Sports Odds Discrepancy Alert Scanner

@hirujiyu

Hi, Based on your requirements, I can build a modular odds-arbitrage and value-betting monitoring system that continuously compares probabilities from Kalshi and Polymarket against major sportsbook markets and alerts you whenever a configurable edge is detected. Suggested Tech Stack: • Python (FastAPI + AsyncIO) for high-frequency data collection • PostgreSQL for market history and opportunity tracking • Redis for caching and rate-limit handling • Telegram/Slack bot for instant notifications • Lightweight React dashboard (optional) for monitoring opportunities ❓ A few questions: 1. Which sportsbooks are your highest priority (DraftKings, FanDuel, BetMGM, Caesars, etc.)? 2. Do you already have access to any paid odds APIs, or should I recommend the best data-source strategy? 3. Do you want only pre-game markets initially, or should the architecture support live/in-play markets later? I have experience building API-driven data platforms, real-time monitoring systems, and automated alerting workflows. The modular approach will ensure new sportsbooks or prediction markets can be integrated quickly as your requirements evolve. Looking forward to discussing the milestones and implementation strategy. Best regards, Team DDS.

@opencollar

Hi, I see you want a tool to catch betting odds gaps that can help find profitable bets in sports. It's interesting how markets and odds can sometimes drift apart, revealing hidden value. I will make sure to get live data from Kalshi, Polymarket, and two big sportsbooks, then turn those prices into easy-to-understand probabilities. A quick and reliable way to add new sportsbooks later will be built in. How do you see a good way to set those alert thresholds for your preferred sports? Let's discuss a plan and build something bigger together. Regards, Nick.

@diektech

Hi — Elias here from Miami. I understand you need a tool to monitor event-level probabilities from Kalshi and Polymarket. This application is crucial for informed betting, but it comes with specific technical challenges. What usually matters most here is ensuring real-time data accuracy and seamless integration with both APIs. A common issue in systems like this is managing the frequency of data pulls without overwhelming the servers. The tricky part is maintaining data reliability while ensuring scalability as the number of events grows. I suggest structuring the tool with a robust backend to handle asynchronous API calls and implement caching to optimize performance. This approach enhances stability and makes future expansion easier as you add more data sources. I've worked on similar data aggregation tools and understand the nuances involved in ensuring a smooth user experience and reliable data flow. A few questions to better understand the scope: Q1 – What specific events or probabilities are most critical for your needs? Q2 – How do you envision handling user roles or permissions within the tool? Q3 – Are there particular data processing requirements or output formats to consider? Happy to discuss the details and suggest the best technical approach. Looking forward to hearing from you.

@kabirchy

Hi there, I’ll build a lightweight, reliable odds discrepancy scanner that pulls live probability data from Kalshi and Polymarket via their official APIs, then compares it against real-time sportsbook odds — including moneyline, spreads, totals, and props — using both official APIs (where available) and carefully structured web scraping for sites without public endpoints. The system will run as a scheduled Node.js service with Python subprocesses for heavy data normalization and discrepancy scoring. I’ll implement smart caching, rate-limiting, error resilience, and a clean JSON output format showing detected arbitrage or mispricing opportunities with timestamps and source confidence levels. All code will be modular, well-documented, and container-ready (Docker included). You’ll get full source access, setup instructions, and a simple CLI/webhook interface to trigger scans or receive alerts. Best Regards, Khorshed Alam, RS Software

@kamran2012

Hello, I will build the Python scanner that pulls probabilities from Kalshi, Polymarket, and two sportsbooks, converts all lines to implied probability, and fires Telegram alerts when the gap exceeds your configurable threshold. Each source gets its own modular adapter, so adding a new book stays under 30 lines. On a similar odds comparison tool, using async polling cut latency enough to catch lines before they corrected. Questions: 1) Which two sportsbooks in your area should I prioritize first? 2) Do you have existing API keys for Kalshi and any book, or starting fresh? Share your Kalshi account tier and preferred sportsbooks and I will map out the data access strategy tonight. Looking forward to talking through the details. Kamran

@starttechno

This tool will continuously compare Kalshi and Polymarket event probabilities with money-line, spread, totals, and prop prices from two major sportsbooks, then flag meaningful pre-game discrepancies across football, basketball, tennis, soccer, and baseball. I’ll build it in Python with a modular adapter architecture: separate Kalshi, Polymarket, and sportsbook connectors will normalize events, markets, outcomes, timestamps, and prices into one schema. American/decimal odds will be converted to implied probabilities, with configurable vig removal, event matching, threshold checks, and discrepancy/EV logging. I’ll use official APIs where available and a reliable rate-limited scraper fallback where necessary, while keeping each source replaceable. Alerts will use a simple adapter for Slack, Telegram, or email, and a CLI summary will show the latest opportunities; the design can later support a lightweight dashboard. Docker configuration will allow deployment on a standard cloud instance. The first milestone will demonstrate live feeds from all four sources, deliberately trigger a test alert, and include documentation showing how to add a sportsbook module in under 30 lines. I can deliver the working initial version in 3 days. Which two sportsbooks are available in your area for the first live-feed test? Muhammad Saad

@onkarkulkarni8

Hello, I checked your "Sports Odds Discrepancy Alert Scanner" project and it looks like understanding the existing workflow will be important before making any changes. I've worked on similar PHP projects involving php, javascript, python, web scraping, software architecture, node.js, data analysis, api development and prefer delivering work in small milestones so everything stays easy to review and adjust if needed. If you can share a few more details about the current setup and expected outcome, I'll suggest the best approach and provide an accurate timeline. ⭐ 5.0/5 from a recent client: "Project was delivered before Time with Best professional Knowledge One could ever held. Thanks for the support" Final timeline and cost will be confirmed in chat after a complete understanding and documentation of the project expectations in detail.

@Herculesz

Hi, this reads like a market-scanning system more than a simple alert tool, and I’ve built production logic around live data streams where the value is in clean normalization and fast signal detection. The real engineering risk is not the alert itself; it’s distinguishing true discrepancies from feed lag, line formatting differences, and transient market movement so the scanner stays usable. The closest project here is NYSE Day Trading Bot Development, where I built real-time ingestion and rule-driven signal logic against live market data. Custom Feature Development & Integration is also relevant if this needs to fit into an existing product rather than start greenfield. I usually structure systems like this with separate layers for source ingestion, canonical odds normalization, discrepancy evaluation, and alert delivery. That keeps feed-specific issues isolated and makes threshold tuning safer as coverage expands. I typically design the evaluation layer with deduping, confidence rules, and source health checks so bad or delayed inputs do not spam alerts. These are the kinds of systems I build for long-term production use, not one-off scripts. If useful, I can sketch the scanner architecture and alert decision flow first. Thanks, Hercules

@DeveloperStation

Hi, I've built ingestion pipelines that pull from multiple external sources, normalize the data, and fire alerts on threshold breaches. On one contract I set up a secure CI pipeline with deterministic ingestion where consistent parsing across feeds was the whole point. SecureCI: from Adil Freelancer, deterministic data ingestion The swappable-module part is the real design question here. I'd build each market (Kalshi, Polymarket, each book) as an adapter behind one interface that returns a clean implied probability, so adding a book stays under 30 lines. Python fits, async fetching with a small scheduler, alerts via Telegram or Slack. One question: for the two sportsbooks in your area, do any expose an official API, or should I plan for scraping from the start? Adil

@C0RETECHIES

Hi there, I understand you need an arbitrage scanner that polls odds from Kalshi, Polymarket, and various sportsbooks. The core operation is to normalize diverse odds formats into implied probabilities, compare them to find discrepancies exceeding a configurable threshold, and fire an immediate alert via Slack or Telegram. Technical approach: I propose a modular Python backend. Each data source will be a self-contained connector class. A central scheduler will poll them, storing normalized data in a lightweight DB like SQLite. The application can be containerized with Docker for simple deployment on any cloud VM. Core modules: Pluggable Data Connectors for each source (API/scraping). A unified Probability Engine for all odds formats. The Discrepancy Detector for the comparison logic. An Alert Dispatcher for Slack/Telegram. A simple YAML file will manage API keys, thresholds, and settings. Relevant systems: Our internal AI automation pipelines follow this exact pattern: integrating with disparate APIs, processing data against complex rules, and triggering real-time actions. This mirrors the data ingestion, comparison, and alerting workflow you require. Implementation strategy: We'll build a CLI-based MVP first: Kalshi + one sportsbook + Slack alerts. This validates the core normalization and detection engine quickly. From there, we'll add more sportsbook modules incrementally, and then build the simple web dashboard as a final step. Questions: 1. Do you have existing API keys for any paid data services, or should the initial strategy focus exclusively on public APIs and screen-scraping? 2. For converting odds to probabilities, how should we handle the bookmaker's margin (the 'vig')? Should we remove it to get a 'fair' probability, or is the raw implied probability sufficient? 3. What is the acceptable latency for an alert? Is polling every 30-60 seconds sufficient, or is a near-real-time streaming approach required? Regards, Rohit

@corpmember29

Hello, I HAVE SUCCESSFULLY BUILT SIMILAR AI-BASED DATA ANALYTICS AND AUTOMATION SYSTEMS AND CAN SHOW YOU MY PREVIOUS WORK. I have carefully reviewed your requirements and can develop a Sports Odds Discrepancy Alert Scanner with real-time data collection, probability conversion, market comparison, EV opportunity detection, and automated alerts through Telegram, Slack, or email. I have 10+ years of experience in the required technologies and expertise in Python, API integrations, data processing, automation, dashboards, and scalable backend systems. I can build a modular architecture where Kalshi, Polymarket, and sportsbook integrations can be easily extended. 1. User Dashboard Market Overview Odds Comparison Probability Analysis Discrepancy Alerts Favorite Markets Reports & History Notifications Profile & Settings 2. Data Processing System Kalshi Integration Polymarket Integration Sportsbook API Integration Odds Data Collection Probability Conversion EV Opportunity Detection Real-Time Monitoring 3. Admin Panel Dashboard Market Management API Source Management Alert Configuration User Management Reports & Analytics System Logs Settings I am available on desk as per your convenient time zone and will work on your project until you satisfied with my work. Thanks Christina

@Hubready

Hello!, I am a Florida-based senior software engineer(frontend, backend, ecommerce, etc) with 15 years of experience building data-driven tools, API integrations, and automated monitoring systems. I read your project description carefully, and I understand the goal is to continuously pull event-level probabilities from Kalshi and Polymarket, compare them, and alert on meaningful discrepancies quickly and reliably. This is the kind of project I take seriously because the value is in accuracy, timing, and clean architecture. My approach would be: 1) confirm the exact data sources and fields for both markets 2) build a stable pull and normalization layer 3) implement discrepancy rules and alert triggers 4) add logging, retries, and a simple output or dashboard if needed I have strong experience with Python, Node.js, JavaScript, PHP, API development, web scraping, and data analysis, so I can build this in a way that is maintainable, not brittle. I have also worked on financial and trading-style systems where precision and refresh timing matter a lot. A few relevant examples of similar work I’ve handled: - odds monitoring and alert tooling for a private sports analytics dashboard - market price comparison engine for a small trading signal app - automated data scraper and notifier for an internal arbitrage tracker Could you please clarify the following questions to help me better understand the project? 1) Do you want this to run as a local script, server app, or scheduled

@hiberokpars

Hello! I have 15+ years of experience building data aggregation tools, API integrations, sports odds systems, and automation. I can build a modular application that collects data from Kalshi, Polymarket, and sportsbooks, converts odds to implied probabilities, detects value opportunities, and sends alerts via Telegram, Slack, or email. The code will be easy to extend with additional sportsbooks. **Estimate:** **$2,000–4,000**, **3–5 weeks**.

@efanntyo

Hi there, I am excited about the opportunity to work on your Sports Odds Discrepancy Alert Scanner project. With extensive experience in web scraping, data analysis, and API development, I am well-equipped to deliver a robust tool that meets your requirements. I understand the core need is to continuously fetch and compare probabilities from Kalshi, Polymarket, and major sportsbooks, identifying discrepancies that offer positive expected value opportunities. My approach involves using Python for its versatility and rich library support, particularly in web scraping and data processing. I'll integrate APIs where available and develop reliable screen-scraping solutions as needed, ensuring we can convert odds into comparable implied probabilities effectively. To facilitate easy extension, I'll design the system with modular architecture, allowing for straightforward addition of new sportsbook modules. Alerts will be configured to notify you via your preferred channel when discrepancies exceed the set threshold. Additionally, a lightweight web dashboard or CLI summary will be developed for easy monitoring of the latest findings. My experience with similar projects ensures I can deliver a solution that is not only functional but also scalable and easy to maintain. I am eager to bring my expertise to your project and look forward to discussing how we can achieve your goals efficiently. Best Regards,

@ShaikhAneesa

Hi there, I understand you're looking to build a real-time value betting platform that compares probabilities from Kalshi and Polymarket against live sportsbook odds to identify positive expected value opportunities across Football, Basketball, Tennis, Soccer, and Baseball. With experience in Python, APIs, web scraping, and real-time analytics, I am confident I can deliver a scalable solution optimized for accuracy, speed, and future expansion. My approach is to develop a modular Python application that collects live market data from Kalshi, Polymarket, and sportsbook APIs or approved scraping sources, converts every price into standardized implied probabilities, and continuously compares markets using configurable EV thresholds. I'll implement provider-specific modules for easy expansion, robust retry logic, error handling, and detailed logging. Alerts will be delivered through Telegram, Slack, or email whenever profitable discrepancies are detected. The solution will also include a lightweight CLI or web dashboard, clean documentation, and a flexible architecture that makes adding new sportsbooks straightforward while ensuring reliable cloud deployment. Could you clarify which sportsbooks you want integrated initially, and do you already have API access to any premium odds providers, or should the solution rely on publicly available APIs and approved scraping where required? I'm ready to start immediately. Warm Regards, Aneesa.

@devendrathakur12

Hello I have gone through your specific requirement for betting probability monitor. I would use asyncio over threading because dozens of live feeds spend most time waiting on network replies. I will build a Python service that pulls Kalshi, Polymarket and sportsbook odds, converts them to implied probability and sends Telegram alerts, at least that is where I would start. Using httpx and Redis for refresh and caching. Built market data pipelines for finance clients with live feeds. Code samples and benchmarks I can share. Which sportsbooks matter most in your region? Need to clear API access and refresh rate first. Free for a quick call this week? Dev S.

@naminghn

Hi there, I can implement an alert scanner that smoothly fetches event-level odds and probabilities from Kalshi and Polymarket, while providing a clean interface for comparing these against sportsbooks. Given your focus on diving into positive-expected-value opportunities, I can ensure the architecture supports easy module swapping for various sportsbooks, while also creating alerts through email or Slack based on your defined thresholds. I'm confident in utilizing Python for this task, coupled with strong data parsing skills, ensuring that the system runs efficiently on standard cloud setups. Please share any specifics on the sportsbooks you want to include and let's get started on this project. Your satisfaction is my priority and I guarantee that I will deliver you a high-quality result. Regards, Ali

@rbtech1984

Hello!! Understood that you need a Python based tool pulling live probabilities from Kalshi and Polymarket, comparing them against sportsbook lines across football, basketball, tennis, soccer and baseball, flagging positive expected value gaps beyond a set threshold, then alerting through email, Slack or Telegram with a simple dashboard or CLI summary. To plan this properly, please share: - Do you already have API access for Kalshi, Polymarket and the sportsbooks, or should sourcing be researched first? - Which alert channel would you prefer between email, Slack or Telegram? - Should the first milestone be a working CLI report or a basic web dashboard? Have worked on similar odds comparison and real time alerting projects before, structuring modules for easy expansion while keeping probability conversion accurate and alerts firing reliably without unnecessary noise. Let us chat and lock in stack, data source strategy and milestone timeline quickly. Best regards Farhin B

@HadiDar786

Hi, I can build a modular tool that continuously aggregates data from Kalshi, Polymarket, and major sportsbooks, converts all odds into comparable implied probabilities, and identifies positive EV opportunities with configurable alerts. The solution will be scalable, well-documented, and include a lightweight dashboard or CLI, along with automated notifications via Slack, Telegram, or email, making it easy to extend with additional sportsbooks in the future. You can message me so we can proceed further. Thanks.