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

Author: sonnynomnom

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

@@ -296,6 +296,8 @@ Now that we've explored some basic data analysis and visualization, let's put ou
 
 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.
 
+<iframe width="560" height="315" src="https://www.youtube.com/embed/Tzin1DgexlE?si=W2u_dt9ctz5CZnKY" 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>
+
 Let's try to identify some of those players! Here's how we'll tackle this problem:
 
 - OBP is not currently listed in the Batting table, but we can calculate that value ourselves based on other information and add a new OBP column to the table.
@@ -304,7 +306,9 @@ Let's try to identify some of those players! Here's how we'll tackle this proble
 
 ### Calculating OBP
 
-Let's begin by calculating OBP. This is the formula that we'll use. It takes into account not just hits, but walks, sacrifice flies, etc.
+Let's begin by calculating OBP. 
+
+This is the formula that we'll use. It takes into account not just hits, but walks, sacrifice flies, etc.
 
 $$
 OBP = {H + BB + HBP \over AB + BB + HBP + SF}