Drew Boyd

How can we track the NBA playoff series as data professionals? By looking at 40 different advanced stats for each team and isolating stylistic differences that determine who gains the advantage throughout the series. Dallas has a stronger bench and depth that ranks 4th in the league in fewest turnovers. So OKC ranks 1st in steals (8.5/gm) but they can't capitalize on strong defense. Also they have less depth in their top stats (Blocks, 3pt FG% and several more advanced stats). Dallas won the series by outrebounding and spreading their shooting beyond only a couple players like OKC (having 82% of their scoring from their 5 primary players compared to just 74% from OCK's top 5). The series evolved to give OKC a dissadvantage when SGA wasn't on the floor from players like PJ Washington and Lively II giving Dallas a boost in their marginal advantages which helped them erase the 3P FG % gap by taking over 44% of all field goals per game during the season and extending this performance into the series to remove the advantage that OKC has against over 85% of the league. The analysis of the Minnesota Timberwolves against Dallas is even more stylistically contraversial which is why Dallas will win the series in 5 games!

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With summer winding down, beach volleyball season is coming to a close. 🏐 As a longtime fan of the AVP Pro Beach Volleyball, I decided to combine my love for the sport with my data science background to create a fun project: 𝗽𝗹𝗮𝘆𝗲𝗿 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 𝗼𝗻 𝘁𝗵𝗲 𝗰𝗼𝘂𝗿𝘁. This clip is from the 2024 AVP Manhattan Open, featuring Phil Dalhausser, a USA Olympian with 𝟲 𝗚𝗼𝗹𝗱 𝗠𝗲𝗱𝗮𝗹𝘀. The tracking (which is almost spot-on) maps out player movements on both sides of the court. The best part? 𝗧𝗵𝗲 𝗰𝗼𝗱𝗲 𝘄𝗼𝗿𝗸𝘀 𝘀𝗲𝗮𝗺𝗹𝗲𝘀𝘀𝗹𝘆 𝘄𝗶𝘁𝗵 𝗼𝘁𝗵𝗲𝗿 𝗴𝗮𝗺𝗲𝘀 𝘀𝗵𝗼𝘁 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗰𝗼𝘂𝗿𝘁 𝗮𝗻𝗴𝗹𝗲 𝗮𝗻𝗱 𝗱𝗶𝘀𝘁𝗮𝗻𝗰𝗲! 🔗 The code is up on GitHub: https://lnkd.in/dDNbASfD Inspired by AVP Pro Beach Volleyball and Balltime, I’m looking forward to more volleyball data projects ahead. Next up: Ball tracking! If you have any ideas or insights, let’s connect and collaborate! #DataScience #Volleyball #Python #YOLOv8 #ComputerVision

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If I had to start from 0 with Tableau here's what I'd do: (how I actually got noticed) → Put the majority of your learning time into your Tableau Public portfolio Here’s why: 1. **Instant Credibility**: Your Tableau Public portfolio acts as a live resume. Employers, clients, and peers can see your work, skills, and creativity in real time. No need to just talk about your expertise—you can **show** it.     2. **Community Recognition**: Tableau Public isn’t just a tool, it’s a community. Having high-quality visualizations can get you noticed, featured, and shared. This exposure can lead to new opportunities and connections that may never have come your way otherwise.     3. **Constant Learning & Improvement**: A public profile keeps you motivated to innovate. Each new visualization is an opportunity to experiment, learn new techniques, and raise the bar. The feedback and inspiration from the community only push you further.     4. **Visibility to Opportunities**: Recruiters, companies, and collaborators use Tableau Public to scout talent. A standout profile could open doors to projects, roles, or collaborations that others simply won’t get access to. The trick is to find a topic from interesting data sources that you enjoy.  You'll stick to it longer. 1. makeovermonday 2. data.world 3. kaggle This is exactly how I have landed interest in my Tableau expertise. And how I've been recruited for multiple positions. Pick a data source, viz it and sell your skills on Tableau Public.

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Just submitted my creation, Arctic Analytics (https://lnkd.in/gVehD56b) to The Future of AI is Open hackathon conducted by #Snowflake and #Streamlit, and what a ride it's been! 🏁💥 Arctic Analytics a Streamlit app based on Snowflake's Arctic AI model, represents a leap toward bridging data privacy and analytics accessibility. It's not merely a tool but a statement - ethical AI use can empower anyone to unleash their data's potential, all the while maintaining stringent data privacy. Imagine drawing powerful insights and visuals without the AI ever peeking into your data, coupled with unmatched transparency about the data shared with the AI. 🛡️🤖 I am proud to have navigated this venture, merging technology with values. And this is merely the start. As submissions close and I await results on June 6, Arctic Analytics stands as a testament to what we can achieve at the intersection of privacy, accessibility, and innovative technology. 🔥🌐 Ready to usher in the future of AI with open arms and secure data? Join me in pushing tomorrow's boundaries today! 🌈⏳ #Hackathon #AI #Innovation #DataPrivacy #TheFutureOfAIIsOpen #SnowflakeArctic #ArcticAnalytics

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Two weeks ago I shared my latest baseball analytics project, which is a framework for evaluating pitching change decision making in Major League Baseball. Since then, I have been running the decisions from each playoff game through the framework and showcasing the output in the dashboard that I created. If you're a fan of baseball and are curious how various decisions have been graded, check them out here! Also note that I will be doing this throughout the postseason, so follow along if you're so inclined. If you'd like to learn more about what is going on in the backend or how to use the dashboard, then click on the "Dashboard Information" tab and there you can find links to two blog posts that will walk you through everything. Link to dashboard: https://lnkd.in/gG89YHqP

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The #tableau Workout Wednesday this week explores a couple of ways you can make your dashboards show user specific data. We have explored this in the past on a couple side projects but haven't applied it generally. This is definitely something we will be implementing for the reporting my team will be delivering moving forward. #wow2024 #datafam #dataviz https://lnkd.in/gnBzrYP8

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One Year at Zelus Analytics My job is fun and my colleagues are awesome to be around. Before I landed at Zelus, I was looking for places where I could learn and grow, would be surrounded by experts, and work would be enjoyable (including work/life balance). I found all three! It's not all fun and games. The team is actively building best-in-class capabilities to provide competitive advantage to professional sports teams. That pursuit comes with stress and deadlines, but out of it comes deeper understanding of sport and respect for diversity of thought. My work at Zelus is mostly focused on estimating individual player skill and projecting estimates into the future. We have a robust model statistical model and I've been using and extending it. In the past year, I've seen data from baseball, basketball, football, hockey, and esports. Things I'm learning: * nuances of different sports, sports data, what makes good metrics * time series assumptions that hold (and don't) * parts of linear algebra and optimization that I missed Things I'm bringing to the table: * statistical modeling, understanding posterior geometry, Stan expertise * efficient computation, general problem-solving / programming skills * basketball knowledge; who knew that score keeping and coaching in my youth would pay off? I'm looking forward to the next year and building with the team.

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🚀 New Blog Alert: Understanding regression metrics can be confusing sometimes, but it’s essential if you want to evaluate the performance of your models accurately. In my latest post, I break down the most important metrics in simple terms: 🔸 MAE (Mean Absolute Error) – A straightforward way to understand the average prediction error. 🔸 MSE (Mean Squared Error) – Helps you catch larger errors by squaring the differences. 🔸 RMSE (Root Mean Squared Error) – Gives you error in the same units as your target variable, making it easier to interpret. 🔸 R² (R-Squared) – Want to know how well your model fits the data? This is the metric for you! Whether you’re just getting started with regression models or looking to deepen your knowledge, this guide will help you navigate these metrics with clarity. Check it out and feel free to leave your thoughts! 💬👇 #MachineLearning #DataScience #RegressionMetrics #AI #MAE #RMSE #R²

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Inspired by the #2024Olympics, I just wrapped up a little project that combines two things I love: volleyball and data science. I put together a Python script called VolleyForm AI. It’s a simple tool that uses AI to help "spot check" my volleyball skills by analyzing my form for passing, setting, attacking, and blocking. 🏐 Here’s what it does: 𝗣𝗮𝘀𝘀𝗶𝗻𝗴: Detects if your arms are too high or if your elbows aren't straight. 𝗦𝗲𝘁𝘁𝗶𝗻𝗴: Checks if your elbows are too close or far apart and if your hands are closed. 𝗛𝗶𝘁𝘁𝗶𝗻𝗴: Evaluates your hitting elbow’s position and whether your guide arm is in the right spot. 𝗕𝗹𝗼𝗰𝗸𝗶𝗻𝗴: Makes sure your hands are open when blocking. 🔗 If you want to try it yourself, the code is up on GitHub: https://lnkd.in/dYaxTSBm Would love to hear what you think or any ideas for improving it! #Python #Volleyball #DataScience #Olympics

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I've been developing my probabilistic modeling capabilities this year at work, so in my free time I thought it'd be a fun exercise to write an article about modeling MLB win probabilities using a Bayesian Hierarchical model with Stan. If this interests you, then give it a whirl! If not, then just pretend to read it and tell me I did great!! https://lnkd.in/gbwvJDxw #Stan #BayesianStatistics

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Exciting News, LinkedIn Family! I just published another article to my snowflake series: "An Interactive Guide to Snowflake Stages and Storage Integration". In this article, I dive deep into: 1. The nuances of Snowflake Stages and their role in streamlining data movement 2. How to leverage COPY commands for bulk loading and the power of Snowpipe for continuous data ingestion 3. Practical examples of integrating storage solutions with Snowflake for optimal performance But wait, it’s more than just theory! I’ve included interactive examples and code snippets to help you apply these concepts in real time! Curious to enhance your Snowflake skills? Check it out and let me know what you think! #SnowflakeData #DataEngineering #CloudComputing #ETL #DataLoading #DataOptimization #RealTimeData #Snowpipe #CloudData #BigData

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🚀 Excited to share my latest data visualization challenge entry using Tableau! 🎨📊 This Labor Day weekend, I am taking the 2024 - Week 17 Back 2 Viz Basics challenged and I am diving into the world of TV series with a dataset from Tableau's brand new collaboration with IMDb, Data + TV! I focused on the top 2,500 rated series that received at least 10K IMDb votes, exploring how we can compare series based on their total seasons and IMDb ratings using a scatter plot. 📺 What did I uncover?  Using Tableau, I created a scatter plot to visually compare TV series based on their total seasons, starting year and IMDb ratings. This approach allowed me to spot trends and outliers in how long-running series fared in terms of audience ratings. The visualization effectively uses parameter actions, color and formatting to highlight key insights, making it easy to understand at a glance. 🔍 Why a scatter plot?  Scatter plots are a powerful way to compare categorical variables (like TV series names) by two continuous variables. They’re perfect for spotting patterns or anomalies in the data, and in this case, helped me understand the relationship between the number of seasons a show runs and how it's rated by audiences. Check out my final dashboard on Tableau Public here: https://lnkd.in/gKqRXh3b , and let me know your thoughts! 👉 Interested in the full dataset?  Explore more about the Data + TV project and see the dataset for yourself here: https://lnkd.in/gft6s7sa Eric Balash Elisa Davis Shreya Arya #DataPlusTV #B2VB #DataVisualization #Tableau #IMDB #TVSeries #Analytics

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Had fun playing around with some of the newer Tableau features. Specifically, I was working with the dashed lines introduced a while back and learned a couple of tricks with regards to labeling the endpoints of this viz. https://lnkd.in/gXxMru38 #datafam #MakeoverMonday #dataviz

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There’s too much noise about landing Data Analytics jobs. (Read this if you want to break into data analytics.) Over the past year, my DMs have been filled with questions like: “What skills do I actually need?” “How do I start?” “What are recruiters looking for?” I’ve been in your shoes—confused, overwhelmed, and unsure of where to begin. But through my journey to becoming a Senior Data scientist. ↳ I figured out what works, (more importantly, what doesn’t.) Now, I want to make it easier for you. That’s why Rishabh Dhingra, Sahil Gogna and I are hosting a Data Analytics  Workshop. Here’s what we’ll cover: - What recruiters care about most (insider industry insights) - How to get started (the exact roadmap you can follow) - How to build a resume that lands interviews - What is a data mindset/analytical mindset Make sure you don’t miss it. Join us at YWCA Toronto, on November 24th (Time - 10:30 AM to 01:00 PM.) Sign up here: https://lnkd.in/gXTgjZwC P.S. Full event details are in the pinned comment!

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Turnover can either derail a project or be just a bump in the road. Losing critical knowledge is expensive—but it doesn’t have to be. With the right documentation practices, high turnover becomes manageable, not disastrous. Here’s my approach to handling turnover as a Data Scientist: 𝗣𝗠𝘀/𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗟𝗲𝗮𝗱𝘀: Capture the big picture—why the project matters, key deadlines, expected outcomes, and the essential stakeholders. Ensure every detail is documented so the project doesn’t stall when someone exits. 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀: Document pipelines, key tables, runtimes, and critical Airflow links. This allows anyone stepping in to pick up exactly where things left off. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀/𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀: Create a culture of documentation. Ensure data processes, methodologies, and insights are organized and accessible, so no knowledge is lost when team members move on. Being a Data Scientist isn’t just about crunching numbers—it's about being the safety net that keeps knowledge intact when your team changes. Technical skills matter, but adaptability and documentation make you indispensable. 👉 How do you handle turnover in your teams? #DataScience #Turnover #Documentation

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Week 3 at CUNY Tech Prep: I explored Tableau vs. Jupyter Notebooks. Tableau's data management was insightful, but I prefer Jupyter, possibly due to my Python background. I am looking forward to working more with Tableau, but for right now, I will stick to Jupyter Notebooks. #DataAnalysis #TechSkills #CUNYTechPrep

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Brandon Traditi just built one of the coolest capstone projects I've ever seen. 🏈 It's a Tableau dashboard displaying NFL betting data in a clear, concise way. He used a: - line chart - point map - scatter plot - stacked bar chart And honestly, it's a work of art 🖼️ If you're an aspiring analyst, save this for inspiration! And reshare this post so we can help Brandon get seen. If you're a recruiter or hiring manager, consider reaching out to Brandon Traditi as he'd make a great addition to your team. ---- Brandon made this dashboard as part of The Data Analytics Accelerator Bootcamp. If you want to learn more, click the "View my website" at the top of this post.

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I see posts highlighting SMOTE for class-imbalance problems on social media every few weeks. Recently, I even saw that an agent claimed to be fully autonomous at a "Kaggle Grandmaster level", suggested running SMOTE at the beginning of a solution. It’s so heavily mentioned that it is ending up corrupting the training data of agentic frameworks. 😅 Have the people making this suggestion ever tried using class-weights or specialized implementations like Focal Loss? I see that many of the people using this approach under-over sampling the data first and then splitting it into train and validation sets, causing data leakage. Please try to solve any imbalance by making improvements to features and parameters without distorting the data.

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🚩 We Don’t Gatekeep Here. Free Data Science, Analytics, and Engineering Books 📍Over the years, I’ve built quite a collection of books. Some I bought, some were passed down to me by my mentor, and others were thoughtful gifts. These books have been my go-to references countless times, and now, as part of this amazing community, I want to share some of them with you. 📍But don’t just collect them, make sure you actually read them or refer back to them when you need to. 📍Instead of dropping your email in the comments, please fill out the form, the link is in the comment section and I’ll send a few your way. 🚩One thing to note: I’ve got a mix of Data Science, Analytics, and Engineering books, so I’ll send them as they are, no time to sort them out right now. ♻️ Report to a fellow bookworm #dataliving #favouribude

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