players during that time said, what actual statistics we have, etc. That's what qualitative analysis is all about. Now, there ARE some issues that people would like to bring up. One is that there is too much information out there that we have to search for, and even if we compile it you can't necessarily judge one player against another. My answer, and the answer of other scientists: TOUGH, buddy. What is inherent in qualitative analysis is that we recognize as well that our judgements are never objective. It takes a lot of back and forth to straighten things out. Many objectivists (positivists) do not like this. Not only that, the numbers and the advanced statistics give one the comfort about that they are doing an objective, scientific analysis. There is great danger in this, in that it not only fools the person himself but others that would have otherwise resorted to qualitative analysis (or certainly a mix). It gives one comfort when in reality it should give one great doubt.

[wigglestrue]

The more efficient team wins even in an era of general inefficiency. If you get 85 points per 100 possessions and hold your opponent to 80 pts100, by definition you're the more efficient team. Correlations from the 70s or 60s (if we had the necessary stats to calculate efficiency stats) would show similar relationships between efficiency and winning percentage. What might vary a bit are relationships between individual components of the four factors.

Right, and the Celtics of that era were bad (by our standards) at scoring efficiency, and so in 1970 there would have been a strong correlation between winning championships and bad scoring efficiency.

[Dr Positivity]

Quant -

I agree the +/- stats for when a player gets injured for 10 Gs being used as evidence for players' quality is wrong, for a few reasons. One is that small sample sizes in general very rarely mean anything useful. Even the Adjusted Plus Minus guys with all their information over an entire year have a big margin of error - So how are we supposed to trust like 300 minutes of play? I don't know if it means anything more than how a baseball team or player does for 10 games in a row

Secondly, I think there's something to be said for adjustments in the NBA. I think there was a Bulls forum PC forum once who said how he's very familiar with the plays and gameplanning each teams do, and that he finds it takes 2-3 weeks for a team's new lineup to be adjusted to. Many of the injury +/- stats can be effected by this IMO. New lineups can surprise teams and put off their weaknesses being exposed. Furthermore I'm a huge proponent of the concept of energy in basketball. It's pretty clear that everyone doesn't play pedal to the metal all 82 games or 48 minutes, except for maybe a rare situation like the Thibs Bulls. For everyone else there's another gear they go to in the playoffs. But what's to say that if a team loses their star for 10 Gs, that they don't engage "Playoff mode" to keep their playoff seeding alive until he comes back to take pressure off them? A good example is that in the top 100 project people were taking away some of the shine of Pau's Memphis years because of how his 45-50 W team, continued to play like a playoff team whenever he got injured for a few months. The Grizzlies of course with Battier, Miller, Bonzi, White Chocolate, simply weren't to me, the caliber of team that their "without Pau" small sample size record would indicate. What would make sense is if they managed to keep their playoff season going through a combination of a lag to catch up to their style of play + putting in their greatest efforts of the season, so their season wouldn't fall apart. But if they had played the entire season led by Battier, Miller, Bonzi, they'd likely be somewhere between 20 and 35 Ws, since nobody makes the playoffs with talent that bad

Thirdly, I think there's something to be said for just how exact the In/Out +/- stats would have to be to be useful. A margin of error of +/- 2.5 in either direction would sink the entire thing. Durant having a score of +6, with the possibility that he's as high as +8.5 or as low as +3.5, tells us literally nothing. Any conclusions based on a player's number being 0.5-2 points higher or lower than another player he is compared to, means absolutely nothing unless the margin of error is like less than 1 pt. So the question is, does of the combination of a) Natural faults with small sample sizes, b) Adjustments by teams, c) The "expend energy to make up for stars not being there" factor, add together for a margin of error that big? I think the answer to that question is "Hell to the yes". In fact a margin of error of like 3 pts in either direction is probably on the low end for expectations of how off those numbers can be

So I agree with you - people can use the methods they want but I have reason to believe there is zero value in using small sample sizes "when the player is injured" In/Out statistics, there's simply not enough information there

[Nivek]

The more efficient team wins even in an era of general inefficiency. If you get 85 points per 100 possessions and hold your opponent to 80 pts100, by definition you're the more efficient team. Correlations from the 70s or 60s (if we had the necessary stats to calculate efficiency stats) would show similar relationships between efficiency and winning percentage. What might vary a bit are relationships between individual components of the four factors.

Right, and the Celtics of that era were bad (by our standards) at scoring efficiency, and so in 1970 there would have been a strong correlation between winning championships and bad scoring efficiency.

What matters is a team's efficiency relative to its opponents. What looks inefficient to our modern eye may have been efficient for that era. The question is where Boston's efficiency stood as compared to the teams they faced. I don't know the answer to that, but I'm curious to look at the issue tomorrow if I have time.

[EvanZ]

Of course teams were less efficient in the 50's, 60's, and 70's. Without a 3-pt shot to space the floor, teams could just crowd the interior and middle of the floor. You think long 2-pt jumpers look bad now? Back then, you'd give up the 23-ft 2-pt shot all day.

Why people would even bother to compare between eras like that is beyond me. It's a fool's errand. The best you can say is the guys who were considered the best then would probably be among the best now and vice-versa. Human beings haven't evolved in 40 years. It's the same people.

[Doctor MJ]

Qualitative analysis is analysis that can be tested, and doesn't hide silly assumptions. On the other hand, quantiative analysis (the favorite tool of the positivist) cannot be tested, and certain assumptions have to be made to make the numbers say what you want them to say.

I find your bias here to be rather astonishing. The notion that qualitative analysis is somehow inherently more testable than quantitative analysis is just absurd. Right from the start, you aren't making any kind of sense.

More generally, your perspective is just bizarre to me because you insist on a fundamental distinction between yourself and those who come to different conclusions that simply doesn't exist.

As one of the group who favors Russell over Wilt (which is the group you despise so much), I wouldn't have come to the conclusions I did if it weren't for:

-People of the time explaining how they believed Russell was a more impactful player than Wilt.
-Various accolades singling out Russell above Wilt.
-Decisive team results in favor of Russell and
-Various negative observations and disappointments people had about Wilt.

None of these things is a "quant" thing. In fact, they put me very much at odds with many statisticians who could reasonably be called myopic positivists, and yet you don't see the distinction? I guess they aren't the only ones with myopia. I'm using qualitative and quantitative just like you, the only difference I can really see is that I've done it in a deeper, and yes more scientific, way.

You talk of testability. I wonder if you even understand what that means. What does it mean, for example, to have a historical basketball hypothesis? What of these "experiments" have you performed?

[Doctor MJ]

What is inherent in qualitative analysis is that we recognize as well that our judgements are never objective. It takes a lot of back and forth to straighten things out. Many objectivists (positivists) do not like this.

I've had my opinions about basketball changed so many times in so many different ways in the past few years, and yet you accuse people like me of "not liking' to have "back and forth". Meanwhile, you've basically stuck to the exact same mantra the whole time I've known you. It's remarkable that you cannot see how your actions would appear to others. You claim rational superiority by pointing out the rigidity of others, while speaking with greater rigidity than almost anyone else in the room at all times.

One would expect someone tossing around such lofty philosophical terms would know himself better.

[QuantMisleads]

What is inherent in qualitative analysis is that we recognize as well that our judgements are never objective. It takes a lot of back and forth to straighten things out. Many objectivists (positivists) do not like this.

I've had my opinions about basketball changed so many times in so many different ways in the past few years, and yet you accuse people like me of "not liking' to have "back and forth". Meanwhile, you've basically stuck to the exact same mantra the whole time I've known you. It's remarkable that you cannot see how your actions would appear to others. You claim rational superiority by pointing out the rigidity of others, while speaking with greater rigidity than almost anyone else in the room at all times.

One would expect someone tossing around such lofty philosophical terms would know himself better.

Before saying anything else, I should note that I do not despise people who like Russell over Wilt, in fact I think Russell was also a once in a lifetime player as Wilt was, as they both had an unparalleled athleticism (and height to match), and I think Russell's skillsets made it easier for him to understand what he had to do which is why I think his teams were more successful. I do despise, however, the intellectual dishonesty among some posters and their appeal towards what they think is scientific analysis.

You raise some important points that I didn't explicitly raise perhaps in my last few posts. There are two issues at play when I see someone resort to DRTG/ORTG or other advanced statistical numbers and then pin most of it on one player:
1) Preconceived biases
2) Using themes to support the quantitative.

So as I mentioned before, we try to find the numbers to support our hypothesis...big surprise there. I find it both hilarious and pathetic that bastillon shows up (and does so faithfully) when he gets to push his view of a certain player using whatever methods at his disposal and has voting power (INCLUDING spreading the lie that Russell was injured in 1967). Of course there are other numbers, other facts that don't support that, but lets put that at the side for the moment.
The second matter is related to the first. In this case, we use the themes that aren't numbers to support the cherrypicked numbers that we have. Yet these themes are so overused that it's almost as if we're talking about robots rather than a complex 5 on 5 game. For example, we want to rationalize why Wilt only won 2 championships. So the theme is created to explain away EVERY YEAR HE PLAYED AND LOST, and we use whatever statistical tool we can find to support it! Every other poster reads this and assumes their analysis is impeccable, and you basically get everyone thinking the same way. You call this analysis?? So when all the newspapers, Cousy, and Russell said Wilt played like a monster in that 7 game series in 1962 (where Wilt loses by 2 points due to someone else not covering Sam Jones) where nobody gave the Warriors a chance in hell in winning, how do we treat Wilt? "Oh...he only scored 14 points in that final game! His scoring average was WAAAAY below average in the playoffs, he choked"! This is literally the story told. Was it true in other years? yes, perhaps it was, but not this year.

Look, there is ALWAYS room for interpretation. For example, I think Wilt's 1962 season isn't given the credit it deserves, but I respect other opinions on the matter. But when we use advanced statistics (that, no, whose assumptions cannot be tested in the way I described earlier) to give it the allure of scientific analysis, we should know better. But judging by the fact that many posters resort to themes to support their analysis (which, by the way, is the only thing I would accuse you of) and don't do enough newspaper or comments from their peers (or even interviews that were done with older players from that generation).

There is always going to be bias in our interpretations, but we're not doing the best of trying to root it out. The problem so much isn't with you guys, but that there are few educated dissenting voices. The problem with me is that I get easily angered at intellectual dishonesty so I can't seem to hang around for longer than a month, nor are my comments helpful in the right direction either because I'm too lazy to try to find the newspaper article or interview to rebut someone.

And for me, finding someone's dishonesty is particularly easy, even when it comes to people like ElGee, who has used 1965 and 1969(much like Bastillon, I might note) (without any historical analysis) to "prove" that Chamberlain had no impact when he switched teams. These guys are dishonest because I'm sure they have read my posts on what happened those particular years and why they're not applicable, but unfortunately for Chamberlain those were also the only seasons where he switched teams and/or missed games. This creates a theme of course: Wilt didn't have an impact, while Russell did. Does team record show this to be true? does where they made it in the playoff show this to be true? Nope. Though others will say yes, that this huge impact was evident in Russell's 2 point or 1 point victory over Wilt in game 7.

[Nivek]

Quant: You're not making much sense to me. Your central point seems to be that you think some people are doing bad analysis. Okay, you're correct. There's lots of bad analysis. It's generally pretty easy to spot bad analysis and to identify why. When someone cherrypicks data to support a point, there's going to be data to counter with. Or, at very least, their argument can be deconstructed.

I don't see what this has to do with advanced stats. Your point is really about poor argumentation.

[QuantMisleads]

Quant: You're not making much sense to me. Your central point seems to be that you think some people are doing bad analysis. Okay, you're correct. There's lots of bad analysis. It's generally pretty easy to spot bad analysis and to identify why. When someone cherrypicks data to support a point, there's going to be data to counter with. Or, at very least, their argument can be deconstructed.

I don't see what this has to do with advanced stats. Your point is really about poor argumentation.

No, it's not easy to spot bad analysis. Go to the Player Comparison forum and how they're ranking the highest peaks. What essentially happens is someone like ElGee comes along and posts a bunch of advanced statistics (one that of course favors his argument), ignores the statistics he thinks conceals what is hidden, tells everyone (based on his ranking) who the best player are, and lo and behold: everyone else starts voting the same way he does. Never mind that his analysis may suffer from all of the things I listed above (which, they surely do).

And as to someone countering data with data, it just doesn't work like that. Real data from the games, and even slightly more advanced ones like TS%, do not counter false and misleading data like DRTG and ORTG. Furthermore, as I stated above, people build themes around their data to create their story. It's clever of course, but incorrect and sometimes (depending on the poster) entirely dishonest. Look, people can do whatever they want, and they can fool whoever they want (you can fool with numbers, but not with qualitative data) but if they think their silly rankings actually mean anything, they are entirely mistaken.

So not only is the use of advanced statistics abused, but it also creates the false appearance of scientific vigor and creates what should be a good discussion into silly and nearly worthless analysis.

The only person I've seen to use a good mix of both is Tsherkin (and for good qualitative analysis posters, see Dipper13 or Tharegul8r(but don't look at what he bolds)). The rest of you can carry on your mostly pseudoscientific endeavor until you wisen up.

[EvanZ]

Look, people can do whatever they want, and they can fool whoever they want (you can fool with numbers, but not with qualitative data) but if they think their silly rankings actually mean anything, they are entirely mistaken.

Sorry, but that's one of the silliest statements I've read in a long time. I'll have to remember that. You can fool people with numbers, but not with qualitative data. Ok.

[QuantMisleads]

Look, people can do whatever they want, and they can fool whoever they want (you can fool with numbers, but not with qualitative data) but if they think their silly rankings actually mean anything, they are entirely mistaken.

Sorry, but that's one of the silliest statements I've read in a long time. I'll have to remember that. You can fool people with numbers, but not with qualitative data. Ok.

Seems like someone is taking offense because I'm hitting at the way he sees the world?

"In the early 1900s, some researchers rejected positivism, the theoretical idea that there is an objective world about which we can gather data and "verify" this data through empiricism. " --Wikipedia

Qualitative data consists of historical analysis, interviews, grounded theory (creating a theory from your research rather than through deductive means), ethnographic research, etc. All of this is lain bare in front of those interested. Now, he could make all of it up and "fool" them that way, but otherwise I can't think of another instance where it's possible.

so right back in your face bud

[EvanZ]

I'd probably be offended, but your arguments are virtually incoherent.

[Nivek]

Virtually?

[QuantMisleads]

Virtually?

Here's the deal. If you attempt to lead your argument with quantitative data only (and worse, only advanced statistics), then dial yourself back and bring in some newspaper accounts, what other players or coaches or others said at the time or later, etc. And don't complain that that's too hard.