The hottest Analytics Substack posts right now

And their main takeaways
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Top Technology Topics

The NFL has entered the Scorigami Era

Silver Bulletin • 334 implied HN points • 14 Nov 25
🎾 Sports Analytics
  1. The NFL is scoring more points than ever, leading to strange final scores that were rare before. Think of scores now being like high school locker combinations rather than traditional ones.
  2. New strategies for 4th down plays are changing how teams approach scoring. Teams are going for it more often, which is helping them build longer drives and creative scoring.
  3. Kicking has become easier with new rules, allowing for higher accuracy and longer field goals. Fans are seeing kickers make amazing plays that impact the game's outcome.

Which brands are sponsoring newsletters?

Simon Owens's Media Newsletter • 199 implied HN points • 08 Dec 25
💼 Business Analytics
  1. Many brands are starting to sponsor newsletters, showing they see value in this advertising method.
  2. Tracking newsletter sponsorships can help publishers understand which brands are actively spending money on ads.
  3. It's helpful for anyone looking to sell sponsorships to know which brands have already advertised in other newsletters.

A view from the Pinnacle

networked • 143 implied HN points • 22 Dec 25
🎾 Sports Analytics
  1. Pinnacle has long been the sharpest football bookmaker, so using its odds as a baseline lets bettors spot expected-value edges by taking better prices at softer bookies.
  2. Since about 2023, Pinnacle’s closing odds have become less reliable and produced lower-than-expected returns, which could be down to randomness, arbitrage-driven moves, or a decline in their model accuracy.
  3. Prediction markets offer low house take and no bans so they attract sharps, but limited liquidity and wider spreads create an effective vig and stake limits, meaning Pinnacle’s deep liquidity and high limits still keep it relevant.

Which way from here?

benn.substack • 1048 implied HN points • 06 Jun 25
🕹 Technology Analytics
  1. Data tools are getting more advanced, but many people still struggle with knowing how to use them effectively. This means that having the right tools isn't enough if users lack direction.
  2. The industry is shifting focus from traditional analytics towards building AI systems and infrastructure. Companies are now adapting their technologies to support AI applications instead of just analyzing data.
  3. Self-serve BI tools aren't being used as intended because people often don't know what questions to ask. Providing clearer direction and goals might help users make better use of available data.

Do we need the Lakehouse architecture?

VuTrinh. • 399 implied HN points • 20 Apr 24
🕹 Technology Analytics
  1. Lakehouse architecture combines the strengths of data lakes and data warehouses. It aims to solve the problems that arise from keeping these two systems separate.
  2. This new approach allows for better data management, including features like ACID transactions and efficient querying of big datasets. It enables real-time analytics on raw data without needing complex data movements.
  3. With the help of technologies like Delta Lake and similar systems, the Lakehouse can handle both structured and unstructured data efficiently, making it a promising solution for modern data needs.
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Data Science Weekly - Issue 527

Data Science Weekly Newsletter • 959 implied HN points • 29 Dec 23
🕹 Technology Analytics
  1. This week, there's a focus on using data science techniques for practical decision-making, highlighted by an interview with Steven Levitt, who discusses making tough choices using data.
  2. There's a roundup of AI developments from 2023, showing how the field has evolved over the past year, which can help professionals stay updated.
  3. Understanding data quality is essential, as it directly impacts how useful data is for decision-making and analysis in any organization.

How to Escape the Ad-Hoc Data Request Trap - 4 Mindset Shifts From a Former Director Of Analytics

SeattleDataGuy’s Newsletter • 659 implied HN points • 25 Jul 25
💼 Business Analytics
  1. Data teams should move from being reactive to proactive. This means instead of just answering requests, they should focus on setting goals that help the business grow.
  2. Being reactive makes it hard for data teams to have real influence. When they just respond to requests, they miss out on adding value to the business strategy.
  3. To break free from the reactive cycle, data teams need to care about the overall business outcomes, not just individual requests. This way, they can better support strategic initiatives.

Olympics, AI, and some BI

HyperArc • 59 implied HN points • 05 Aug 24
🕹 Technology Analytics
  1. AI can help us learn about the Olympics and analyze different aspects, like who won medals and their physical attributes. It starts with basic questions and gets more complicated over time.
  2. While AI is good at remembering information and summarizing it, it struggles with reasoning about things it hasn't seen before. This means it can't always come up with new insights without the right data.
  3. For businesses, using AI with their private data can lead to smarter insights and faster decisions. It's important to combine human knowledge with AI to make the best use of available information.

Issue #8 - Deliver on the Data Needs, not the Data Desires

The Data Ecosystem • 199 implied HN points • 02 Jun 24
💼 Business Analytics
  1. It's important to focus on what the business truly needs from data, not just what they think they want. Conversations should help uncover real goals and challenges.
  2. Data projects often fail because teams don't ask the right questions or fully understand the business context. Engaging stakeholders regularly is key to success.
  3. A clear step-by-step process helps develop effective data solutions. Start with building a strong data foundation before moving on to more complex analytics.

Is Substack exaggerating its network effects?

escape the algorithm • 579 implied HN points • 06 Feb 24
💼 Business Analytics
  1. Substack's network effects might be exaggerated: Data shows that most new subscribers come from sources other than Substack.
  2. Subscriber growth on Substack may not solely be due to Substack's technology: Many readers find newsletters due to recommendations from other writers or external sources.
  3. The power of a newsletter audience lies more in the people than the platform: Leaving Substack might not drastically impact growth as much as anticipated.

Biotech Startup Stats

Rough Diamonds • 20 implied HN points • 05 Feb 26
💼 Business Analytics
  1. Most biotech startups either fail or lose value after IPO, with only a small share of currently trading firms showing positive long‑term returns; many poorly performing public companies may simply not have failed yet.
  2. Location and company age strongly predict outcomes: firms based in biotech hubs (CA, MA, NY, NJ, PA) do much better, and newer firms are more likely to still be trading due to lifecycle effects.
  3. Scientific focus and pipeline stage matter: biologics (especially antibodies), rare disease and immunology focuses, targets like PD‑1, and IPOing at Phase III are linked to acquisitions or positive returns, while "other" modalities (e.g., formulations, natural products) tend to underperform.

Is this a career?

benn.substack • 1713 implied HN points • 13 Dec 24
💼 Business Analytics
  1. Getting good at something often just takes a little focused effort over time. Many people don't actively try to improve, so they stay at a decent skill level rather than reaching their full potential.
  2. In fields like data analytics, it's essential to specialize to truly excel. Being a generalist might keep you busy, but it can lead to a career without a clear direction or growth.
  3. To stand out and achieve more in their careers, people need to identify a specific area of expertise and commit to it. Relying on being 'good at data' isn't usually enough to make a significant impact.

OpenAI Acquired Rockset

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 31 Jul 24
🕹 Technology Analytics
  1. OpenAI bought Rockset to make their data retrieval system better, which helps in using AI more effectively.
  2. The acquisition shows that LLMs are being seen more like a tool, and the focus is shifting to building useful applications using these technologies.
  3. Rockset's technology will help OpenAI work better with developers and make it easier to access and use real-time data for AI products.

Issue #7 - The Business Strategy, Where The Data Journey Starts

The Data Ecosystem • 179 implied HN points • 26 May 24
💼 Business Analytics
  1. A business strategy is the game plan for a company to reach its goals. It involves having a clear vision, mission, and set of goals to guide the organization.
  2. Good business strategies have defined components that everyone in the company knows. This helps avoid confusion and keeps everyone focused on the same objectives.
  3. Data plays a crucial role in shaping modern business strategies. Companies need to integrate data and analytics into their plans to make informed decisions and stay competitive.

Intro to SQL Indexes

Data Engineering Central • 589 implied HN points • 17 Jan 24
🕹 Technology Analytics
  1. Indexes are crucial for improving performance in SQL operations and data access.
  2. Clustered and non-clustered indexes are the two main types to understand in SQL indexing.
  3. Understanding use cases and query access patterns is key to designing effective indexes for data warehouses.

Issue #1 - We Need to Rethink Data

The Data Ecosystem • 259 implied HN points • 13 Apr 24
🕹 Technology Analytics
  1. The data industry is really complicated and often misunderstood. People usually talk about symptoms, like bad data quality, instead of getting to the real problems underneath.
  2. It's important to see the entire data ecosystem as connected, not just as separate parts. Understanding how these parts work together can help us find new opportunities and improve how we use data.
  3. This newsletter aims to break down complex data topics into simple ideas. It's like a cheat sheet for everything related to data, helping readers understand what each part is and why it matters.

GroupBy #41: Uber’s Batch Data Infrastructure with Google Cloud Platform

VuTrinh. • 99 implied HN points • 25 Jun 24
🕹 Technology Analytics
  1. Uber is moving its huge amount of data to Google Cloud to keep up with its growth. They want a smooth transition that won't disrupt current users.
  2. They are using existing technologies to make sure the change is easy. This includes tools that will help keep data safe and accessible during the move.
  3. Managing costs is a big concern for Uber. They plan to track and control spending carefully as they switch to cloud services.

Data Science Weekly - Issue 543

Data Science Weekly Newsletter • 219 implied HN points • 19 Apr 24
🕹 Technology Analytics
  1. Statistical ideas have a big impact on the world. Learning about important papers can help us understand how statistics shape modern research and decision-making.
  2. Machine Learning teams have different roles that face unique challenges. Understanding these personas can help leaders support their teams better.
  3. Using vector embeddings can greatly improve search experiences in apps. They simplify processes that previously seemed too complex and highlight their usefulness in technology.

Procella - The query engine at YouTube

VuTrinh. • 79 implied HN points • 29 Jun 24
🕹 Technology Analytics
  1. YouTube built Procella to combine different data processing needs into one powerful SQL query engine. This means they can handle many tasks, like analytics and reporting, without needing separate systems for each task.
  2. Procella is designed for high performance and scalability by keeping computing and storage separate. This makes it faster and more efficient, allowing for quick data access and analysis.
  3. The engine uses clever techniques to reduce delays and improve response times, even when many users are querying at once. It constantly optimizes and adapts, making sure users get their data as quickly as possible.

Indian IT’s GenAI Lag

Sector 6 | The Newsletter of AIM • 439 implied HN points • 14 Jan 24
🕹 Technology Analytics
  1. Indian IT companies like Infosys and TCS have shown strong financial performance, but they lack confidence in generating revenue from generative AI.
  2. In contrast, Accenture is making notable progress with generative AI, securing significant investments and showcasing strong growth.
  3. Many Indian IT firms are reducing new hiring and focusing more on training current employees, highlighting an emphasis on automation and upskilling rather than bringing on fresh talent.

Searching for insight

benn.substack • 1099 implied HN points • 29 Nov 24
💼 Business Analytics
  1. Many jobs in areas like think tanks or journalism are more about creating a background or illusion rather than producing real change or value. They serve as props for the more influential figures.
  2. There's a concern that as AI becomes capable of producing content, it might not be because it’s better, but because the original jobs might not have mattered as much as once thought.
  3. In analytics, there's a question of whether the insights businesses claim to offer are real or just part of the narrative they tell to appear competent and important.

Is It Time to Say Goodbye to Data Engineers?

SeattleDataGuy’s Newsletter • 812 implied HN points • 06 Feb 25
🕹 Technology Analytics
  1. Data engineers are often seen as roadblocks, but cutting them out can lead to major problems later on. Without them, the data can become messy and unmanageable.
  2. Initially, removing data engineers may seem like a win because things move quickly. However, this speed can cause chaos as data quality suffers and standards break down.
  3. A solid data strategy needs structure and governance. Rushing without proper planning can lead to a situation where everything collapses under the weight of disorganization.

Does data quality matter?

benn.substack • 1099 implied HN points • 22 Nov 24
🕹 Technology Analytics
  1. Data quality is important for making both strategic and operational decisions, as inaccurate data can lead to poor outcomes. Good data helps companies know what customers want and improve their services.
  2. AI models can tolerate some bad data better than traditional methods because they average out inaccuracies. This means these models might not break as easily if some of the input data isn’t perfect.
  3. Businesses now care more about AI than they used to about regular data reporting. This shift in focus might make data quality feel more important, even if it doesn’t technically impact AI model performance as much.

How the Kansas City Royals Are Using Stadium Geometry to Gain a Competitive Edge

Huddle Up • 26 implied HN points • 20 Jan 26
🎾 Sports Analytics
  1. They’re moving Kauffman Stadium’s outfield fences in about 10 feet to turn more fly balls into home runs, and analytics predict that change will add roughly 1.5 wins per season.
  2. The decision is driven by detailed stadium-geometry and wind modeling, led by advanced analytics (including work from a NASA-awarded computer scientist) and backed by financial projections showing the upgrade is cheap compared to its value.
  3. This is a cost-effective way for a small-market team to buy wins and revenue without big free-agent spending, and if it succeeds other clubs will likely copy the stadium-engineering approach.

Data Science Weekly - Issue 526

Data Science Weekly Newsletter • 419 implied HN points • 22 Dec 23
🕹 Technology Analytics
  1. Generative AI is changing how we work with tools, improving the Human-Tool Interface. This can help us use technology in ways we never could before.
  2. Support Vector Machines (SVMs) can be very effective for prediction tasks, often outperforming other models in error rates. However, they aren’t as commonly used, possibly due to their complexity.
  3. Deep multimodal fusion is useful in surgical training. It helps classify feedback from experienced surgeons to trainees by combining different types of data like text, audio, and video.

Championship Sunday was a strange watch for Vikings fans

Purple Insider • 294 implied HN points • 29 Jan 24
🎾 Sports Analytics
  1. Sunday's games were strange for Vikings fans to watch from a unique perspective.
  2. Building a championship team can involve having an all-time great quarterback, hitting on many draft picks, or building a strong supporting cast around an affordable quarterback.
  3. Success in the NFL requires making bold decisions and it's challenging to win even with a great team.

The buzz from radio row

Purple Insider • 255 implied HN points • 06 Feb 24
🎾 Sports Analytics
  1. Radio Row during the Super Bowl is chaotic with hundreds of hosts and guests wandering around.
  2. Analytics in NFL is gaining trust but there's no consensus on interpretation yet.
  3. Former players and hosts are discussing the use of analytics and its impact on decision-making in football.

Data: the new oil or a new byproduct?

Topsoil • 511 implied HN points • 30 Jun 23
🕹 Technology Analytics
  1. Data in agriculture is essential for advancements like Generative AI, automation, and precision agriculture.
  2. Challenges in farm digitization include issues like connectivity, interoperability, data quality, trust, and incentives.
  3. Farmers derive value from data through decision-making, enabling technologies, sharing with advisors, compliance, and future income opportunities.

Data Science Weekly - Issue 542

Data Science Weekly Newsletter • 139 implied HN points • 12 Apr 24
🕹 Technology Analytics
  1. This newsletter provides links and updates about data science, AI, and machine learning. It's a helpful resource for anyone wanting to stay informed in this field.
  2. One article teaches how to handle real questions using Python, which is great for people wanting practical coding skills. Another discusses techniques to make sure AI outputs stay on task.
  3. The newsletter also features resources and courses to help people learn and improve their skills in data science and related areas. It's a good place to find learning opportunities.

How To Measure Data Quality - Issue 185

Data Analysis Journal • 235 implied HN points • 07 Feb 24
🕹 Technology Analytics
  1. Data quality metrics are essential for measuring data governance and analytics success.
  2. There is no industry standard for defining poor-quality data; it varies based on context.
  3. Having specific KPIs for data quality is crucial to scale data governance initiatives and improve the state of data quality.

NFL Thoughts: A Contrarian Theory on Who the NFL's Best Player is and a Reader Dumps on Justin Herbert

House of Strauss • 23 implied HN points • 17 Jan 26
🎾 Sports Analytics
  1. Derek Stingley Jr. may be the NFL’s most impactful player because quarterbacks often avoid his side of the field. His coverage stats — a very low completion rate and tiny passer rating when targeted — suggest elite, game-changing disruption.
  2. Cornerbacks are widely undervalued and losing a starting corner can cause a big defensive drop-off, so truly elite corners can provide outsized team advantage. That lack of public and analytic attention may hide how important the position is.
  3. Justin Herbert is a very good quarterback but not clearly elite; repeated OC changes, persistent injuries, and middling advanced metrics indicate he hasn’t taken the next step. Blaming only the organization for his shortcomings is becoming a weaker explanation.

Issue #2 - The Data Ecosystem: Where do you even start?

The Data Ecosystem • 119 implied HN points • 21 Apr 24
🕹 Technology Analytics
  1. Data can be really complicated, and it's easy to miss how everything connects. People often focus on their own area and forget about the bigger picture of the data ecosystem.
  2. Chief Data Officers (CDOs) are important but can only do so much to fix data issues. They deal with many challenges, including limited power, lack of experience, and politics within the organization.
  3. To improve in the data field, we need to recognize the gaps in our knowledge, prioritize what to focus on, and continuously educate ourselves in both our own areas and related data domains.

Celebrating An Anniversary: Three Years of Writing About Analytics - Issue 154

Data Analysis Journal • 452 implied HN points • 26 Jul 23
🕹 Technology Analytics
  1. The author reflects on three years of writing a newsletter about analytics, thanking supporters and subscribers.
  2. The author's newsletter aims to document their journey, bridge the gap between academics and industry, and encourage classic data analysis.
  3. The author shares insights on their writing strategy, the power of being small and independent, and future plans for the newsletter.

From Average To Exceptional: 5 Skills To Help You Become The Data Expert Everyone Wants To Work With

SeattleDataGuy’s Newsletter • 494 implied HN points • 19 Feb 25
💼 Business Analytics
  1. Always focus on the real problem behind a request, not just what is being asked. This helps you deliver better solutions that actually meet the business needs.
  2. Using clear frameworks can help organize your thoughts and make complex investigations easier. A structured approach leads to clearer communication and better results.
  3. Keep your communication simple and focused on what matters to your stakeholders. This helps everyone stay on the same page and reduces confusion.

Stealing Signals, Week 8, Part 1

Stealing Signals • 439 implied HN points • 31 Oct 23
🎾 Sports Analytics
  1. Teams may not always give 100% effort every game in the NFL due to strategic reasons.
  2. Watching games can give a big advantage in fantasy football over just looking at stats.
  3. First-read targets dataset may not accurately reflect offensive intentions in play calling and should be analyzed cautiously.

GroupBy #39: 2000+ DBT models in airflow; Serverless Jupyter Notebooks at Meta

VuTrinh. • 59 implied HN points • 11 Jun 24
🕹 Technology Analytics
  1. Meta has developed a serverless Jupyter Notebook platform that runs directly in web browsers, making data analysis more accessible.
  2. Airflow is being used to manage over 2000 DBT models, which helps teams create and maintain their own data models effectively.
  3. Building a data platform from scratch can be a valuable learning experience, revealing important lessons about data structure and management.