The clock reads 6:47 in the third quarter. Your team is down three points, facing 4th and 2 at midfield. For decades, the conventional wisdom was automatic: punt the ball away and hope your defense makes a stop. But on Sunday across America's NFL stadiums, something remarkable is happening. Teams are challenging that wisdom with data, and the results are reshaping how football is played. In the 2023 NFL season, teams went for it on 4th down approximately 23% more often than they did a decade earlier. Some of this increase stems from rule changes and philosophical shifts, but the real driver is analytics. Teams now have access to comprehensive data showing that going for it on 4th down is dramatically undervalued by traditional football thinking. The margin between analytical expectation and actual performance reveals one of the most significant inefficiencies in professional sports. This article explores the data patterns behind 4th down decision-making, revealing why teams willing to challenge convention are winning more games than Vegas expects, and what the numbers tell us about the future of NFL strategy. The NFL Data Ecosystem: More Information Than Ever Understanding NFL analytics requires first appreciating the sheer volume of data available to modern front offices. We're not talking about simple box scores anymore. Teams now collect: Tracking data : Real-time positioning of all 22 players on every play, collected at 10 frames per second Biometric data : Player fatigue levels, GPS tracking during games, heart rate variability, and recovery metrics Situational data : Down and distance, field position, score differential, time remaining, and opponent tendencies Personnel data : Matchup analysis comparing specific offensive and defensive units Environmental data : Weather conditions, field surface characteristics, altitude, and crowd noise levels This data ecosystem emerged gradually. NFL teams began serious analytics initiatives in the early 2010s, largely insp

Hook: Why Your Next Sports Analytics Project Shouldn't Cost a Fortune Last summer, a college student in Ohio built a machine learning model that predicted NBA player performance with 87% accuracy—without spending a single dollar on data. Meanwhile, a startup in London created a real-time football analytics dashboard that rivaled paid enterprise solutions. The secret? Free sports data APIs. The sports data landscape has transformed dramatically. Where teams once paid six figures for proprietary datasets, developers and data scientists now have access to institutional-quality information at zero cost. Whether you're building a fantasy sports optimizer, analyzing player statistics, or creating predictive models, the barrier to entry has never been lower. But not all free APIs are created equal. Some offer comprehensive historical datasets spanning decades. Others provide real-time updates but limited depth. This guide cuts through the noise and delivers a practical, hands-on review of the best free sports data tools available in 2025. Why Free Sports Data Matters Now More Than Ever The democratization of sports data represents a fundamental shift in the industry. Five years ago, accessing granular sports statistics required partnerships with ESPN, official league APIs, or expensive data brokers. Today's ecosystem has flipped that model. The practical advantages: Lower barriers to entry : Students, hobbyists, and early-stage startups can build sophisticated analytics projects without capital constraints Rapid prototyping : Test hypotheses and validate ideas before investing in premium data services Educational access : Learn data engineering, machine learning, and API integration with real-world sports datasets Competitive alternatives : Many free APIs now compete directly with paid solutions in specific domains The catch? Free doesn't mean unrestricted. Rate limits, update frequencies, and feature sets vary dramatically. Understanding what each tool offers—and its limi

If you've ever tried to pull live options data into a Python script, you've probably hit the same wall I did: the cheapest real-time providers start at $99/mo. Here's how to do it for $20/mo — or free if you stay within 1,000 credits/day. What You'll Need Python 3.8+ requests library ( pip install requests ) An API key from market-option.com (free tier available, no card required) Fetching a Full Options Chain import os import requests API_KEY = os . environ [ " MARKET_OPTIONS_KEY " ] BASE_URL = " https://market-option.com/api/v1 " def get_chain ( ticker : str ) -> list [ dict ]: res = requests . get ( f " { BASE_URL } /options/chain/ { ticker } " , params = { " apiKey " : API_KEY }, ) res . raise_for_status () return res . json ()[ " results " ] contracts = get_chain ( " SPY " ) print ( f " { len ( contracts ) } contracts returned " ) print ( contracts [ 0 ]) Each contract in results looks like this: { "details" : { "contract_type" : "call" , "strike_price" : 530 , "expiration_date" : "2026-01-17" , "ticker" : "O:SPY260117C00530000" }, "last_quote" : { "bid" : 3.45 , "ask" : 3.50 , "midpoint" : 3.475 }, "greeks" : { "delta" : 0.42 , "gamma" : 0.031 , "theta" : -0.18 , "vega" : 0.29 }, "implied_volatility" : 0.182 , "open_interest" : 12418 } Filtering by Expiration and Strike def get_near_the_money ( ticker : str , expiration : str , spot : float , width : float = 0.05 ): """ Return contracts within ±width% of spot price. """ contracts = get_chain ( ticker ) low = spot * ( 1 - width ) high = spot * ( 1 + width ) return [ c for c in contracts if c [ " details " ][ " expiration_date " ] == expiration and low <= c [ " details " ][ " strike_price " ] <= high ] atm = get_near_the_money ( " SPY " , " 2026-01-17 " , spot = 530 ) for c in atm : print ( c [ " details " ][ " strike_price " ], c [ " details " ][ " contract_type " ], c [ " last_quote " ][ " bid " ], c [ " greeks " ][ " delta " ], ) Scanning for High IV Contracts def high_iv_scan ( ticker : str , iv_threshold :

If you've shipped anything to the browser, you've used localStorage . And if you've used it for more than five minutes, you've also written this exact line more times than you'd like to admit: const user = JSON . parse ( localStorage . getItem ( ' user ' ) || ' null ' ) The Web Storage API has aged remarkably well for something so small, but it carries three persistent pain points that every frontend codebase ends up papering over by hand. Pain point #1: everything is a string. localStorage.setItem('count', 0) doesn't store the number 0 — it stores the string "0" . Read it back and typeof is "string" . Booleans become "true" / "false" , Date objects collapse into ISO strings (if you're lucky) or "[object Object]" (if you're not), and undefined becomes the literal string "undefined" . So every project grows a thin serialization layer of JSON.parse / JSON.stringify wrappers, plus a pile of defensive try/catch blocks for the day a malformed value sneaks in. Pain point #2: the API is verbose and stringly-typed. getItem , setItem , removeItem — three method calls and a string key for what is conceptually just reading and writing a property. It reads nothing like the rest of your code. Pain point #3: reactivity is broken in the tab you actually care about. The native storage event only fires in other tabs of the same origin. The tab that performed the write never hears about it. So if you want to react to your own storage changes — the overwhelmingly common case — the platform gives you nothing. Stokado is a small, zero-dependency library that addresses all three by wrapping any storage object in a Proxy . It's framework-agnostic, TypeScript-friendly, and works equally well with localStorage , sessionStorage , cookies, async backends like localForage, and a handful of mini-program runtimes. This article walks through what it actually does, feature by feature, with runnable code. Quick start npm install stokado import { createProxyStorage } from ' stokado ' const storage =

A couple weeks ago I posted about ImageX - a dumb little CLI that lets you edit images without googling "resize image online" for the hundredth time. If you haven't seen it, the tldr is: pip install imagex && imagex , a menu pops up, pick what you want, done. No flags to memorize, no syntax to look up - just arrow keys and enter. Everything runs locally on your machine - no uploads, no server ever sees your images, no browser,no ads. Honestly? Didn't expect much. But: 10 stars on GitHub 1 contributor already dropped a PR (Flip feature - thanks! :) A bunch of feature requests in issues So I kept building. What's new in v0.3.0 Three features, one fix: Grayscale / B&W - luminosity conversion or threshold-based true black & white. Slider for the threshold so you can dial in exactly how much black you want. Invert Colors - instant negative. Handles RGBA without destroying alpha. Flip - mirror horizontally, vertically, or both. (PR from a contributor!) Plus: version check on startup (tells you when to upgrade), and all operations now preserve EXIF/ICC metadata instead of silently dropping it. Yeah, that was a bug. Fixed. The code is still stupid simple NAME = " Invert Colors " DESCRIPTION = " Invert image colors (negative effect) " def run ( file , output_path , args ): img = Image . open ( file ) # ... invert logic ... img . save ( output_path ) return True Links GitHub: github.com/kushal1o1/ImageX PyPI: pip install -U imagex PRs welcome. You could write a feature in the time it took to read this.

Jump Tables, TLV Footers, and the Real Cost of Reading What You Don't Need You're paying for bytes you never read. A data engineer on a busy pipeline touches dozens of Parquet files a day: schema discovery, predicate pushdown, column pruning, metadata scrapes for a data catalog sync. In each case, the application needs maybe 200 KB of context from a file that is 4 GB on disk. Without a seekable archive format and a jump table to find the right frame, your HTTP client fetches the whole thing, and your cloud egress invoice reflects every unnecessary gigabyte. This post quantifies the problem, then walks through how HuskHoard uses seekable Zstd frames, a per-volume jump table, and TLV-encoded footer metadata to make partial egress a first-class citizen across multi-volume archives — disk, cloud, and LTO tape alike. The Problem, In Dollars S3 standard egress runs $0.09/GB. GCS is $0.08/GB. Even Cloudflare R2, which is free for egress from R2 to the internet , still costs you in latency and API call count when you cannot bound the range of bytes you need. Here is a representative read pattern for a cold analytics archive: Operation Bytes Needed Bytes Fetched (naïve) Ratio Schema discovery ~50 KB (Parquet footer) 1–8 GB (full file) ~1:16,000 Single column scan ~200 MB (one column chunk) 4 GB (full row group) 1:20 Data catalog sync (1M files) ~50 GB (footers only) ~4 PB (full files) 1:80,000 Selective restore (1 row group) ~128 MB 4 GB 1:32 On 100 TB of cold Parquet data with $0.09/GB egress: Full read for schema sync : 100 TB × $0.09 = $9,216 Partial read (footers only, avg 100 KB/file, 1M files) : ~100 GB × $0.09 = $9.00 Savings per catalog sync: $9,207 — 99.9% reduction Even a conservative column-scan scenario (pulling 15% of each file's bytes) cuts a $9,216 monthly read bill to $1,382 . The ceiling on savings is determined entirely by how precisely you can address the bytes you actually need. That precision is what frames and jump tables buy you. Zstd Frames: What They

Europe is in the middle of a record-breaking heat wave, and the grid is being pushed to its limits as people turn to fans and air-conditioning to try to stay cool. Some power plants won’t be online to help handle the load. On June 23, France saw its hottest day since record-keeping began in 1947.…

One of the most important blogs I have written in the last 25 years that most devs struggle with. submitted by /u/watson_full_scale [link] [留言]

There are an overwhelming number of Prime Day deals to sort through, which is why we spend so much time highlighting products we’ve already tested and can stand behind. But our recommendations are only one way to identify a worthwhile deal: another is seeing which products fellow Verge readers actually buy. Below, we’ve rounded up […]

At high-stakes meetings with the White House, Anthropic's cofounder—a "weirdo," per one official—has been replaced by cofounder Tom Brown.

Article URL: https://www.winnipegsd.ca/student-registration Comments URL: https://news.ycombinator.com/item?id=48661195 Points: 4 # Comments: 1

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The new Zoox robotaxi has more cushioning, lighter colors, and a better microphone and speaker for communicating with Zoox Support.

8BitDo announced a new version of its all-button Arcade Controller that debuted a little over a year ago. The new Arcade Controller Pro looks similar to 8BitDo's original with a leverless (or Hitbox) design that replaces joysticks with buttons for improved precision and responsiveness. The new Pro version comes with upgrades that include an even […]

Zoox, the autonomous vehicle company owned by Amazon, unveiled a new look for its boxy, bidirectional robotaxi, calling it the "next evolution" of the vehicle intended for mass production. The company is currently operating a free robotaxi service in San Francisco, Las Vegas, Austin, and Miami while it waits for the federal government to approve […]