Integrating video & data in sports analytics | Arielle Dror | Data Science Hangout

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We were recently joined by Arielle Dror, Director of Data and Analytics at Bay FC, a team in the US National Women's Soccer League (NWSL), to chat about working out loud, integrating video with data analysis, technology they use in sports analytics, and quantifying intangible player traits. Oh, and ICE CREAM!

In this Hangout, Arielle discusses how she works to integrate data into Bay FC’s decision-making processes, including recruitment, tactical analysis, and game preparation. She uses tools like Quarto and Posit Connect to automate weekly match reports. To enhance understanding among non-technical staff, Ariel’s team also uses proprietary sports software (Sportscode) to build dashboards on top of timestamped game footage with specific events tagged in it. This allows end-users to click on specific data points, such as those related to chance creation, and immediately view the corresponding video play that demonstrates the data's meaning. This visual context is essential for translating data results to coaches.

Resources mentioned in the video and zoom chat:
🔗 R and AI Conference → https://rconsortium.github.io/RplusAI...
🔗 Bay FC Data Video on LinkedIn → https://www.linkedin.com/posts/weareb...

If you didn’t join live, one great discussion you missed from the zoom chat was about the "passion penalty". Attendees discussed whether working in a field you love, such as sports, typically comes with lower pay than other industries, especially given the high supply of passionate people who want to work in the space. Do you think the passion penalty exists? 🤔

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Timestamps
00:00 Introduction
03:19 "What traits in players do you wish you were able to quantify?"
06:48 "How is the data science work implemented on the field?"
10:50 "How do you translate results to coaches?"
12:44 "What tools do you wish you had access to?"
18:27 "Tell us about how you began working out loud and what that led to."
23:26 "Why is it so hard to recruit for sports?"
24:31 "Do you feel like there's a passion penalty for working in the sports world?"
28:31 "Are you working with PyTorch, scikit-learn, or OpenCV computer vision?"
30:28 "Do you ever correct anyone and tell them it's football, not soccer? Is the same set of data available for everyone?"
34:08 "Were there any sporting myths you've been able to analyze, like home ground advantage?"
37:28 "What's the best way to break into sports data roles?"
40:11 "Are athletes willing to share personal health data from wearables?"
42:06 "Is there commercially viable technology for 3D modeling movements?"
43:43 "How do you handle scenarios where play on the field contradicts data predictions?"
45:33 "How did you pick the analytics stack you use?"
47:41 "Do you have any advice for new people learning data science skills?"
48:44 "Is there a danger players will optimize individual statistics over team performance?"
50:22 "Is there a 'Believe' sign in the locker room?"
50:59 "What's the most important thing you learned about communicating analytics to stakeholders?"