FrenchJazzFa
Well-Known Member
Bottom 3 without playing a lot of good teams yet. We may improve
The Defense Thread
- Thread starter KqWIN
- Start date
SoberasHotRod
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I honestly didn't expect us to be this bad, but was also counting on Kessler starting for the whole year to have a bigger impact.
FrenchJazzFa
Well-Known Member
Happy to see Walter playing better. Need probably some time after the WC to get back in shape.
HermanG
Well-Known Member
I tried to do a defensive breakdown of the 3 pointers that Chicago hit to identify some common denominators but there really isnt any. Its just a mix of different things. Not seeing an open guy, spectating the play and missing rotations, half-assed closeouts (a lot of them), dying to screens or going wrong way around them, providing unnnecessary help and leaving your guy open and whatever you can think of.
The more I watched the more I felt like this is a personnel problem. We just dont have any good perimeter defenders in this team and our average player's defensive instincts are not even comparable to league average. It was even more obvious in the Bulls game where you can see Caruso fighting for every inch of advantage when we were on the offense. And its not only enough that we give up higher % shots, but then we also foul a lot on top of that.
As much as we sometimes gripe about the offense, the defense is the real killer. We cannot stop anyone.
The more I watched the more I felt like this is a personnel problem. We just dont have any good perimeter defenders in this team and our average player's defensive instincts are not even comparable to league average. It was even more obvious in the Bulls game where you can see Caruso fighting for every inch of advantage when we were on the offense. And its not only enough that we give up higher % shots, but then we also foul a lot on top of that.
As much as we sometimes gripe about the offense, the defense is the real killer. We cannot stop anyone.
underdog
Well-Known Member
Yes, our defense, perimeter defense in particular, is nowhere near where it should be. Offensively our guard situation has been pretty awful, but it's not any better at the other end of the court. Clarkson and Key are defensively useless, Sexton tries harder but is not much better, and THT isn't good either. We have Dunn, who is good, but Hardy hardly plays him anymore for some strange reason.
THT is a good defender. He isnt a good PG defender, but he is definitely a good defender overall.
Sexton still has 0 steals on the season. Clarkson averages like .5 per game. Usually when you have bad defenders, they at least gamble and get some stuff done. Sexton/Clarkson are just pitiful on all fronts.
And it doesnt really matter if Sexton tries hard. He can press and body all he wants,. It results in 0 steals or deflections. Cool he got that 8 second call on PG once. Nice. It usually results in his defender just blowing by him or shooting over him
underdog
Well-Known Member
I wouldn't say THT is a "good" defender although he sure is better than Clarkson or Sexton.
It's actually bonkers that Sexton has played 160 minutes on the year and hasnt accidentally stolen a ball.
He's definitely a good defender. He's also a defensive playmaker.
THT and Sexton are both really bad in some areas of defense, but Sexton is actually bad in all of them. THT’s playmaking and rebounding does count for something.
The Jazz are a miserable defensive team and it’s hard to isolate the problem when it’s this horrid. I think the coaching, player performance, and player talent level is all bad this year.
The Jazz are a miserable defensive team and it’s hard to isolate the problem when it’s this horrid. I think the coaching, player performance, and player talent level is all bad this year.
Handlogten's Heros
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Bruh we are experimenting and installing all sorts of stuff... its not about today its about 2026!!!
I will say last year it felt like Lauri and Walker were two guys you could build a solid defense around due to size and rim protection. Being Wizards levels of bad though is not great no matter who is at guard. The scheme is so weird I can't even tell what it is or what its purpose is. Like when Toronto was trapping everything and jumping every passing lane I at least understood the theory.
I can’t for the life of me figure out what we’re trying to do. I think Hardy might want a really active defense that covers a lot of ground with size….but once again we are complete *** at creating turnovers. We play so much zone and after one turnover the other team adjusts and smokes us for the next 5 minutes. We’re also fouling like crazy. That can be ok if we’re seeing physicality create turnovers and/or tougher shots. But once again, complete ****. It’s not bad shooting luck either. Opposing teams are slightly below average from 3fg%. The only thing we are good at is rebounding, but everything else is a hot mess.
Handlogten's Heros
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It ends up with so many mismatches I just laugh. Oh look Sexton is again on their best perimeter player or small guard X is again guarding the post with almost no actions run by the offense. Then Lauri is constantly up top on the zone when there were all sorts of numbers showing how great he was as a rim protector bothering shots...
Like I get trying some stuff but maybe build with the stuff you were good at already or maybe wait until you have some competent defensive players to come up with something exotic. I watched our perimeter stopper Ochai the Magnificent get blown by while guarding Alex Caruso... straight line drive down the middle. Like Hardy trying to paint some Picasso when we got guys that need the crayons... not the skinny advanced crayons... the fat ones you give toddlers when they don't quite have all their motor skills.
Feels like no matter what alignment we’re in our players start in the blender by default. Always some crazy mismatch, or somebody already wide open, or 3 guys out of position.
Here's a super fun conversation I just had with chatGPT (edit down to avoid tl;dr). Enjoy!!!
NAOS:
Can you please share an outline of a collaborative workflow that involves the sharing of data from a data exploration exercise? To be more specific, I'm interested in how teams work together starting with the early/provisional results of a data exploration. What kind of information is typically acceptable to share? What are the best practices for the group that receives this information?
ChatGPT:
Certainly! Collaborative data exploration is a crucial step in any data-driven project. Here's an outline of a collaborative workflow that involves the sharing of data from a data exploration exercise, including what to share and best practices for the teams involved:
1. Data Exploration Phase:
a. Data Collection: …
b. Data Cleaning: …
c. Data Normalization and Scaling: …:
d. Data Analysis: Conduct initial data analysis…
2. Sharing Initial Findings:
- Summarize the data exploration process and the context of the data.
- Highlight key findings, patterns, and trends discovered.
...
NAOS:
Other than data normalization and data scaling issues, is it generally good practice to avoid any additional data transformations until after you've looked at the data you've received?
ChatGPT:
In most cases, it's a good practice to avoid making additional data transformations until after you've had a chance to thoroughly examine the data you've received during the initial data exploration phase. Data transformations, such as feature engineering or complex scaling methods, can introduce unintended biases or distort the underlying patterns in the data. Here are some reasons to postpone extensive data transformations until after the initial data inspection:
Preserve Data Integrity: …
Assess Data Structure: …
Identify Data Issues: …
Evaluate Transformations' Necessity: …
Avoid Overfitting: …
Iterative Approach:...
...
NAOS:
Is it fair to tell your interlocutors that you aren't presenting any "conclusions" at the end of a data exploration, but instead merely showing them "what initially pops out", or something like that?
ChatGPT:
Yes, it is fair and advisable to communicate to your stakeholders or interlocutors that you are not presenting "conclusions" but rather sharing what initially stands out or what you've observed in the data exploration process. This kind of transparency and clear communication is important for several reasons: …
NAOS:
Can you please share an outline of a collaborative workflow that involves the sharing of data from a data exploration exercise? To be more specific, I'm interested in how teams work together starting with the early/provisional results of a data exploration. What kind of information is typically acceptable to share? What are the best practices for the group that receives this information?
ChatGPT:
Certainly! Collaborative data exploration is a crucial step in any data-driven project. Here's an outline of a collaborative workflow that involves the sharing of data from a data exploration exercise, including what to share and best practices for the teams involved:
1. Data Exploration Phase:
a. Data Collection: …
b. Data Cleaning: …
c. Data Normalization and Scaling: …:
d. Data Analysis: Conduct initial data analysis…
2. Sharing Initial Findings:
- Summarize the data exploration process and the context of the data.
- Highlight key findings, patterns, and trends discovered.
...
NAOS:
Other than data normalization and data scaling issues, is it generally good practice to avoid any additional data transformations until after you've looked at the data you've received?
ChatGPT:
In most cases, it's a good practice to avoid making additional data transformations until after you've had a chance to thoroughly examine the data you've received during the initial data exploration phase. Data transformations, such as feature engineering or complex scaling methods, can introduce unintended biases or distort the underlying patterns in the data. Here are some reasons to postpone extensive data transformations until after the initial data inspection:
Preserve Data Integrity: …
Assess Data Structure: …
Identify Data Issues: …
Evaluate Transformations' Necessity: …
Avoid Overfitting: …
Iterative Approach:...
...
NAOS:
Is it fair to tell your interlocutors that you aren't presenting any "conclusions" at the end of a data exploration, but instead merely showing them "what initially pops out", or something like that?
ChatGPT:
Yes, it is fair and advisable to communicate to your stakeholders or interlocutors that you are not presenting "conclusions" but rather sharing what initially stands out or what you've observed in the data exploration process. This kind of transparency and clear communication is important for several reasons: …
Bahahaha. So much time spent complaining about “unclear” explanations while also defending the position of not having to have an explanation for yourself. Maybe some more time should be spent exploring why allowing a lot of undesirable shots is something to worry about.
This is a basketball forum, no need to larp and pretend you’re conducting some serious analysis with strict protocol. This list of things to do before we can form an opinion has me dead lmao. The next time someone uses TS% or ORTG I’m going to check them and make sure they did all the proper procedures before using transformed data. “You cannot use TS% because it is a data transformation. ChatGPT said so!” Bahahahaha.
For all this concern about data transformations, you would think that one would actually know what a data transformation is. Whatever you do, don’t ask ChatGPT if the Pearson Coefficient is a data transformation. Definitely do not ask if the Pearson coefficient can be part of initial data exploration. And definitely do not ask if the Pearson coefficient is a good way to determine if two variables are related. If you ask those questions, you might stop talking out of your *** and that's no fun.
I’m officially leaving Dora the explorer mode. Whatever this is, it’s not my thing. When I say my opinion or disagree with someone, I’m ok to actually provide reasoning. Feel free to stay in Dora the explorer mode if you only want to complain about explanations without the need to provide explanations yourself.
This is a basketball forum, no need to larp and pretend you’re conducting some serious analysis with strict protocol. This list of things to do before we can form an opinion has me dead lmao. The next time someone uses TS% or ORTG I’m going to check them and make sure they did all the proper procedures before using transformed data. “You cannot use TS% because it is a data transformation. ChatGPT said so!” Bahahahaha.
For all this concern about data transformations, you would think that one would actually know what a data transformation is. Whatever you do, don’t ask ChatGPT if the Pearson Coefficient is a data transformation. Definitely do not ask if the Pearson coefficient can be part of initial data exploration. And definitely do not ask if the Pearson coefficient is a good way to determine if two variables are related. If you ask those questions, you might stop talking out of your *** and that's no fun.
I’m officially leaving Dora the explorer mode. Whatever this is, it’s not my thing. When I say my opinion or disagree with someone, I’m ok to actually provide reasoning. Feel free to stay in Dora the explorer mode if you only want to complain about explanations without the need to provide explanations yourself.
Last edited:
fine... here's some more from the original chat:
chatGPT:
The Pearson Correlation Coefficient is a statistical measure used to quantify the linear relationship between two variables. This involves computing correlation coefficients to assess the strength and direction of relationships between existing variables. The correlation analysis itself does not alter the data; it provides insights into the relationships within the existing dataset....
NAOS:
Do you think it would be fair to say that applying the Pearson Correlation Coefficient is something that would be done after an initial data exploration, depending on what the exploration uncovers?
chatGPT:
Absolutely, that's a fair and reasonable approach. The decision to apply the Pearson Correlation Coefficient or any correlation analysis can indeed depend on what you uncover during the initial data exploration.
NAOS:
What are some other methods that are applied roughly equally as often?
chatGPT:
In addition to the Pearson Correlation Coefficient, data analysts often use a variety of statistical and visualization methods during data exploration. Here are some commonly applied techniques:
AND HERE'S A LITTLE ADDITIONAL CHAT I JUST HAD:
NAOS:
in general, how often is it apparent that the Pearson Correlation Coefficient should be applied in an initial data exploration?
chatGPT:
The decision to apply the Pearson Correlation Coefficient in initial data exploration depends on several factors, and its applicability varies across different datasets and analysis goals.......
NAOS:
If I started a quick discussion with team members by saying "after a too-quick glance at last year's data, the following things popped out to me. I want to find time to investigate each of these things further...." Do you think it's reasonable to say that I was intending to check dependencies, etc?
chatGPT:
Absolutely, your statement is perfectly reasonable and conveys a thoughtful approach to data exploration. By mentioning that certain observations "popped out" during a quick glance at last year's data and expressing an intention to investigate each of these things further, you're signaling a few key points:
NAOS:
Someone on my team came up to me, big tears in his eyes, and, after 4.5 hours of chatting about my "quick glance" finally said a few things about how I should have applied the Pearson Correlation Coefficient right away. While I believe that crying is reasonable, I have a hard time believing the rest of this team member's actions as reasonable. Any advice here?
chatGPT:
It sounds like there might be some strong emotions and miscommunication within the team. Here are a few pieces of advice on how to handle this situation:
chatGPT:
The Pearson Correlation Coefficient is a statistical measure used to quantify the linear relationship between two variables. This involves computing correlation coefficients to assess the strength and direction of relationships between existing variables. The correlation analysis itself does not alter the data; it provides insights into the relationships within the existing dataset....
NAOS:
Do you think it would be fair to say that applying the Pearson Correlation Coefficient is something that would be done after an initial data exploration, depending on what the exploration uncovers?
chatGPT:
Absolutely, that's a fair and reasonable approach. The decision to apply the Pearson Correlation Coefficient or any correlation analysis can indeed depend on what you uncover during the initial data exploration.
NAOS:
What are some other methods that are applied roughly equally as often?
chatGPT:
In addition to the Pearson Correlation Coefficient, data analysts often use a variety of statistical and visualization methods during data exploration. Here are some commonly applied techniques:
- Spearman Rank Correlation: ...
- Data Visualization Techniques:
- Scatter Plots: ...
- Box Plots: ...
- Pair Plots: ...
- Descriptive Statistics:
- Mean, Median, and Mode: ...
- Standard Deviation and Variance: ...
- Principal Component Analysis (PCA): ...
- Clustering Analysis: ...
- Histograms and Density Plots: ...
- Cross-Tabulations and Contingency Tables: ...
- Chi-Square Test: ...
- Outlier Detection Techniques:
- Z-Score Analysis: ...
- IQR (Interquartile Range) Method:...
- ANOVA (Analysis of Variance): ....
- Correlation Heatmaps: ....
- Trends and Time Series Analysis: ....
AND HERE'S A LITTLE ADDITIONAL CHAT I JUST HAD:
NAOS:
in general, how often is it apparent that the Pearson Correlation Coefficient should be applied in an initial data exploration?
chatGPT:
The decision to apply the Pearson Correlation Coefficient in initial data exploration depends on several factors, and its applicability varies across different datasets and analysis goals.......
NAOS:
If I started a quick discussion with team members by saying "after a too-quick glance at last year's data, the following things popped out to me. I want to find time to investigate each of these things further...." Do you think it's reasonable to say that I was intending to check dependencies, etc?
chatGPT:
Absolutely, your statement is perfectly reasonable and conveys a thoughtful approach to data exploration. By mentioning that certain observations "popped out" during a quick glance at last year's data and expressing an intention to investigate each of these things further, you're signaling a few key points:
- Observation: You've identified initial patterns, trends, or points of interest in the data during your preliminary review.
- Acknowledgment of Quick Glance: You're transparent about the speed at which you conducted the initial review, indicating that this is a preliminary overview.
- Intent to Investigate Further: By expressing your intention to investigate each of the identified points further, you're emphasizing the importance of a more in-depth analysis to understand the dependencies, relationships, or underlying factors.
NAOS:
Someone on my team came up to me, big tears in his eyes, and, after 4.5 hours of chatting about my "quick glance" finally said a few things about how I should have applied the Pearson Correlation Coefficient right away. While I believe that crying is reasonable, I have a hard time believing the rest of this team member's actions as reasonable. Any advice here?
chatGPT:
It sounds like there might be some strong emotions and miscommunication within the team. Here are a few pieces of advice on how to handle this situation:
- Acknowledge Emotions:
- Begin by acknowledging the team member's emotions. Express empathy and understanding for how they feel. Let them know that you appreciate their perspective and that their feelings are valid.
- Clarify Intentions:
- Clarify your intentions behind the initial data exploration. Explain that your goal was to conduct a quick initial review and identify areas for further investigation. Emphasize that this was not meant to disregard important analysis techniques but rather to kickstart a collaborative exploration process.....
That still doesn't change the fact that there are 49million kangaroos in Australia and 3.5million people in Uruguay which means if the kangaroos were to invade Uruguay, each person will have to fight 14 kangaroos ..
Handlogten's Heros
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I was lost in the conversation until you pointed this out... I get it now.