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Dashboard Week. Day 2.

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

Battle of the Giants: Does Size Really Matter?

 

I know absolutely nothing about basketball. All I really know is that basketball players are tall. So, when I got access to the NCAA dataset, that was the first thing that caught my attention. I wanted to answer a very simple question: does being tall actually make you a better basketball player?

The idea was straightforward. Take players in each position –Center, Forward, Guard – and compare the tallest ones with the rest. Is being taller an actual advantage in performance?

I started this analysis by creating a basic plan. Group players by position. Rank them by height. Compare the performance of the tallest players in each group against their shorter teammates. Simple enough.

 

What went wrong?

At least, it seemed simple.

I thought it would be a good idea to make this dynamic. Let users choose whether to compare the tallest 5, 10, 15 players and see how the results change. What happened next was several hours of fighting Power BI, writing and rewriting DAX, hitting constant errors, breaking context, and discovering how fragile the logic can get when dynamic grouping meets complicated relationships.

There were a few moments where I genuinely considered giving up on the entire idea. Nothing worked the way it should. The parameter broke. The rank broke. The filters broke. The labels broke. And so did my patience.

 

The Solution

Eventually, I made a decision that every analyst makes at some point. Forget fancy. Do what works.

I dropped the parameter idea completely. I focused on a clean, static split: the tallest 10 players in each position versus the rest. No slicers. No dynamic measures. No more fighting with the model. Just simple logic that holds.

This change didn’t just save my sanity. It actually made the dashboard easier to interpret.

 

The Findings

Height makes a difference, but only for certain things. Taller players block more and grab more rebounds, especially among centers and forwards. For guards, the difference is smaller but still there.

For points, the impact of height is inconsistent. Taller guards actually score more, but for forwards, the shorter players score slightly higher. Centers rely less on scoring either way.

When it comes to assists and steals, being shorter seems to be an advantage. Across all positions, the shortest players consistently outperform the tallest in those stats.

The scatterplots make this clear. Height strongly impacts rebounds and blocks but has little to no relationship with points, assists, or steals.

 

What Didn’t Work

I also tried to look at whether taller teams win more games. That analysis went nowhere. I plotted average team height against win percentage and got nothing but a wall of random dots. There was no pattern at all. I dropped that idea completely.

 

The Final Report

The final dashboard focuses entirely on player-level analysis. It shows the height distribution across positions and compares how the Tallest 10 players in each group perform compared to the rest. It also includes a scatterplot of individual players’ height versus their key stats, which makes it easy to explore whether height affects performance on an individual level.

 

 

The Conclusion

It’s pretty simple. Height matters, but only when it matters. If you’re trying to rebound or block shots, being tall helps. If you’re trying to score or assist, it doesn’t matter as much.

This project also reminded me of something important about data work. Not every problem needs a dynamic, complex solution. Sometimes, the simplest solution is not just the easiest it’s the best.

 

Share this post

Dashboard Week. Day 2.

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

Battle of the Giants: Does Size Really Matter?

 

I know absolutely nothing about basketball. All I really know is that basketball players are tall. So, when I got access to the NCAA dataset, that was the first thing that caught my attention. I wanted to answer a very simple question: does being tall actually make you a better basketball player?

The idea was straightforward. Take players in each position –Center, Forward, Guard – and compare the tallest ones with the rest. Is being taller an actual advantage in performance?

I started this analysis by creating a basic plan. Group players by position. Rank them by height. Compare the performance of the tallest players in each group against their shorter teammates. Simple enough.

 

What went wrong?

At least, it seemed simple.

I thought it would be a good idea to make this dynamic. Let users choose whether to compare the tallest 5, 10, 15 players and see how the results change. What happened next was several hours of fighting Power BI, writing and rewriting DAX, hitting constant errors, breaking context, and discovering how fragile the logic can get when dynamic grouping meets complicated relationships.

There were a few moments where I genuinely considered giving up on the entire idea. Nothing worked the way it should. The parameter broke. The rank broke. The filters broke. The labels broke. And so did my patience.

 

The Solution

Eventually, I made a decision that every analyst makes at some point. Forget fancy. Do what works.

I dropped the parameter idea completely. I focused on a clean, static split: the tallest 10 players in each position versus the rest. No slicers. No dynamic measures. No more fighting with the model. Just simple logic that holds.

This change didn’t just save my sanity. It actually made the dashboard easier to interpret.

 

The Findings

Height makes a difference, but only for certain things. Taller players block more and grab more rebounds, especially among centers and forwards. For guards, the difference is smaller but still there.

For points, the impact of height is inconsistent. Taller guards actually score more, but for forwards, the shorter players score slightly higher. Centers rely less on scoring either way.

When it comes to assists and steals, being shorter seems to be an advantage. Across all positions, the shortest players consistently outperform the tallest in those stats.

The scatterplots make this clear. Height strongly impacts rebounds and blocks but has little to no relationship with points, assists, or steals.

 

What Didn’t Work

I also tried to look at whether taller teams win more games. That analysis went nowhere. I plotted average team height against win percentage and got nothing but a wall of random dots. There was no pattern at all. I dropped that idea completely.

 

The Final Report

The final dashboard focuses entirely on player-level analysis. It shows the height distribution across positions and compares how the Tallest 10 players in each group perform compared to the rest. It also includes a scatterplot of individual players’ height versus their key stats, which makes it easy to explore whether height affects performance on an individual level.

 

 

The Conclusion

It’s pretty simple. Height matters, but only when it matters. If you’re trying to rebound or block shots, being tall helps. If you’re trying to score or assist, it doesn’t matter as much.

This project also reminded me of something important about data work. Not every problem needs a dynamic, complex solution. Sometimes, the simplest solution is not just the easiest it’s the best.

 

Share this post

Dashboard Week. Day 2.

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

Battle of the Giants: Does Size Really Matter?

 

I know absolutely nothing about basketball. All I really know is that basketball players are tall. So, when I got access to the NCAA dataset, that was the first thing that caught my attention. I wanted to answer a very simple question: does being tall actually make you a better basketball player?

The idea was straightforward. Take players in each position –Center, Forward, Guard – and compare the tallest ones with the rest. Is being taller an actual advantage in performance?

I started this analysis by creating a basic plan. Group players by position. Rank them by height. Compare the performance of the tallest players in each group against their shorter teammates. Simple enough.

 

What went wrong?

At least, it seemed simple.

I thought it would be a good idea to make this dynamic. Let users choose whether to compare the tallest 5, 10, 15 players and see how the results change. What happened next was several hours of fighting Power BI, writing and rewriting DAX, hitting constant errors, breaking context, and discovering how fragile the logic can get when dynamic grouping meets complicated relationships.

There were a few moments where I genuinely considered giving up on the entire idea. Nothing worked the way it should. The parameter broke. The rank broke. The filters broke. The labels broke. And so did my patience.

 

The Solution

Eventually, I made a decision that every analyst makes at some point. Forget fancy. Do what works.

I dropped the parameter idea completely. I focused on a clean, static split: the tallest 10 players in each position versus the rest. No slicers. No dynamic measures. No more fighting with the model. Just simple logic that holds.

This change didn’t just save my sanity. It actually made the dashboard easier to interpret.

 

The Findings

Height makes a difference, but only for certain things. Taller players block more and grab more rebounds, especially among centers and forwards. For guards, the difference is smaller but still there.

For points, the impact of height is inconsistent. Taller guards actually score more, but for forwards, the shorter players score slightly higher. Centers rely less on scoring either way.

When it comes to assists and steals, being shorter seems to be an advantage. Across all positions, the shortest players consistently outperform the tallest in those stats.

The scatterplots make this clear. Height strongly impacts rebounds and blocks but has little to no relationship with points, assists, or steals.

 

What Didn’t Work

I also tried to look at whether taller teams win more games. That analysis went nowhere. I plotted average team height against win percentage and got nothing but a wall of random dots. There was no pattern at all. I dropped that idea completely.

 

The Final Report

The final dashboard focuses entirely on player-level analysis. It shows the height distribution across positions and compares how the Tallest 10 players in each group perform compared to the rest. It also includes a scatterplot of individual players’ height versus their key stats, which makes it easy to explore whether height affects performance on an individual level.

 

 

The Conclusion

It’s pretty simple. Height matters, but only when it matters. If you’re trying to rebound or block shots, being tall helps. If you’re trying to score or assist, it doesn’t matter as much.

This project also reminded me of something important about data work. Not every problem needs a dynamic, complex solution. Sometimes, the simplest solution is not just the easiest it’s the best.

 

Share this post

Dashboard Week. Day 2.

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

Battle of the Giants: Does Size Really Matter?

 

I know absolutely nothing about basketball. All I really know is that basketball players are tall. So, when I got access to the NCAA dataset, that was the first thing that caught my attention. I wanted to answer a very simple question: does being tall actually make you a better basketball player?

The idea was straightforward. Take players in each position –Center, Forward, Guard – and compare the tallest ones with the rest. Is being taller an actual advantage in performance?

I started this analysis by creating a basic plan. Group players by position. Rank them by height. Compare the performance of the tallest players in each group against their shorter teammates. Simple enough.

 

What went wrong?

At least, it seemed simple.

I thought it would be a good idea to make this dynamic. Let users choose whether to compare the tallest 5, 10, 15 players and see how the results change. What happened next was several hours of fighting Power BI, writing and rewriting DAX, hitting constant errors, breaking context, and discovering how fragile the logic can get when dynamic grouping meets complicated relationships.

There were a few moments where I genuinely considered giving up on the entire idea. Nothing worked the way it should. The parameter broke. The rank broke. The filters broke. The labels broke. And so did my patience.

 

The Solution

Eventually, I made a decision that every analyst makes at some point. Forget fancy. Do what works.

I dropped the parameter idea completely. I focused on a clean, static split: the tallest 10 players in each position versus the rest. No slicers. No dynamic measures. No more fighting with the model. Just simple logic that holds.

This change didn’t just save my sanity. It actually made the dashboard easier to interpret.

 

The Findings

Height makes a difference, but only for certain things. Taller players block more and grab more rebounds, especially among centers and forwards. For guards, the difference is smaller but still there.

For points, the impact of height is inconsistent. Taller guards actually score more, but for forwards, the shorter players score slightly higher. Centers rely less on scoring either way.

When it comes to assists and steals, being shorter seems to be an advantage. Across all positions, the shortest players consistently outperform the tallest in those stats.

The scatterplots make this clear. Height strongly impacts rebounds and blocks but has little to no relationship with points, assists, or steals.

 

What Didn’t Work

I also tried to look at whether taller teams win more games. That analysis went nowhere. I plotted average team height against win percentage and got nothing but a wall of random dots. There was no pattern at all. I dropped that idea completely.

 

The Final Report

The final dashboard focuses entirely on player-level analysis. It shows the height distribution across positions and compares how the Tallest 10 players in each group perform compared to the rest. It also includes a scatterplot of individual players’ height versus their key stats, which makes it easy to explore whether height affects performance on an individual level.

 

 

The Conclusion

It’s pretty simple. Height matters, but only when it matters. If you’re trying to rebound or block shots, being tall helps. If you’re trying to score or assist, it doesn’t matter as much.

This project also reminded me of something important about data work. Not every problem needs a dynamic, complex solution. Sometimes, the simplest solution is not just the easiest it’s the best.

 

Share this post

Dashboard Week. Day 2.

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

Battle of the Giants: Does Size Really Matter?

 

I know absolutely nothing about basketball. All I really know is that basketball players are tall. So, when I got access to the NCAA dataset, that was the first thing that caught my attention. I wanted to answer a very simple question: does being tall actually make you a better basketball player?

The idea was straightforward. Take players in each position –Center, Forward, Guard – and compare the tallest ones with the rest. Is being taller an actual advantage in performance?

I started this analysis by creating a basic plan. Group players by position. Rank them by height. Compare the performance of the tallest players in each group against their shorter teammates. Simple enough.

 

What went wrong?

At least, it seemed simple.

I thought it would be a good idea to make this dynamic. Let users choose whether to compare the tallest 5, 10, 15 players and see how the results change. What happened next was several hours of fighting Power BI, writing and rewriting DAX, hitting constant errors, breaking context, and discovering how fragile the logic can get when dynamic grouping meets complicated relationships.

There were a few moments where I genuinely considered giving up on the entire idea. Nothing worked the way it should. The parameter broke. The rank broke. The filters broke. The labels broke. And so did my patience.

 

The Solution

Eventually, I made a decision that every analyst makes at some point. Forget fancy. Do what works.

I dropped the parameter idea completely. I focused on a clean, static split: the tallest 10 players in each position versus the rest. No slicers. No dynamic measures. No more fighting with the model. Just simple logic that holds.

This change didn’t just save my sanity. It actually made the dashboard easier to interpret.

 

The Findings

Height makes a difference, but only for certain things. Taller players block more and grab more rebounds, especially among centers and forwards. For guards, the difference is smaller but still there.

For points, the impact of height is inconsistent. Taller guards actually score more, but for forwards, the shorter players score slightly higher. Centers rely less on scoring either way.

When it comes to assists and steals, being shorter seems to be an advantage. Across all positions, the shortest players consistently outperform the tallest in those stats.

The scatterplots make this clear. Height strongly impacts rebounds and blocks but has little to no relationship with points, assists, or steals.

 

What Didn’t Work

I also tried to look at whether taller teams win more games. That analysis went nowhere. I plotted average team height against win percentage and got nothing but a wall of random dots. There was no pattern at all. I dropped that idea completely.

 

The Final Report

The final dashboard focuses entirely on player-level analysis. It shows the height distribution across positions and compares how the Tallest 10 players in each group perform compared to the rest. It also includes a scatterplot of individual players’ height versus their key stats, which makes it easy to explore whether height affects performance on an individual level.

 

 

The Conclusion

It’s pretty simple. Height matters, but only when it matters. If you’re trying to rebound or block shots, being tall helps. If you’re trying to score or assist, it doesn’t matter as much.

This project also reminded me of something important about data work. Not every problem needs a dynamic, complex solution. Sometimes, the simplest solution is not just the easiest it’s the best.

 

Share this post

Dashboard Week. Day 2.

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

Battle of the Giants: Does Size Really Matter?

 

I know absolutely nothing about basketball. All I really know is that basketball players are tall. So, when I got access to the NCAA dataset, that was the first thing that caught my attention. I wanted to answer a very simple question: does being tall actually make you a better basketball player?

The idea was straightforward. Take players in each position –Center, Forward, Guard – and compare the tallest ones with the rest. Is being taller an actual advantage in performance?

I started this analysis by creating a basic plan. Group players by position. Rank them by height. Compare the performance of the tallest players in each group against their shorter teammates. Simple enough.

 

What went wrong?

At least, it seemed simple.

I thought it would be a good idea to make this dynamic. Let users choose whether to compare the tallest 5, 10, 15 players and see how the results change. What happened next was several hours of fighting Power BI, writing and rewriting DAX, hitting constant errors, breaking context, and discovering how fragile the logic can get when dynamic grouping meets complicated relationships.

There were a few moments where I genuinely considered giving up on the entire idea. Nothing worked the way it should. The parameter broke. The rank broke. The filters broke. The labels broke. And so did my patience.

 

The Solution

Eventually, I made a decision that every analyst makes at some point. Forget fancy. Do what works.

I dropped the parameter idea completely. I focused on a clean, static split: the tallest 10 players in each position versus the rest. No slicers. No dynamic measures. No more fighting with the model. Just simple logic that holds.

This change didn’t just save my sanity. It actually made the dashboard easier to interpret.

 

The Findings

Height makes a difference, but only for certain things. Taller players block more and grab more rebounds, especially among centers and forwards. For guards, the difference is smaller but still there.

For points, the impact of height is inconsistent. Taller guards actually score more, but for forwards, the shorter players score slightly higher. Centers rely less on scoring either way.

When it comes to assists and steals, being shorter seems to be an advantage. Across all positions, the shortest players consistently outperform the tallest in those stats.

The scatterplots make this clear. Height strongly impacts rebounds and blocks but has little to no relationship with points, assists, or steals.

 

What Didn’t Work

I also tried to look at whether taller teams win more games. That analysis went nowhere. I plotted average team height against win percentage and got nothing but a wall of random dots. There was no pattern at all. I dropped that idea completely.

 

The Final Report

The final dashboard focuses entirely on player-level analysis. It shows the height distribution across positions and compares how the Tallest 10 players in each group perform compared to the rest. It also includes a scatterplot of individual players’ height versus their key stats, which makes it easy to explore whether height affects performance on an individual level.

 

 

The Conclusion

It’s pretty simple. Height matters, but only when it matters. If you’re trying to rebound or block shots, being tall helps. If you’re trying to score or assist, it doesn’t matter as much.

This project also reminded me of something important about data work. Not every problem needs a dynamic, complex solution. Sometimes, the simplest solution is not just the easiest it’s the best.

 

Share this post

Dashboard Week. Day 2.

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

Battle of the Giants: Does Size Really Matter?

 

I know absolutely nothing about basketball. All I really know is that basketball players are tall. So, when I got access to the NCAA dataset, that was the first thing that caught my attention. I wanted to answer a very simple question: does being tall actually make you a better basketball player?

The idea was straightforward. Take players in each position –Center, Forward, Guard – and compare the tallest ones with the rest. Is being taller an actual advantage in performance?

I started this analysis by creating a basic plan. Group players by position. Rank them by height. Compare the performance of the tallest players in each group against their shorter teammates. Simple enough.

 

What went wrong?

At least, it seemed simple.

I thought it would be a good idea to make this dynamic. Let users choose whether to compare the tallest 5, 10, 15 players and see how the results change. What happened next was several hours of fighting Power BI, writing and rewriting DAX, hitting constant errors, breaking context, and discovering how fragile the logic can get when dynamic grouping meets complicated relationships.

There were a few moments where I genuinely considered giving up on the entire idea. Nothing worked the way it should. The parameter broke. The rank broke. The filters broke. The labels broke. And so did my patience.

 

The Solution

Eventually, I made a decision that every analyst makes at some point. Forget fancy. Do what works.

I dropped the parameter idea completely. I focused on a clean, static split: the tallest 10 players in each position versus the rest. No slicers. No dynamic measures. No more fighting with the model. Just simple logic that holds.

This change didn’t just save my sanity. It actually made the dashboard easier to interpret.

 

The Findings

Height makes a difference, but only for certain things. Taller players block more and grab more rebounds, especially among centers and forwards. For guards, the difference is smaller but still there.

For points, the impact of height is inconsistent. Taller guards actually score more, but for forwards, the shorter players score slightly higher. Centers rely less on scoring either way.

When it comes to assists and steals, being shorter seems to be an advantage. Across all positions, the shortest players consistently outperform the tallest in those stats.

The scatterplots make this clear. Height strongly impacts rebounds and blocks but has little to no relationship with points, assists, or steals.

 

What Didn’t Work

I also tried to look at whether taller teams win more games. That analysis went nowhere. I plotted average team height against win percentage and got nothing but a wall of random dots. There was no pattern at all. I dropped that idea completely.

 

The Final Report

The final dashboard focuses entirely on player-level analysis. It shows the height distribution across positions and compares how the Tallest 10 players in each group perform compared to the rest. It also includes a scatterplot of individual players’ height versus their key stats, which makes it easy to explore whether height affects performance on an individual level.

 

 

The Conclusion

It’s pretty simple. Height matters, but only when it matters. If you’re trying to rebound or block shots, being tall helps. If you’re trying to score or assist, it doesn’t matter as much.

This project also reminded me of something important about data work. Not every problem needs a dynamic, complex solution. Sometimes, the simplest solution is not just the easiest it’s the best.

 

Share this post

Dashboard Week. Day 2.

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

Battle of the Giants: Does Size Really Matter?

 

I know absolutely nothing about basketball. All I really know is that basketball players are tall. So, when I got access to the NCAA dataset, that was the first thing that caught my attention. I wanted to answer a very simple question: does being tall actually make you a better basketball player?

The idea was straightforward. Take players in each position –Center, Forward, Guard – and compare the tallest ones with the rest. Is being taller an actual advantage in performance?

I started this analysis by creating a basic plan. Group players by position. Rank them by height. Compare the performance of the tallest players in each group against their shorter teammates. Simple enough.

 

What went wrong?

At least, it seemed simple.

I thought it would be a good idea to make this dynamic. Let users choose whether to compare the tallest 5, 10, 15 players and see how the results change. What happened next was several hours of fighting Power BI, writing and rewriting DAX, hitting constant errors, breaking context, and discovering how fragile the logic can get when dynamic grouping meets complicated relationships.

There were a few moments where I genuinely considered giving up on the entire idea. Nothing worked the way it should. The parameter broke. The rank broke. The filters broke. The labels broke. And so did my patience.

 

The Solution

Eventually, I made a decision that every analyst makes at some point. Forget fancy. Do what works.

I dropped the parameter idea completely. I focused on a clean, static split: the tallest 10 players in each position versus the rest. No slicers. No dynamic measures. No more fighting with the model. Just simple logic that holds.

This change didn’t just save my sanity. It actually made the dashboard easier to interpret.

 

The Findings

Height makes a difference, but only for certain things. Taller players block more and grab more rebounds, especially among centers and forwards. For guards, the difference is smaller but still there.

For points, the impact of height is inconsistent. Taller guards actually score more, but for forwards, the shorter players score slightly higher. Centers rely less on scoring either way.

When it comes to assists and steals, being shorter seems to be an advantage. Across all positions, the shortest players consistently outperform the tallest in those stats.

The scatterplots make this clear. Height strongly impacts rebounds and blocks but has little to no relationship with points, assists, or steals.

 

What Didn’t Work

I also tried to look at whether taller teams win more games. That analysis went nowhere. I plotted average team height against win percentage and got nothing but a wall of random dots. There was no pattern at all. I dropped that idea completely.

 

The Final Report

The final dashboard focuses entirely on player-level analysis. It shows the height distribution across positions and compares how the Tallest 10 players in each group perform compared to the rest. It also includes a scatterplot of individual players’ height versus their key stats, which makes it easy to explore whether height affects performance on an individual level.

 

 

The Conclusion

It’s pretty simple. Height matters, but only when it matters. If you’re trying to rebound or block shots, being tall helps. If you’re trying to score or assist, it doesn’t matter as much.

This project also reminded me of something important about data work. Not every problem needs a dynamic, complex solution. Sometimes, the simplest solution is not just the easiest it’s the best.

 

Share this post

Dashboard Week. Day 2.

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

Battle of the Giants: Does Size Really Matter?

 

I know absolutely nothing about basketball. All I really know is that basketball players are tall. So, when I got access to the NCAA dataset, that was the first thing that caught my attention. I wanted to answer a very simple question: does being tall actually make you a better basketball player?

The idea was straightforward. Take players in each position –Center, Forward, Guard – and compare the tallest ones with the rest. Is being taller an actual advantage in performance?

I started this analysis by creating a basic plan. Group players by position. Rank them by height. Compare the performance of the tallest players in each group against their shorter teammates. Simple enough.

 

What went wrong?

At least, it seemed simple.

I thought it would be a good idea to make this dynamic. Let users choose whether to compare the tallest 5, 10, 15 players and see how the results change. What happened next was several hours of fighting Power BI, writing and rewriting DAX, hitting constant errors, breaking context, and discovering how fragile the logic can get when dynamic grouping meets complicated relationships.

There were a few moments where I genuinely considered giving up on the entire idea. Nothing worked the way it should. The parameter broke. The rank broke. The filters broke. The labels broke. And so did my patience.

 

The Solution

Eventually, I made a decision that every analyst makes at some point. Forget fancy. Do what works.

I dropped the parameter idea completely. I focused on a clean, static split: the tallest 10 players in each position versus the rest. No slicers. No dynamic measures. No more fighting with the model. Just simple logic that holds.

This change didn’t just save my sanity. It actually made the dashboard easier to interpret.

 

The Findings

Height makes a difference, but only for certain things. Taller players block more and grab more rebounds, especially among centers and forwards. For guards, the difference is smaller but still there.

For points, the impact of height is inconsistent. Taller guards actually score more, but for forwards, the shorter players score slightly higher. Centers rely less on scoring either way.

When it comes to assists and steals, being shorter seems to be an advantage. Across all positions, the shortest players consistently outperform the tallest in those stats.

The scatterplots make this clear. Height strongly impacts rebounds and blocks but has little to no relationship with points, assists, or steals.

 

What Didn’t Work

I also tried to look at whether taller teams win more games. That analysis went nowhere. I plotted average team height against win percentage and got nothing but a wall of random dots. There was no pattern at all. I dropped that idea completely.

 

The Final Report

The final dashboard focuses entirely on player-level analysis. It shows the height distribution across positions and compares how the Tallest 10 players in each group perform compared to the rest. It also includes a scatterplot of individual players’ height versus their key stats, which makes it easy to explore whether height affects performance on an individual level.

 

 

The Conclusion

It’s pretty simple. Height matters, but only when it matters. If you’re trying to rebound or block shots, being tall helps. If you’re trying to score or assist, it doesn’t matter as much.

This project also reminded me of something important about data work. Not every problem needs a dynamic, complex solution. Sometimes, the simplest solution is not just the easiest it’s the best.

 

Share this post

Dashboard Week. Day 2.

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

Battle of the Giants: Does Size Really Matter?

 

I know absolutely nothing about basketball. All I really know is that basketball players are tall. So, when I got access to the NCAA dataset, that was the first thing that caught my attention. I wanted to answer a very simple question: does being tall actually make you a better basketball player?

The idea was straightforward. Take players in each position –Center, Forward, Guard – and compare the tallest ones with the rest. Is being taller an actual advantage in performance?

I started this analysis by creating a basic plan. Group players by position. Rank them by height. Compare the performance of the tallest players in each group against their shorter teammates. Simple enough.

 

What went wrong?

At least, it seemed simple.

I thought it would be a good idea to make this dynamic. Let users choose whether to compare the tallest 5, 10, 15 players and see how the results change. What happened next was several hours of fighting Power BI, writing and rewriting DAX, hitting constant errors, breaking context, and discovering how fragile the logic can get when dynamic grouping meets complicated relationships.

There were a few moments where I genuinely considered giving up on the entire idea. Nothing worked the way it should. The parameter broke. The rank broke. The filters broke. The labels broke. And so did my patience.

 

The Solution

Eventually, I made a decision that every analyst makes at some point. Forget fancy. Do what works.

I dropped the parameter idea completely. I focused on a clean, static split: the tallest 10 players in each position versus the rest. No slicers. No dynamic measures. No more fighting with the model. Just simple logic that holds.

This change didn’t just save my sanity. It actually made the dashboard easier to interpret.

 

The Findings

Height makes a difference, but only for certain things. Taller players block more and grab more rebounds, especially among centers and forwards. For guards, the difference is smaller but still there.

For points, the impact of height is inconsistent. Taller guards actually score more, but for forwards, the shorter players score slightly higher. Centers rely less on scoring either way.

When it comes to assists and steals, being shorter seems to be an advantage. Across all positions, the shortest players consistently outperform the tallest in those stats.

The scatterplots make this clear. Height strongly impacts rebounds and blocks but has little to no relationship with points, assists, or steals.

 

What Didn’t Work

I also tried to look at whether taller teams win more games. That analysis went nowhere. I plotted average team height against win percentage and got nothing but a wall of random dots. There was no pattern at all. I dropped that idea completely.

 

The Final Report

The final dashboard focuses entirely on player-level analysis. It shows the height distribution across positions and compares how the Tallest 10 players in each group perform compared to the rest. It also includes a scatterplot of individual players’ height versus their key stats, which makes it easy to explore whether height affects performance on an individual level.

 

 

The Conclusion

It’s pretty simple. Height matters, but only when it matters. If you’re trying to rebound or block shots, being tall helps. If you’re trying to score or assist, it doesn’t matter as much.

This project also reminded me of something important about data work. Not every problem needs a dynamic, complex solution. Sometimes, the simplest solution is not just the easiest it’s the best.

 

Share this post

Dashboard Week. Day 2.

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

Battle of the Giants: Does Size Really Matter?

 

I know absolutely nothing about basketball. All I really know is that basketball players are tall. So, when I got access to the NCAA dataset, that was the first thing that caught my attention. I wanted to answer a very simple question: does being tall actually make you a better basketball player?

The idea was straightforward. Take players in each position –Center, Forward, Guard – and compare the tallest ones with the rest. Is being taller an actual advantage in performance?

I started this analysis by creating a basic plan. Group players by position. Rank them by height. Compare the performance of the tallest players in each group against their shorter teammates. Simple enough.

 

What went wrong?

At least, it seemed simple.

I thought it would be a good idea to make this dynamic. Let users choose whether to compare the tallest 5, 10, 15 players and see how the results change. What happened next was several hours of fighting Power BI, writing and rewriting DAX, hitting constant errors, breaking context, and discovering how fragile the logic can get when dynamic grouping meets complicated relationships.

There were a few moments where I genuinely considered giving up on the entire idea. Nothing worked the way it should. The parameter broke. The rank broke. The filters broke. The labels broke. And so did my patience.

 

The Solution

Eventually, I made a decision that every analyst makes at some point. Forget fancy. Do what works.

I dropped the parameter idea completely. I focused on a clean, static split: the tallest 10 players in each position versus the rest. No slicers. No dynamic measures. No more fighting with the model. Just simple logic that holds.

This change didn’t just save my sanity. It actually made the dashboard easier to interpret.

 

The Findings

Height makes a difference, but only for certain things. Taller players block more and grab more rebounds, especially among centers and forwards. For guards, the difference is smaller but still there.

For points, the impact of height is inconsistent. Taller guards actually score more, but for forwards, the shorter players score slightly higher. Centers rely less on scoring either way.

When it comes to assists and steals, being shorter seems to be an advantage. Across all positions, the shortest players consistently outperform the tallest in those stats.

The scatterplots make this clear. Height strongly impacts rebounds and blocks but has little to no relationship with points, assists, or steals.

 

What Didn’t Work

I also tried to look at whether taller teams win more games. That analysis went nowhere. I plotted average team height against win percentage and got nothing but a wall of random dots. There was no pattern at all. I dropped that idea completely.

 

The Final Report

The final dashboard focuses entirely on player-level analysis. It shows the height distribution across positions and compares how the Tallest 10 players in each group perform compared to the rest. It also includes a scatterplot of individual players’ height versus their key stats, which makes it easy to explore whether height affects performance on an individual level.

 

 

The Conclusion

It’s pretty simple. Height matters, but only when it matters. If you’re trying to rebound or block shots, being tall helps. If you’re trying to score or assist, it doesn’t matter as much.

This project also reminded me of something important about data work. Not every problem needs a dynamic, complex solution. Sometimes, the simplest solution is not just the easiest it’s the best.

 

Share this post

Dashboard Week. Day 2.

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

Battle of the Giants: Does Size Really Matter?

 

I know absolutely nothing about basketball. All I really know is that basketball players are tall. So, when I got access to the NCAA dataset, that was the first thing that caught my attention. I wanted to answer a very simple question: does being tall actually make you a better basketball player?

The idea was straightforward. Take players in each position –Center, Forward, Guard – and compare the tallest ones with the rest. Is being taller an actual advantage in performance?

I started this analysis by creating a basic plan. Group players by position. Rank them by height. Compare the performance of the tallest players in each group against their shorter teammates. Simple enough.

 

What went wrong?

At least, it seemed simple.

I thought it would be a good idea to make this dynamic. Let users choose whether to compare the tallest 5, 10, 15 players and see how the results change. What happened next was several hours of fighting Power BI, writing and rewriting DAX, hitting constant errors, breaking context, and discovering how fragile the logic can get when dynamic grouping meets complicated relationships.

There were a few moments where I genuinely considered giving up on the entire idea. Nothing worked the way it should. The parameter broke. The rank broke. The filters broke. The labels broke. And so did my patience.

 

The Solution

Eventually, I made a decision that every analyst makes at some point. Forget fancy. Do what works.

I dropped the parameter idea completely. I focused on a clean, static split: the tallest 10 players in each position versus the rest. No slicers. No dynamic measures. No more fighting with the model. Just simple logic that holds.

This change didn’t just save my sanity. It actually made the dashboard easier to interpret.

 

The Findings

Height makes a difference, but only for certain things. Taller players block more and grab more rebounds, especially among centers and forwards. For guards, the difference is smaller but still there.

For points, the impact of height is inconsistent. Taller guards actually score more, but for forwards, the shorter players score slightly higher. Centers rely less on scoring either way.

When it comes to assists and steals, being shorter seems to be an advantage. Across all positions, the shortest players consistently outperform the tallest in those stats.

The scatterplots make this clear. Height strongly impacts rebounds and blocks but has little to no relationship with points, assists, or steals.

 

What Didn’t Work

I also tried to look at whether taller teams win more games. That analysis went nowhere. I plotted average team height against win percentage and got nothing but a wall of random dots. There was no pattern at all. I dropped that idea completely.

 

The Final Report

The final dashboard focuses entirely on player-level analysis. It shows the height distribution across positions and compares how the Tallest 10 players in each group perform compared to the rest. It also includes a scatterplot of individual players’ height versus their key stats, which makes it easy to explore whether height affects performance on an individual level.

 

 

The Conclusion

It’s pretty simple. Height matters, but only when it matters. If you’re trying to rebound or block shots, being tall helps. If you’re trying to score or assist, it doesn’t matter as much.

This project also reminded me of something important about data work. Not every problem needs a dynamic, complex solution. Sometimes, the simplest solution is not just the easiest it’s the best.

 

Share this post

Dashboard Week. Day 2.

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

Battle of the Giants: Does Size Really Matter?

 

I know absolutely nothing about basketball. All I really know is that basketball players are tall. So, when I got access to the NCAA dataset, that was the first thing that caught my attention. I wanted to answer a very simple question: does being tall actually make you a better basketball player?

The idea was straightforward. Take players in each position –Center, Forward, Guard – and compare the tallest ones with the rest. Is being taller an actual advantage in performance?

I started this analysis by creating a basic plan. Group players by position. Rank them by height. Compare the performance of the tallest players in each group against their shorter teammates. Simple enough.

 

What went wrong?

At least, it seemed simple.

I thought it would be a good idea to make this dynamic. Let users choose whether to compare the tallest 5, 10, 15 players and see how the results change. What happened next was several hours of fighting Power BI, writing and rewriting DAX, hitting constant errors, breaking context, and discovering how fragile the logic can get when dynamic grouping meets complicated relationships.

There were a few moments where I genuinely considered giving up on the entire idea. Nothing worked the way it should. The parameter broke. The rank broke. The filters broke. The labels broke. And so did my patience.

 

The Solution

Eventually, I made a decision that every analyst makes at some point. Forget fancy. Do what works.

I dropped the parameter idea completely. I focused on a clean, static split: the tallest 10 players in each position versus the rest. No slicers. No dynamic measures. No more fighting with the model. Just simple logic that holds.

This change didn’t just save my sanity. It actually made the dashboard easier to interpret.

 

The Findings

Height makes a difference, but only for certain things. Taller players block more and grab more rebounds, especially among centers and forwards. For guards, the difference is smaller but still there.

For points, the impact of height is inconsistent. Taller guards actually score more, but for forwards, the shorter players score slightly higher. Centers rely less on scoring either way.

When it comes to assists and steals, being shorter seems to be an advantage. Across all positions, the shortest players consistently outperform the tallest in those stats.

The scatterplots make this clear. Height strongly impacts rebounds and blocks but has little to no relationship with points, assists, or steals.

 

What Didn’t Work

I also tried to look at whether taller teams win more games. That analysis went nowhere. I plotted average team height against win percentage and got nothing but a wall of random dots. There was no pattern at all. I dropped that idea completely.

 

The Final Report

The final dashboard focuses entirely on player-level analysis. It shows the height distribution across positions and compares how the Tallest 10 players in each group perform compared to the rest. It also includes a scatterplot of individual players’ height versus their key stats, which makes it easy to explore whether height affects performance on an individual level.

 

 

The Conclusion

It’s pretty simple. Height matters, but only when it matters. If you’re trying to rebound or block shots, being tall helps. If you’re trying to score or assist, it doesn’t matter as much.

This project also reminded me of something important about data work. Not every problem needs a dynamic, complex solution. Sometimes, the simplest solution is not just the easiest it’s the best.

 

Share this post

Dashboard Week. Day 2.

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

Battle of the Giants: Does Size Really Matter?

 

I know absolutely nothing about basketball. All I really know is that basketball players are tall. So, when I got access to the NCAA dataset, that was the first thing that caught my attention. I wanted to answer a very simple question: does being tall actually make you a better basketball player?

The idea was straightforward. Take players in each position –Center, Forward, Guard – and compare the tallest ones with the rest. Is being taller an actual advantage in performance?

I started this analysis by creating a basic plan. Group players by position. Rank them by height. Compare the performance of the tallest players in each group against their shorter teammates. Simple enough.

 

What went wrong?

At least, it seemed simple.

I thought it would be a good idea to make this dynamic. Let users choose whether to compare the tallest 5, 10, 15 players and see how the results change. What happened next was several hours of fighting Power BI, writing and rewriting DAX, hitting constant errors, breaking context, and discovering how fragile the logic can get when dynamic grouping meets complicated relationships.

There were a few moments where I genuinely considered giving up on the entire idea. Nothing worked the way it should. The parameter broke. The rank broke. The filters broke. The labels broke. And so did my patience.

 

The Solution

Eventually, I made a decision that every analyst makes at some point. Forget fancy. Do what works.

I dropped the parameter idea completely. I focused on a clean, static split: the tallest 10 players in each position versus the rest. No slicers. No dynamic measures. No more fighting with the model. Just simple logic that holds.

This change didn’t just save my sanity. It actually made the dashboard easier to interpret.

 

The Findings

Height makes a difference, but only for certain things. Taller players block more and grab more rebounds, especially among centers and forwards. For guards, the difference is smaller but still there.

For points, the impact of height is inconsistent. Taller guards actually score more, but for forwards, the shorter players score slightly higher. Centers rely less on scoring either way.

When it comes to assists and steals, being shorter seems to be an advantage. Across all positions, the shortest players consistently outperform the tallest in those stats.

The scatterplots make this clear. Height strongly impacts rebounds and blocks but has little to no relationship with points, assists, or steals.

 

What Didn’t Work

I also tried to look at whether taller teams win more games. That analysis went nowhere. I plotted average team height against win percentage and got nothing but a wall of random dots. There was no pattern at all. I dropped that idea completely.

 

The Final Report

The final dashboard focuses entirely on player-level analysis. It shows the height distribution across positions and compares how the Tallest 10 players in each group perform compared to the rest. It also includes a scatterplot of individual players’ height versus their key stats, which makes it easy to explore whether height affects performance on an individual level.

 

 

The Conclusion

It’s pretty simple. Height matters, but only when it matters. If you’re trying to rebound or block shots, being tall helps. If you’re trying to score or assist, it doesn’t matter as much.

This project also reminded me of something important about data work. Not every problem needs a dynamic, complex solution. Sometimes, the simplest solution is not just the easiest it’s the best.

 

Share this post

Dashboard Week. Day 2.

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

Battle of the Giants: Does Size Really Matter?

 

I know absolutely nothing about basketball. All I really know is that basketball players are tall. So, when I got access to the NCAA dataset, that was the first thing that caught my attention. I wanted to answer a very simple question: does being tall actually make you a better basketball player?

The idea was straightforward. Take players in each position –Center, Forward, Guard – and compare the tallest ones with the rest. Is being taller an actual advantage in performance?

I started this analysis by creating a basic plan. Group players by position. Rank them by height. Compare the performance of the tallest players in each group against their shorter teammates. Simple enough.

 

What went wrong?

At least, it seemed simple.

I thought it would be a good idea to make this dynamic. Let users choose whether to compare the tallest 5, 10, 15 players and see how the results change. What happened next was several hours of fighting Power BI, writing and rewriting DAX, hitting constant errors, breaking context, and discovering how fragile the logic can get when dynamic grouping meets complicated relationships.

There were a few moments where I genuinely considered giving up on the entire idea. Nothing worked the way it should. The parameter broke. The rank broke. The filters broke. The labels broke. And so did my patience.

 

The Solution

Eventually, I made a decision that every analyst makes at some point. Forget fancy. Do what works.

I dropped the parameter idea completely. I focused on a clean, static split: the tallest 10 players in each position versus the rest. No slicers. No dynamic measures. No more fighting with the model. Just simple logic that holds.

This change didn’t just save my sanity. It actually made the dashboard easier to interpret.

 

The Findings

Height makes a difference, but only for certain things. Taller players block more and grab more rebounds, especially among centers and forwards. For guards, the difference is smaller but still there.

For points, the impact of height is inconsistent. Taller guards actually score more, but for forwards, the shorter players score slightly higher. Centers rely less on scoring either way.

When it comes to assists and steals, being shorter seems to be an advantage. Across all positions, the shortest players consistently outperform the tallest in those stats.

The scatterplots make this clear. Height strongly impacts rebounds and blocks but has little to no relationship with points, assists, or steals.

 

What Didn’t Work

I also tried to look at whether taller teams win more games. That analysis went nowhere. I plotted average team height against win percentage and got nothing but a wall of random dots. There was no pattern at all. I dropped that idea completely.

 

The Final Report

The final dashboard focuses entirely on player-level analysis. It shows the height distribution across positions and compares how the Tallest 10 players in each group perform compared to the rest. It also includes a scatterplot of individual players’ height versus their key stats, which makes it easy to explore whether height affects performance on an individual level.

 

 

The Conclusion

It’s pretty simple. Height matters, but only when it matters. If you’re trying to rebound or block shots, being tall helps. If you’re trying to score or assist, it doesn’t matter as much.

This project also reminded me of something important about data work. Not every problem needs a dynamic, complex solution. Sometimes, the simplest solution is not just the easiest it’s the best.

 

Share this post

Dashboard Week. Day 2.

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

Battle of the Giants: Does Size Really Matter?

 

I know absolutely nothing about basketball. All I really know is that basketball players are tall. So, when I got access to the NCAA dataset, that was the first thing that caught my attention. I wanted to answer a very simple question: does being tall actually make you a better basketball player?

The idea was straightforward. Take players in each position –Center, Forward, Guard – and compare the tallest ones with the rest. Is being taller an actual advantage in performance?

I started this analysis by creating a basic plan. Group players by position. Rank them by height. Compare the performance of the tallest players in each group against their shorter teammates. Simple enough.

 

What went wrong?

At least, it seemed simple.

I thought it would be a good idea to make this dynamic. Let users choose whether to compare the tallest 5, 10, 15 players and see how the results change. What happened next was several hours of fighting Power BI, writing and rewriting DAX, hitting constant errors, breaking context, and discovering how fragile the logic can get when dynamic grouping meets complicated relationships.

There were a few moments where I genuinely considered giving up on the entire idea. Nothing worked the way it should. The parameter broke. The rank broke. The filters broke. The labels broke. And so did my patience.

 

The Solution

Eventually, I made a decision that every analyst makes at some point. Forget fancy. Do what works.

I dropped the parameter idea completely. I focused on a clean, static split: the tallest 10 players in each position versus the rest. No slicers. No dynamic measures. No more fighting with the model. Just simple logic that holds.

This change didn’t just save my sanity. It actually made the dashboard easier to interpret.

 

The Findings

Height makes a difference, but only for certain things. Taller players block more and grab more rebounds, especially among centers and forwards. For guards, the difference is smaller but still there.

For points, the impact of height is inconsistent. Taller guards actually score more, but for forwards, the shorter players score slightly higher. Centers rely less on scoring either way.

When it comes to assists and steals, being shorter seems to be an advantage. Across all positions, the shortest players consistently outperform the tallest in those stats.

The scatterplots make this clear. Height strongly impacts rebounds and blocks but has little to no relationship with points, assists, or steals.

 

What Didn’t Work

I also tried to look at whether taller teams win more games. That analysis went nowhere. I plotted average team height against win percentage and got nothing but a wall of random dots. There was no pattern at all. I dropped that idea completely.

 

The Final Report

The final dashboard focuses entirely on player-level analysis. It shows the height distribution across positions and compares how the Tallest 10 players in each group perform compared to the rest. It also includes a scatterplot of individual players’ height versus their key stats, which makes it easy to explore whether height affects performance on an individual level.

 

 

The Conclusion

It’s pretty simple. Height matters, but only when it matters. If you’re trying to rebound or block shots, being tall helps. If you’re trying to score or assist, it doesn’t matter as much.

This project also reminded me of something important about data work. Not every problem needs a dynamic, complex solution. Sometimes, the simplest solution is not just the easiest it’s the best.

 

Share this post