The hottest Analytics Substack posts right now

And their main takeaways
Category
Top Technology Topics

New on Substack: draft Notes, hide revenue stats, pin multiple posts, and more

Substack Blog • 1920 implied HN points • 09 Mar 26
🕹 Technology Analytics
  1. Drafting and homepage control got simpler: you can save Notes as drafts, pin multiple posts to your homepage, and adjust text alignment so your work looks and lands how you want.
  2. Dashboard and analytics give you more control: you can export publisher stats as CSV, hide revenue or subscriber counts, and manage live videos from one place to simplify workflows and protect privacy.
  3. Code and formatting are much improved: code blocks now auto-detect language, offer syntax highlighting, line numbers, and one-click copy, making technical posts clearer and easier to share.

The NFL Media is Structurally Anti-Player

Freddie deBoer • 2289 implied HN points • 07 Mar 26
🎾 Sports Analytics
  1. NFL analysts often treat cost-efficiency analytics as neutral, which leads them to praise roster strategies that systematically underpay players, effectively aligning media coverage with owners even when individuals sympathize with players.
  2. League institutions — the draft and rookie wage scale, the franchise tag, the salary cap, and legal protections for teams — severely limit players' bargaining power, producing short careers, little long-term security, and wages far below what a true open market would pay.
  3. Sports media usually fails to name or criticize these structural injustices, instead celebrating "smart" moves that maximize owner profit; analysts should be explicit about whose interests those strategies serve and advocate for fairer treatment of players.

The Luol Deng Law

Freddie deBoer • 4084 implied HN points • 16 Feb 26
🎾 Sports Analytics
  1. Intentionally losing games and resting healthy stars for strategic reasons destroys fan trust and makes the product less enjoyable.
  2. Having good players who make a team competitive is better for fans than deliberately staying bad to chase draft picks, even if those players can’t single-handedly win a title.
  3. The league’s incentives are broken, so practical fixes (shorter season, removing protected picks, rethinking the draft) and a cultural shift away from ‘championship-or-nothing’ thinking are needed to restore competitive integrity.

Layer by Layer, We Built Data Systems No One Understands

SeattleDataGuy’s Newsletter • 706 implied HN points • 02 Mar 26
🕹 Technology Analytics
  1. Layering tools and roles keeps adding complexity until systems become fractal sprawl that’s costly and hard to maintain.
  2. Buying managed platforms can replace people and speed delivery short-term, but it often buries business logic and makes it harder to connect technical work to business outcomes, so teams tend to add even more layers.
  3. Before adding any new layer, ask what problem it solves, what happens if you don’t add it, and who will own it in six months—if you can’t answer, you’re creating liability instead of leverage.

COOPER NCAA basketball ratings

Silver Bulletin • 149 implied HN points • 18 Mar 26
🎾 Sports Analytics
  1. COOPER is a new power-rating system that ranks all 365 Division I men’s basketball teams using wins and losses, margin of victory, team tempo, preseason polls, and conference strength.
  2. The ratings are Bayesian/Elo-like and update continuously: an impact factor weights recent games, close matchups, conference games, and NCAA tournament games more heavily, and ratings partly carry over between seasons.
  3. COOPER offers detailed outputs (offensive/defensive ratings, strength of schedule, home-court factors), an objective-only variant, historical season-ending ratings back to 1950, and tools to convert ratings to win probabilities; tournament forecasts blend COOPER with other models and injury data and are available to subscribers.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Behind the Numbers: A Deep Dive into 2025's Top International NBA Prospects (Part 1)

Chad Ford's NBA Big Board • 19 implied HN points • 31 Oct 24
🎾 Sports Analytics
  1. Scouting international NBA prospects is tough because they often play less and face varying competition, making it hard to assess their true potential.
  2. Some young players, like Nolan Traore, show great promise but have mixed stats, indicating areas where they need to improve.
  3. The article highlights top players from Europe now, with plans to cover talents from Australia and China later, suggesting a strong international class for the next NBA draft.

Building Solo

Madhur’s Writings • 84 implied HN points • 09 Mar 26
🕹 Technology Analytics
  1. Launched two consumer products while solo to learn end-to-end product building and shipping real apps.
  2. Leans heavily on AI coding assistants and reusable agent skills to speed up development and design work.
  3. Picks pragmatic, cost-conscious, and privacy-first infrastructure and services—hosting (Vercel/Hetzner/GCP), Cloudflare R2 for storage, Neon for databases, GitHub Actions for CI/CD, Stripe for payments, and Resend/Zoho for email, plus analytics like PostHog and Google Analytics.

Go crazy, folks, go crazy

benn.substack • 1636 implied HN points • 13 Feb 26
🕹 Technology Analytics
  1. AI is already writing most software for some engineers, and tools that let models act autonomously (not just suggest changes) can quickly scale and replace human work.
  2. Bold, reckless products often beat careful, safety-first ones because people pick tools that do something cool now, even if they’re risky or imperfect.
  3. Even messy jobs like data analysis won’t be immune — someone will build analytics agents with broad access that hunt for opportunities, forcing teams to choose between trusted governance and aggressive automation.

COOPER women's NCAA basketball ratings

Silver Bulletin • 86 implied HN points • 15 Mar 26
🎾 Sports Analytics
  1. Final regular-season COOPER power ratings have been published for all 363 Division I women's basketball teams, tracking each team’s highs and lows over the year.
  2. COOPER is a new Elo/Bayesian-style rating system that blends wins, margin of victory, tempo, preseason polls, and conference strength, and it weights recent, close, conference, and tournament games more heavily with some customization for the women’s game.
  3. Paid subscribers get the full dataset and extras — offensive/defensive ratings, strength of schedule and home-court factors, an objective-only version, historical season-end ratings back to 2002–03, a spreadsheet to convert ratings into win probabilities, and NCAA tournament forecasts coming after the brackets.

I spent 8 hours learning Parquet. Here’s what I discovered

VuTrinh. • 1658 implied HN points • 24 Aug 24
🕹 Technology Analytics
  1. Parquet is a special file format that organizes data in columns. This makes it easier and faster to access specific data when you don't need everything at once.
  2. The structure of Parquet involves grouping data into row groups and column chunks. This helps balance the performance of reading and writing data, allowing users to manage large datasets efficiently.
  3. Parquet uses smart techniques like dictionary and run-length encoding to save space. These methods reduce the amount of data stored and speed up the reading process by minimizing the data that needs to be scanned.

Stealing Signals, Week 5, Part 2

Stealing Signals • 499 implied HN points • 08 Oct 24
🎾 Sports Analytics
  1. Offensive football is evolving, with more exciting plays and downfield shots happening. Quarterbacks are becoming better at making big plays, which makes the game more enjoyable.
  2. In fantasy leagues, it's important to play for high-scoring potential rather than just trying to avoid losses. Playing safe can lead to missed opportunities and a loss, so always aim for the best possible plays.
  3. Analyzing football can be a complex task, and it's common for analysts to have blind spots. It's crucial to keep digging deep and not rely only on surface-level insights to make informed decisions.

TPRR check-in: Who has made strides earning volume so far?

Stealing Signals • 599 implied HN points • 03 Oct 24
🎾 Sports Analytics
  1. Routes data is really important for understanding how well players are performing. Different sources measure these routes in different ways, which can create confusion.
  2. The NFL has started providing its own routes data, which could help standardize how we analyze player performance. This might make comparisons easier and clearer moving forward.
  3. Stats like TPRR (Targets Per Route Run) help us understand player efficiency, but they need to be used alongside other context like player roles and QB performance for better insights.

Don’t Design for Average Users

Jakob Nielsen on UX • 48 implied HN points • 19 Mar 26
🪑 Design Analytics
  1. The arithmetic average lies in digital products because usage is heavily skewed: a small P95/P99 group often creates most of the value while the median user is usually a low-contribution "tourist."
  2. You must design two experiences: a ruthlessly simple, friction-free on‑ramp for P50 tourists, and deep, uncapped, high‑performance tools (APIs, macros, shortcuts) for P95 whales, revealed via progressive disclosure.
  3. Track the full distribution (P25/P50/P75/P95/P99) and the P95/P50 ratio to guide pricing, retention, and roadmap choices, and focus resources on protecting and growing the high-value tail.

Maybe, finally—the end of SQL

benn.substack • 1380 implied HN points • 23 Jan 26
🕹 Technology Analytics
  1. Writing and reading SQL demand different styles: shortcuts and shorthand speed up writing but make queries harder to understand, and teams often prioritize writing convenience over clarity.
  2. With AI generating much of the code, development has shifted to a "vibe and verify" model, but data work is hard to verify because queries and analyses are difficult to check by eye or prose alone.
  3. The solution is better representations for comprehension — diagrams, clearer formatting, or a language/app that turns any query into an accessible, annotated picture so humans can quickly verify what the computation actually did.

The Argument That Lebron is the GOAT Is Running Out of Time

Freddie deBoer • 3496 implied HN points • 18 Dec 25
🎾 Sports Analytics
  1. LeBron’s claim to be the GOAT is strongest while he’s still playing, but it will weaken after he retires because cultural attention and recency bias shape who gets remembered.
  2. Comparing athletes across eras is misleading since rules, training, scouting, and media change how players perform and how we perceive them.
  3. GOAT debates are more about identity, nostalgia, and presentism than objective truth, so labels of "greatest ever" are temporary and context-dependent.

The Analytical Skills No One Teaches You

SeattleDataGuy’s Newsletter • 1165 implied HN points • 23 Jan 26
💼 Business Analytics
  1. Practice analytical intuition by doing rough estimates, breaking problems into proxy values, understanding baselines and natural variance, and always running manual spot checks instead of blindly trusting tooling.
  2. When a metric moves unexpectedly, first confirm the data with multiple sources, then generate and test product, market, user, and external hypotheses to pinpoint the root cause and escalate with concrete analysis.
  3. Choose KPIs that are relevant, measurable, specific, prioritized, and balanced — pick the right type (North Star, top-level, secondary, or OMTM), avoid vanity metrics, and use simple, trusted proxy metrics tailored to your product.

5 Key Predictions for the Data Industry in 2026

SeattleDataGuy’s Newsletter • 741 implied HN points • 31 Jan 26
🕹 Technology Analytics
  1. Big cloud vendors will keep rebranding and repositioning their data products to appear 'AI-first', adding marketing noise and confusion about which tools to use.
  2. Almost all companies still rely on Excel, SFTP, and manual exports. Only a small share chase flashy AI while most need simple tools to convert spreadsheets into reliable data pipelines.
  3. The modern data stack will be shaken by acquisitions, price changes, and fragile pipelines, forcing many teams to rebuild infrastructure and turn AI proofs-of-concept into production-ready foundations.

Uh, Guys: Brentford Might've Just Solved Soccer

No Grass in the Clouds • 139 implied HN points • 11 Oct 24
🎾 Sports Analytics
  1. Brentford has been scoring quickly, netting goals in the first 90 seconds of their games. This gives them a strong advantage over the other teams.
  2. Teams that score first tend to win more often, making early scoring really important in soccer.
  3. Brentford's strategy could be a smart playbook for other teams to follow to boost their chances of winning games.

Common Data Pipeline Patterns You’ll See in the Real World

SeattleDataGuy’s Newsletter • 859 implied HN points • 05 Jan 26
🕹 Technology Analytics
  1. Data pipelines come in many shapes — from source standardization and amalgamation to enrichment, operational syncs, and even manual Excel-based processes — each built for different business needs.
  2. Common challenges are mapping and standardizing varied formats, keeping reliable IDs and timing for joins, and handling data quality and system-specific ingestion limits.
  3. Despite the variety, pipelines all aim to move and transform source data into usable outputs for analytics, operations, or ML, and they often follow the same extract-transform-load steps that can be automated and productionized.

Kafka can be so much more

Complexity is overrated • 85 implied HN points • 24 Feb 26
🕹 Technology Analytics
  1. Data should be viewed as a stream of events rather than just a static database state, and Kafka implements this by providing a distributed immutable commit log that decouples producers and consumers.
  2. Kafka is extremely versatile and gets used for many scenarios beyond its original use case, but teams often pigeonhole it or call it overkill for problems it can actually solve well.
  3. An expanding Kafka ecosystem (Kafka++) — integrating tools like Flink and Iceberg — makes real-time streaming data more useful for analytics, data engineering, and operational use cases, widening who can benefit from Kafka.

True Pressure Rate (TPR): Week 6 Update

Trench Warfare • 79 implied HN points • 15 Oct 24
🎾 Sports Analytics
  1. True Pressure Rate (TPR) is a new tool for evaluating pass-rushers that focuses on the quality of pressures, not just the amount. This helps to understand who the best defenders really are.
  2. Pressures are categorized into three quality levels: Rare High Quality, High Quality, and Low Quality. This classification provides deeper insight into a player's performance and effectiveness.
  3. The Pressure Quality Ratio (PQR) compares high-quality pressures to low-quality ones. This helps identify players who may not have a lot of pressures but are still working hard and making an impact.

Did Nerds Ruin the NBA?

House of Strauss • 32 implied HN points • 13 Mar 26
🎾 Sports Analytics
  1. Analytics and so-called "nerds" have reshaped the NBA, but they aren't the only cause of the changes.
  2. Players are also "playing like nerds" by adopting analytically driven styles, and their choices shape how the game looks.
  3. Saying nerds ruined the league is too simple — the shifts are complex and not entirely anyone's fault.

The Silver Bulletin Super Bowl LX preview

Silver Bulletin • 332 implied HN points • 04 Feb 26
🎾 Sports Analytics
  1. The Seahawks and Patriots both started the season as longshots but have become surprising Super Bowl LX contenders, making them feel like overachievers this year.
  2. The ELWAY forecast system has been bullish on both teams since it began publishing, producing ratings, QB adjustments, and simulations that largely line up with Vegas odds.
  3. The preview examines key X‑factors — quarterback health and performance, the Patriots' schedule, and why Super Bowls often score high — and it uses 30,000 simulations to project likely final scores and best square picks.

How does Notion handle 200 billion data entities?

VuTrinh. • 519 implied HN points • 06 Aug 24
🕹 Technology Analytics
  1. Notion uses a flexible block system, letting users customize how they organize their notes and projects. Each block can be changed and moved around, making it easy to create what you need.
  2. To manage the huge amount of data, Notion shifted from a single database to a more complex setup with multiple shards and instances. This change helps them handle stronger user demands and analytics needs more efficiently.
  3. By creating an in-house data lake, Notion saved a lot of money and improved data processing speed. This new system allows them to quickly get data from their main database for analytics and support new features like AI.

NBA Future of the Franchise Rankings III

Silver Bulletin • 136 implied HN points • 19 Feb 26
🎾 Sports Analytics
  1. A three-expert panel ranks all 30 NBA teams by their odds of winning championships over the next ten seasons, treating titles — not merely good records — as the only thing that matters.
  2. Long-term title chances hinge on more than current rosters: draft picks, cap flexibility, front-office skill, and market allure all matter, and the three judges weigh those factors differently.
  3. Recent moves and injuries have meaningfully altered outlooks — some teams are sliding because of bad trades or aging stars and cap constraints, while others rise from draft positioning or clearer rebuild plans.

Introducing COOPER: Silver Bulletin's NCAA basketball rating system

Silver Bulletin • 26 implied HN points • 10 Mar 26
🎾 Sports Analytics
  1. COOPER is a new Bayesian college basketball rating system that combines margin-of-victory, opponent strength, pace, and preseason expert polls to produce offensive/defensive (PPPG/PPAG) and Elo-based ratings.
  2. The model changes include separate offensive and defensive ratings, removal of the rule that winners must always gain points, game impact factors that weight close and high-stakes games more, and a time-varying k-factor that updates ratings more aggressively early in the season.
  3. Tournament forecasts combine COOPER with Pomeroy/Her Hoop Stats (COOPER gets 5/8 weight), run conditional (“hot”) simulations that update ratings as simulated games occur, and explicitly model injuries probabilistically to adjust win probabilities.

Staying Relevant in a Post-AI World

Let's talk games & AI. • 15 implied HN points • 17 Mar 26
🕹 Technology Analytics
  1. Surface-level polish can hide core flaws and create false positives. Always put a bare prototype in front of users first and make evaluation an explicit, scheduled step before you add polish.
  2. AI speeds up production but not judgment, so faster generation shouldn’t force faster review. Don’t let generation volume set your review pace—deliberate discernment must be preserved.
  3. As AI and automated testing scale, volume and measurement can replace human taste, making distribution the real advantage. Build and nurture an audience now because reach will matter more once creation commoditizes.

Inside the Algorithms Sportsbooks Use to Track Bettors, Limit Winners, and Push Losers to Bet More

Huddle Up • 185 implied HN points • 02 Feb 26
🎾 Sports Analytics
  1. A tiny share of bettors — VIPs and high-volume losers — produce most sportsbook profits, so operators design products and margins around that long-tail revenue curve.
  2. Sportsbooks use AI plus required KYC/AML and behavioral data to profile every account from signup, tracking things like age, address, device, geolocation, social links, payment method, and app usage patterns.
  3. Those profiles drive targeted tactics — push notifications, personalized bonuses, VIP perks, A/B tests, product nudges, and limits or bans for winners — to press losing customers to bet more and protect the house.

Netflix Data Engineer Stack

VuTrinh. • 359 implied HN points • 30 Jul 24
🕹 Technology Analytics
  1. Netflix's data engineering stack uses tools like Apache Iceberg and Spark for building batch data pipelines. This helps them transform and manage large amounts of data efficiently.
  2. For real-time data processing, Netflix relies on Apache Flink and a tool called Keystone. This setup makes it easier to handle streaming data and send it where it needs to go.
  3. To ensure data quality and scheduling, Netflix has developed tools like the WAP pattern for auditing data and Maestro for managing workflows. These tools help keep the data process organized and reliable.

Issue #15 – The Data Quality Conundrum (Part 1 – Root Causes)

The Data Ecosystem • 399 implied HN points • 21 Jul 24
💼 Business Analytics
  1. Poor data quality is a big problem for organizations, but it's often misunderstood. It's not just about fixing bad data; you need to figure out what's causing the issues.
  2. Data quality has many aspects, like accuracy and completeness. Good data helps businesses make better decisions, while bad data can cost a lot of money.
  3. To solve data quality issues, you need a complete approach that looks at different root causes. Simply fixing one part won't fix everything, and different sources might create new problems.

Stuff Worth Reading - Sep 2024

clkao@substack • 79 implied HN points • 30 Sep 24
🕹 Technology Analytics
  1. GitHub succeeded because it created tools that developers really wanted and used. The combination of Git's technical features and GitHub's social features made it very popular.
  2. The analytics and data workflow still lag behind traditional development methods. It's important to find better ways to show the value of data to businesses.
  3. There's a new way to think about pricing that considers what buyers really want, not just traditional methods. This can lead to smarter pricing strategies.

SBSQ #28b: Maye vs. Stafford and Yelp vs. Google

Silver Bulletin • 212 implied HN points • 19 Jan 26
🎾 Sports Analytics
  1. Advanced metrics give Drake Maye the edge for MVP because they count all QB plays (passes, sacks, scrambles) and show he leads in QBERT, EPA, and net yards per dropback; his mobility and rushing production offset Stafford’s better raw passing and TD totals.
  2. Google ratings are generally higher and easier to leave while Yelp is stricter and skews toward foodie, urban reviewers, so scores can differ; when picking restaurants, prioritize the number of reviews over the average, check the menu to match your tastes, and don’t oversearch for perfection.
  3. The College Football Playoff selection is broken because the committee mixes inconsistent goals (picking the most ‘deserving’ teams vs. the objectively ‘best’ teams) and applies criteria unevenly; they should clarify their objective, include more diverse and data-savvy members, or use a transparent algorithm.

So, who’s going to win the Super Bowl?

Silver Bulletin • 280 implied HN points • 07 Jan 26
🎾 Sports Analytics
  1. This NFL season was unusually chaotic, with most preseason co-favorites failing to make the playoffs and standings flipping because of close-game variance and injuries.
  2. A new predictive model that updates through the season and accounts for quarterback play, injuries, weather, home-field and rest gives refined playoff odds and currently rates Seattle as the likeliest Super Bowl contender, though no team has anywhere near a 50% chance.
  3. Turnover margins are often driven by luck and are less reliable for prediction than efficiency stats like yards per play or sacks, while injuries can meaningfully reduce a team’s rating and playoff prospects.

Agent Analytics Engineer

davidj.substack • 95 implied HN points • 06 Feb 26
🕹 Technology Analytics
  1. Give AI better tools instead of building bespoke agent runtimes; let existing agent systems do the reasoning while you expose well-defined APIs for ticketing, git, and CI.
  2. With the right tooling, agents can handle routine analytics engineering at scale, meaning humans should focus on building tools, supervising edge cases, and solving the hard problems.
  3. Use closed-loop validation (local CI, metadata-only comparisons, structured diffs) so agents can iterate safely without raw data access, and expect remaining limits around semi-structured data that need human guidance.

I Am Begging Sports Media to Stop Misusing Regression to the Mean

Freddie deBoer • 2165 implied HN points • 03 Jul 25
🎾 Sports Analytics
  1. Regression to the mean means that extreme results are unlikely to happen again without some change in conditions. If a team's situation changes, it’s not just luck but a new factor affecting performance.
  2. Using regression to the mean incorrectly can lead to confusion. If someone thinks a team will do worse because they lost players, that’s not regression to the mean; it’s a different kind of prediction.
  3. There’s a risk of making mistakes by assuming past results will always influence future ones, like betting based on past game outcomes. Each situation should be treated by its own conditions.

Preseason Sunday: Fantasy Football is Much More Fun When You Accept What You Can't Control

Freddie deBoer • 1392 implied HN points • 24 Aug 25
🎾 Sports Analytics
  1. Fantasy football is a game influenced by factors beyond your control, like player injuries or unexpected coaching decisions. Understanding this makes it more enjoyable instead of stressful.
  2. Many people take fantasy football too seriously, often leading to conflicts and anxiety. It's best to see it as a fun way to connect with friends rather than a high-stakes competition.
  3. Embrace the randomness of the game; it's part of what makes it exciting. Celebrating small wins, like a lucky player pick, can enhance your enjoyment of the season.

#46 - Thought Experiment - Your Data Debt Just Became Less Worrying

OSS.fund Newsletter • 113 implied HN points • 29 Jan 26
🕹 Technology Analytics
  1. AI-powered semantic layers can query messy, fragmented systems and deliver unified read-only insights fast, making many long master-data consolidation projects unnecessary for read-heavy analytics.
  2. You still need traditional MDM for writes, transactional consistency, and regulatory requirements like GDPR, because semantic abstraction doesn’t tell you where to update or delete authoritative records.
  3. A practical approach is to segment use cases into read vs write, run semantic tests on top business questions to capture immediate value, and invest in targeted MDM only for the write/compliance-critical scenarios.

QBERT NFL Quarterback Ratings

Silver Bulletin • 364 implied HN points • 02 Dec 25
🎾 Sports Analytics
  1. The QBERT system ranks NFL quarterbacks based on many performance factors, not just traditional stats. It looks at things like rushing yards and how well they handle pressure.
  2. Ratings are adjusted for different conditions, like the strength of the opposing team and the weather, making it fairer across the board.
  3. Along with current ratings, QBERT provides future projections for quarterbacks, taking into account their recent performances, age, and experience.

Chiefs Fans Doing the "War Chant" After Beating the Worst Team in the League by Two is the Most Pathetic Thing I've Ever Seen From Any Fanbase

Freddie deBoer • 3712 implied HN points • 30 Nov 24
🎾 Sports Analytics
  1. Chiefs fans celebrated a narrow win over a bad team with their war chant, which some see as embarrassing and inappropriate. It's not cool to act like you just beat a top team when you barely won against the worst one.
  2. There are concerns about the Chiefs' performance this season compared to past years. Their offensive play has slowed down, and some fans and analysts feel they aren't as dominant as before.
  3. Many Chiefs fans act like a lot of people hate them because they are successful. Instead, they should recognize their team's success and stop complaining about being disrespected, as they are now a winning franchise.