Pierre-Simon Laplace<p>🏈 NFL meets Bayesian stats!</p><p>In this episode Alex Andorra chats with Ron Yurko on</p><p>👉 Writing your own models<br>👉 Building a sports analytics portfolio<br>👉 Pitfalls of modelling expectations<br>👉 Using tracking data for player insights<br>👉 Causal thinking in football data</p><p>🎧 <a href="https://lnkd.in/gWz4v2JG" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">lnkd.in/gWz4v2JG</span><span class="invisible"></span></a></p><p><a href="https://mstdn.science/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bayesian</span></a> <a href="https://mstdn.science/tags/podcast" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>podcast</span></a> <a href="https://mstdn.science/tags/learningbayesianstatistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>learningbayesianstatistics</span></a> <a href="https://mstdn.science/tags/SportsAnalytics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SportsAnalytics</span></a> <a href="https://mstdn.science/tags/NFL" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NFL</span></a> <a href="https://mstdn.science/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a></p>