Deploying to main from @ numpy/numpy.org@3dae42b 🚀

Author: InessaPawson

case-studies/cricket-analytics/index.html

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 with a generative or static model.
 <a href=https://amplitude.com/blog/2017/01/19/causation-correlation>Causal analysis</a>
 and <a href=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/>big data approaches</a>
-are used for tactical analysis.</p></li><li><p><strong>Data Visualization:</strong> Data graphing and <a href=https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b>visualization</a> provide useful insights into relationship between various datasets.</p></li></ul><h2 id=summary>Summary<a class=headerlink href=#summary title="Link to this heading">#</a></h2><p>Sports Analytics is a game changer when it comes to how professional games are
+are used for tactical analysis.</p></li><li><p><strong>Data Visualization:</strong> Data graphing and visualization provide useful insights into relationship between various datasets.</p></li></ul><h2 id=summary>Summary<a class=headerlink href=#summary title="Link to this heading">#</a></h2><p>Sports Analytics is a game changer when it comes to how professional games are
 played, especially how strategic decision making happens, which until recently
 was primarily done based on “gut feeling" or adherence to past traditions. NumPy
 forms a solid foundation for a large set of Python packages which provide higher