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Update analyze-baseball-stats-with-pandas-and-matplotlib.mdx
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projects/analyze-baseball-stats-with-pandas-and-matplotlib/analyze-baseball-stats-with-pandas-and-matplotlib.mdx

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@@ -296,7 +296,7 @@ Now that we've explored some basic data analysis and visualization, let's put ou
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Finally, let's take on a challenge of replicating the work Billy Bean and Peter Brand did for the Oakland A's in _Moneyball_. While they almost certainly considered many statistics, they are most famous for finding players with a high **on-base percentage** (OBP) relative to their cost.
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<iframe width="672" height="378" src="https://www.youtube.com/embed/2ar61DyL4Xo?si=34_O3lw_RJ0_8aMs" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
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<iframe width="100%" src="https://www.youtube.com/embed/2ar61DyL4Xo?si=34_O3lw_RJ0_8aMs" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
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Let's try to identify some of those players! Here's how we'll tackle this problem:
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