I think you're talking past each other. I think AV is saying that an MRI is the data, which makes it like SportsVU. But he would not consider the data to be the analytics. You consider the data to be part of the analytics. He considers this to be important because there is far more subjectivity in interpreting SportsVU data. The result of the MRI is essentially the end of the process, while the SportsVU data is the beginning.
I don't think we're talking past each other. I think I understand his point; I just disagree with it because I think he's asserting a disanalogy by highlighting different aspects of the two technologies to try to show they are different, when in reality the two technologies both utilize the same aspects.
In an MRI I think there's a conflation of the data with the rendering/analysis. The data in an MRI (analogous to the X-Y/X-Y-Z measurements in SportsVU) is the measurements of radio frequency emissions from excited hydrogen atoms after a magnetic field is applied at a specific resonant frequency. It's just that -- raw data of RF emissions. We then process that raw data into measures from which we can derive meaning and conclusions. In an MRI the most frequent application is to render an image, but that's not unique to the MRI process -- we can also image basketball plays from SportsVU data (as I have shown), and in fact, it's also possible to provide other interpretations of MRI data that don't involve imaging.
The processed data are then presented to a subject matter expert who is then able to utilize those results to derive that meaning and formulate conclusions.
And to correct one thing you said of me -- I don't consider the data to be part of the analytics. Analytics are a collection of processes that can be applied to the data, but that doesn't make the data a part of the analytics -- no more than, say, a movie is a part of a review of that movie.
First, I think you have to stick with only the most common use of an MRI -- rendering an image. If you start going into abstruse uses, you lose the value of making an analogy.
Anyway, I see what you're trying to say. But you are talking more about the process of how raw data is turned into the information that is put in front of the end user.
But I don't think the behind-the-scenes processes matter to many people. I think the main point is the information that is put in front of the end user.
But that's me; now I am curious if the good doctor will respond in the way I think he will.
And to correct one thing you said of me -- I don't consider the data to be part of the analytics. Analytics are a collection of processes that can be applied to the data, but that doesn't make the data a part of the analytics -- no more than, say, a movie is a part of a review of that movie.
We all approach analogies like this in light of our own backgrounds and aptitudes. Naturally, your IT background colors your thought processes, just as my legal background colors mine. Your analogy strikes me as useful, but for reasons that are quite different from yours.
Let me explain why I found your analogy to be enlightening.
Traditional medicine involves observation and testing. What are the symptons? "Where does it hurt?" Blood tests. Urine tests. If I see X, Y, and Z, the logical diagnosis is A, and the correct treatment is B. This is much like traditional scouting, box score stats, coaching wisdom, and the like. Basically, it's analogous to "the eyeball test." It is useful stuff, and it has worked pretty well for a long time.
The MRI takes things to a different level. It allows us to see things that we could never see before, by using data that we couldn't gather and arrange before. It allows doctors to make better diagnoses and to prescribe better treatments. It doesn't change medicine, per se, but it allows doctors to practice medicine more effectively. Likewise, analytics allows us to see things that we could not see before, by using data that we couldn't gather and arrange before. It doesn't change basketball, per se, but it allows coaches, GMs, and players to do their jobs more effectively.
First, I think you have to stick with only the most common use of an MRI -- rendering an image. If you start going into abstruse uses, you lose the value of making an analogy.
Actually I was, but I think it's pretty common to just look at the end product of an MRI and ignore the rest of the stack -- the data gathering, processing and visualization to render the final image that one thinks of with an MRI. It's also easy to lose sight of what you're actually looking at -- a visualization built upon source data that is distinctly NOT image data. It's the rendering of this non-image data AS an image, and we lose sight of that fact because: 1) For most intents and purposes the distinction doesn't matter; and 2) It's an easy mental shortcut to think of it as though it were just a picture.
By pointing out that an MRI doesn't even need to result in a visualization I was just illustrating that distinction. The original intent of the analogy still assumed a visualization at the culmination of the process.
Quote:
Anyway, I see what you're trying to say. But you are talking more about the process of how raw data is turned into the information that is put in front of the end user.
I'm talking about the entire end-to-end process, which includes the SME interpreting the presentation.
Quote:
But I don't think the behind-the-scenes processes matter to many people. I think the main point is the information that is put in front of the end user.
Of course. Which supports my point above that it's easy to lose sight of what's happening in an MRI because it's so easy to wrap your head around it being so analogous to a picture. But that doesn't change what it is.
Likewise, when we talk about basketball analytics, it's easy for a lot of people to mentally associate it with oversimplified and nearly useless comparative stats (like showing that Kobe is the greatest player of all time), which is not what analytics is used for in production and in fact bears little to no resemblance to what the process and the result actually is.
Quote:
But that's me; now I am curious if the good doctor will respond in the way I think he will.
Since my wife has the same background and was with me on this one, I can assume...but it's still just an assumption.
BTW, the analogy grows even stronger if we start talking about Functional MRI. With these we're tracking blood flow to infer brain activity in specific areas. We aren't measuring the brain activity itself. Again, much less like an image than an algorithm applied to raw non-image data and interpreting it in the form of a viz.
I try not to venture into topics that I don't know much about, but the medical analogy I'd make would be that analytics are similar to laboratory work. They identify what's working well and what isn't on a more specific level than the eye test does in the form of numbers, and then it's up to an expert to diagnose the problems and prescribe the proper remedies.
I'm going to ignore the personal attacks on me and answer this question because it is actually germain to the topic. A better analogy would be something like the New England Journal of Medicine Rational Clinical Exam Series where they examine a specific clinical disease entity and use meta-analysis in an attempt to determine which physical exam findings give the best relative risk ratios, sensitivities and specificities for diagnosing these disease processes.
It's a good series but as with all meta-analysis, there are problems inherent due to multiple confounding factors in both heterogenicity, homogeneity as well as differences in trial design and weighting abnormalities.
Ok, carry on gentlemen. _________________ “It took many years of vomiting up all the filth I’d been taught about myself, and half-believed, before I was able to walk on the earth as though I had a right to be here.”
― James Baldwin, Collected Essays
B. My dismissal of analytics has far more to do with my experience as an athlete and a physician than it does with Kobe. You have a right to an opinion about my motivation, and you have a right to be wrong, no matter how strongly you word it.
I think your dismissal of analytics would be accepted in here if you seemed to have some knowledge of analytics and why/how it is used.
It's ironic that KBCB constantly feels a need to tell people here he's a physician as if that makes him an analytics expert. It's almost a cliche that physicians, as a group, are known for their resistance/fear/lack of knowledge regarding technology and analytics.
That last point is not directed at him personally, but it's just funny to me. A sales manager or marketing director or logistics coordinator has far better cred to talk about analytics than your average doctor.
I don't know what era you live in but that's a blatantly incorrect comment. Not to mention that KBCB's former medical school is incredibly focused on research and technology. In fact, they focus on those applicants who have extensive backgrounds and have shown initiative-taking in research methodologies (including analytics). It's likely how I even scored an interview despite several pitfalls in my applications.
And the fact that I was co-author on two published papers as a medical student, presented at the most prestigious conference in my former research field in the World and graduated in the top 10 in my class at this research focused medical school is just a bizarre coincidence as well.
But as I said, it's all in the game yo. _________________ “It took many years of vomiting up all the filth I’d been taught about myself, and half-believed, before I was able to walk on the earth as though I had a right to be here.”
― James Baldwin, Collected Essays
I love how so many here who skewer me for daring to offer an opinion on advanced basketball statometrics because they think I don't have a clue are debating medicine with such conviction. _________________ “It took many years of vomiting up all the filth I’d been taught about myself, and half-believed, before I was able to walk on the earth as though I had a right to be here.”
― James Baldwin, Collected Essays
I love how so many here who skewer me for daring to offer an opinion on advanced basketball statometrics because they think I don't have a clue are debating medicine with such conviction.
Joined: 23 Jun 2005 Posts: 8488 Location: The (real) short corner
Posted: Fri Jul 31, 2015 3:20 am Post subject:
KobeBryantCliffordBrown wrote:
I love how so many here who skewer me for daring to offer an opinion on advanced basketball statometrics because they think I don't have a clue are debating medicine with such conviction.
Well, debating and dismissing are two different things.
I'd say if someone was dismissive of established methods of doing research in medicine based on their experience with basketball, then they'd be fair game.
I love how so many here who skewer me for daring to offer an opinion on advanced basketball statometrics because they think I don't have a clue are debating medicine with such conviction.
I think it's funny that you think we are debating medicine. We aren't. We're discussing analogies in the medical field because some of the others are trying to find a way to get through to you. They don't realize that you aren't going to budge because that's just who you are.
We all approach analogies like this in light of our own backgrounds and aptitudes. Naturally, your IT background colors your thought processes, just as my legal background colors mine. Your analogy strikes me as useful, but for reasons that are quite different from yours.
Sorry -- didn't get to this one last night.
Well...of course my background colors my perceptions and thought processes, but I don't think I'm approaching this any differently than you. For example, your explanation of why you found my analogy enlightening was pretty much the same way I see it.
Sorry, but your response kinda reminded me of the kind of people who think taking a science-y approach to nature means I fail to recognize its intrinsic beauty (yes, I've talked to those people). Quite the opposite, I can appreciate the immediate beauty of a sunset AND I understand the underlying processes of astronomy and physics that produce it (and I appreciate the beauty and understanding of that in addition).
Same thing here -- your explanation makes it sound like you think I'm missing the end-user usefulness of the tool.
Quote:
Let me explain why I found your analogy to be enlightening.
Traditional medicine involves observation and testing. What are the symptons? "Where does it hurt?" Blood tests. Urine tests. If I see X, Y, and Z, the logical diagnosis is A, and the correct treatment is B. This is much like traditional scouting, box score stats, coaching wisdom, and the like. Basically, it's analogous to "the eyeball test." It is useful stuff, and it has worked pretty well for a long time.
The MRI takes things to a different level. It allows us to see things that we could never see before, by using data that we couldn't gather and arrange before. It allows doctors to make better diagnoses and to prescribe better treatments. It doesn't change medicine, per se, but it allows doctors to practice medicine more effectively. Likewise, analytics allows us to see things that we could not see before, by using data that we couldn't gather and arrange before. It doesn't change basketball, per se, but it allows coaches, GMs, and players to do their jobs more effectively.
Yes, and I would have given much the same explanation. A patient presents to KBCB with certain symptoms, and he will use the patient's history, symptoms, and other factors to form a differential diagnosis. But just because symptom X is indicative of disease Y, it doesn't mean he's found Y -- there can be MANY diseases that are indicated by symptom X*. So he orders further tests to either confirm or rule out his differential diagnosis.
[*Also guided by heuristics, such as a symptom being more likely to be the result of an atypical presentation of a common disease than a typical presentation of a rare disease. Or as Sherlock Holmes put it, when you hear hoofsteps on the streets of London, you think horse, not zebra.]
An MRI is one such test that may be indicated, and as I said above, it's a sophisticated process of data gathering, algorithmic analysis, and visualization that are then presented to him (or the radiologist) as the SME to interpret, and then it's also up to him as the SME to determine what to do about it.
This really is not that much different from basketball analytics. We have a symptom -- say, we're loosing too many close games in the last minute. We make a differential diagnosis -- say, maybe we're launching the semipenultimate shot at the wrong time in two-for-one situations. We order a test -- let's say we analyze the raw SportsVU data for thousands of games looking for two-for-one situations, looking at the time the semipenultimate shot was launched. We then group this data by the specific time of this shot, and calculate the average net points that result. For fun we might even visualize this -- forming a graph which as it turns out is bell-shaped with a mean of around 30.5 seconds. (Or more realistically, we probably already have this available so we know what an optimal two-for-one SHOULD look like, just as KBCB already knows what a clear MRI should look like). So now we apply this knowledge to our patient (the basketball team) looking specifically at what they are doing in this situation with the best diagnostic tool at our disposal (the SportsVU data). The information goes to a SME, who notices that our team is launching its semipenultimate shots roughly three seconds later than optimal, with an average loss of 1.2 net points, which for this team would translate to three additional losses over the course of a season.
So now we order an intervention, which in the case of the basketball team would be working with the coaching staff to understand the situation and show how it can be improved. The coaching staff devises drills to simulate these situations and get the team used to launching these shots a little sooner. Over time, this becomes second nature, repeated analytics of post-intervention play shows that the team is now launching its semipenultimate shot at an average of 30.1 seconds, and the team has seen a net gain of 1.1 points in these situations. Finally, the analytics guy gets a raise.
I love how so many here who skewer me for daring to offer an opinion on advanced basketball statometrics because they think I don't have a clue are debating medicine with such conviction.
Well, debating and dismissing are two different things.
I'd say if someone was dismissive of established methods of doing research in medicine based on their experience with basketball, then they'd be fair game.
Same thing here -- your explanation makes it sound like you think I'm missing the end-user usefulness of the tool.
Actually, I'm responding to your comment that the data are not part of the analytics. That's the difference between our interpretations of the analogy.
But there is no reason to belabor this. KBCB doesn't care. He got credit on a couple papers as a medical student, and he knows some statistical jargon. Consider your arguments deflected.
First, I think you have to stick with only the most common use of an MRI -- rendering an image. If you start going into abstruse uses, you lose the value of making an analogy.
Actually I was, but I think it's pretty common to just look at the end product of an MRI and ignore the rest of the stack -- the data gathering, processing and visualization to render the final image that one thinks of with an MRI. It's also easy to lose sight of what you're actually looking at -- a visualization built upon source data that is distinctly NOT image data. It's the rendering of this non-image data AS an image, and we lose sight of that fact because: 1) For most intents and purposes the distinction doesn't matter; and 2) It's an easy mental shortcut to think of it as though it were just a picture.
By pointing out that an MRI doesn't even need to result in a visualization I was just illustrating that distinction. The original intent of the analogy still assumed a visualization at the culmination of the process.
Quote:
Anyway, I see what you're trying to say. But you are talking more about the process of how raw data is turned into the information that is put in front of the end user.
I'm talking about the entire end-to-end process, which includes the SME interpreting the presentation.
Quote:
But I don't think the behind-the-scenes processes matter to many people. I think the main point is the information that is put in front of the end user.
Of course. Which supports my point above that it's easy to lose sight of what's happening in an MRI because it's so easy to wrap your head around it being so analogous to a picture. But that doesn't change what it is.
Likewise, when we talk about basketball analytics, it's easy for a lot of people to mentally associate it with oversimplified and nearly useless comparative stats (like showing that Kobe is the greatest player of all time), which is not what analytics is used for in production and in fact bears little to no resemblance to what the process and the result actually is.
Quote:
But that's me; now I am curious if the good doctor will respond in the way I think he will.
Since my wife has the same background and was with me on this one, I can assume...but it's still just an assumption.
BTW, the analogy grows even stronger if we start talking about Functional MRI. With these we're tracking blood flow to infer brain activity in specific areas. We aren't measuring the brain activity itself. Again, much less like an image than an algorithm applied to raw non-image data and interpreting it in the form of a viz.
Quote:
Cheers.
I'll drink to that.
Hi, Larry. I am sorry now that I stomped down on your metaphor. It's actually a clever way to capture the notion of understanding stuff you can't see with your own eyes.
I got too clever with myself, and was reading your challenge to the god doctor too literally, so I raced away and imagined how he would pick the analogy apart. As a result, I took out some of the fun. I was being too clever by half.
If you aren't of a mind to pick the analogy apart based on nitpicky details, the way MRIs are created is a really good imagery of how analytics assembles disparate pieces of data into a clear picture.
So again I apologize. I jumped the gun here. The kind of stuff I brought up should have been much later in the discussion after the metaphor had sunk it.
I try not to venture into topics that I don't know much about, but the medical analogy I'd make would be that analytics are similar to laboratory work. They identify what's working well and what isn't on a more specific level than the eye test does in the form of numbers, and then it's up to an expert to diagnose the problems and prescribe the proper remedies.
I'm going to ignore the personal attacks on me and answer this question because it is actually germain to the topic. A better analogy would be something like the New England Journal of Medicine Rational Clinical Exam Series where they examine a specific clinical disease entity and use meta-analysis in an attempt to determine which physical exam findings give the best relative risk ratios, sensitivities and specificities for diagnosing these disease processes.
It's a good series but as with all meta-analysis, there are problems inherent due to multiple confounding factors in both heterogenicity, homogeneity as well as differences in trial design and weighting abnormalities.
Ok, carry on gentlemen.
Any clinical study is going to face confounding factors that need to be considered. This is true since the beginning of science. But I'd imagine that the NEJM series that you're referring to still carries a significant amount of value, and similar studies have allowed medicine to operate far more efficiently over the last 100 years.
The notion that there are confounding factors that need to be considered when looking at basketball analytics is something that no one would argue with you over, and a far cry from the point that you've been making all along. I'm not even sure what your point is now, considering how much it's shifted around.
So again I apologize. I jumped the gun here. The kind of stuff I brought up should have been much later in the discussion after the metaphor had sunk it.
I try not to venture into topics that I don't know much about, but the medical analogy I'd make would be that analytics are similar to laboratory work. They identify what's working well and what isn't on a more specific level than the eye test does in the form of numbers, and then it's up to an expert to diagnose the problems and prescribe the proper remedies.
I'm going to ignore the personal attacks on me and answer this question because it is actually germain to the topic. A better analogy would be something like the New England Journal of Medicine Rational Clinical Exam Series where they examine a specific clinical disease entity and use meta-analysis in an attempt to determine which physical exam findings give the best relative risk ratios, sensitivities and specificities for diagnosing these disease processes.
It's a good series but as with all meta-analysis, there are problems inherent due to multiple confounding factors in both heterogenicity, homogeneity as well as differences in trial design and weighting abnormalities.
Ok, carry on gentlemen.
Any clinical study is going to face confounding factors that need to be considered. This is true since the beginning of science. But I'd imagine that the NEJM series that you're referring to still carries a significant amount of value, and similar studies have allowed medicine to operate far more efficiently over the last 100 years.
The notion that there are confounding factors that need to be considered when looking at basketball analytics is something that no one would argue with you over, and a far cry from the point that you've been making all along. I'm not even sure what your point is now, considering how much it's shifted around.
You need to learn the difference between a clinical study and a meta-analysis to engage in this discussion. _________________ “It took many years of vomiting up all the filth I’d been taught about myself, and half-believed, before I was able to walk on the earth as though I had a right to be here.”
― James Baldwin, Collected Essays
I try not to venture into topics that I don't know much about, but the medical analogy I'd make would be that analytics are similar to laboratory work. They identify what's working well and what isn't on a more specific level than the eye test does in the form of numbers, and then it's up to an expert to diagnose the problems and prescribe the proper remedies.
I'm going to ignore the personal attacks on me and answer this question because it is actually germain to the topic. A better analogy would be something like the New England Journal of Medicine Rational Clinical Exam Series where they examine a specific clinical disease entity and use meta-analysis in an attempt to determine which physical exam findings give the best relative risk ratios, sensitivities and specificities for diagnosing these disease processes.
It's a good series but as with all meta-analysis, there are problems inherent due to multiple confounding factors in both heterogenicity, homogeneity as well as differences in trial design and weighting abnormalities.
Ok, carry on gentlemen.
Any clinical study is going to face confounding factors that need to be considered. This is true since the beginning of science. But I'd imagine that the NEJM series that you're referring to still carries a significant amount of value, and similar studies have allowed medicine to operate far more efficiently over the last 100 years.
The notion that there are confounding factors that need to be considered when looking at basketball analytics is something that no one would argue with you over, and a far cry from the point that you've been making all along. I'm not even sure what your point is now, considering how much it's shifted around.
You need to learn the difference between a clinical study and a meta-analysis to engage in this discussion.
I know the difference between the two. For the purposes of this discussion, the difference is irrelevant, and just another attempt to obfuscate in an argument that you're losing badly. Confounding factors exist in both individual studies themselves, and then again in the analysis of multiple studies.
But once again, no one would be arguing with you if that's what your point had been all along.
KBCB is throwing around credentials and jargon, slightly miffed that alone isn't enough to end the debate.
Hunter is having a thoughtful discussion up to the point he become frustrated that the other side is being evasive, at which points he throws in some sarcastic comments.
Larry is plugging ahead like a trooper, no matter what childishness he encounters.
Goldenthroat is trying to stop the madness, which, ironically, stirs up the people he's trying to stop because they enjoy the madness.
I am being an annoying contrarian.
So we have the typical mishmash of people who are trying to communicate and people who are simply enjoying being inside their own little vaccuums. It's LG, baby!
Last edited by activeverb on Fri Jul 31, 2015 8:53 am; edited 2 times in total
KBCB is throwing around credentials and jargon, slightly miffed that alone isn't enough to end the debate.
Hunter is having a thoughtful discussion up to the point he become frustrated that the other side is being evasive, at which points he throws in some sarcastic comments.
Larry is plugging ahead like a trooper, no matter what childishness he encounters.
Goldenthroat is trying to stop the madness, which, ironically, stirs up the people he's trying to stop because they enjoy the madness.
I am being an annoying contrarian.
So we have the typical mishmash of people who are trying to communicate and people who are simply enjoying being inside their own little vaccuums. It's LG, baby!
I love how so many here who skewer me for daring to offer an opinion on advanced basketball statometrics because they think I don't have a clue are debating medicine with such conviction.
Actually this post might provide the key to these bizarre exchanges.
KBCB has been operating on the premise that analytics = stats.
It's not an uncommon faulty premise, but it explains some things. Advanced basketball "statometrics" are analytics, but analytics aren't advanced basketball "statometrics". They're more than that. Much more than that.
The only people who are skeptical of its value, appear to have a misunderstanding of what it is.
I love how so many here who skewer me for daring to offer an opinion on advanced basketball statometrics because they think I don't have a clue are debating medicine with such conviction.
1. People aren't debating medicine. They are debating the validity of an analogy comparing MRIs to analytics. The discussion is more about semantics than medicine.
2. I haven't seen anyone skewer you just for offering an opinion on analytics (which is what I assume you mean by "advanced basketball statometrics"). But some people have been questioning the validity and reasoning of your opinions.
I try not to venture into topics that I don't know much about, but the medical analogy I'd make would be that analytics are similar to laboratory work. They identify what's working well and what isn't on a more specific level than the eye test does in the form of numbers, and then it's up to an expert to diagnose the problems and prescribe the proper remedies.
I'm going to ignore the personal attacks on me and answer this question because it is actually germain to the topic. A better analogy would be something like the New England Journal of Medicine Rational Clinical Exam Series where they examine a specific clinical disease entity and use meta-analysis in an attempt to determine which physical exam findings give the best relative risk ratios, sensitivities and specificities for diagnosing these disease processes.
It's a good series but as with all meta-analysis, there are problems inherent due to multiple confounding factors in both heterogenicity, homogeneity as well as differences in trial design and weighting abnormalities.
Ok, carry on gentlemen.
Any clinical study is going to face confounding factors that need to be considered. This is true since the beginning of science. But I'd imagine that the NEJM series that you're referring to still carries a significant amount of value, and similar studies have allowed medicine to operate far more efficiently over the last 100 years.
The notion that there are confounding factors that need to be considered when looking at basketball analytics is something that no one would argue with you over, and a far cry from the point that you've been making all along. I'm not even sure what your point is now, considering how much it's shifted around.
You need to learn the difference between a clinical study and a meta-analysis to engage in this discussion.
I know the difference between the two. For the purposes of this discussion, the difference is irrelevant, and just another attempt to obfuscate in an argument that you're losing badly. Confounding factors exist in both individual studies themselves, and then again in the analysis of multiple studies.
But once again, no one would be arguing with you if that's what your point had been all along.
Uh....no. And that you further argue the point disproves your conclusion. _________________ “It took many years of vomiting up all the filth I’d been taught about myself, and half-believed, before I was able to walk on the earth as though I had a right to be here.”
― James Baldwin, Collected Essays
KBCB is throwing around credentials and jargon, slightly miffed that alone isn't enough to end the debate.
Hunter is having a thoughtful discussion up to the point he become frustrated that the other side is being evasive, at which points he throws in some sarcastic comments.
Larry is plugging ahead like a trooper, no matter what childishness he encounters.
Goldenthroat is trying to stop the madness, which, ironically, stirs up the people he's trying to stop because they enjoy the madness.
I am being an annoying contrarian.
So we have the typical mishmash of people who are trying to communicate and people who are simply enjoying being inside their own little vaccuums. It's LG, baby!
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