Sports Data Engineer / Historical Sports Data Curation

@ZohaibRoy

⭐⭐⭐⭐⭐ Build Clean Historical Sports Data for Validation & Backtesting ❇️ Hi My Friend, I hope you are doing well. I reviewed your project requirements and see you are looking for a Sports Data Engineer. You need someone to create structured historical datasets, and Zohaib is here to help! My team has successfully completed 50+ similar projects for data collection and validation. I will ensure high-quality data by focusing on accuracy and proper documentation. ➡️ Why Me? I can easily do your historical sports data project as I have 5 years of experience in data engineering, sports data management, and data validation. My expertise includes Python programming, data analysis, and spreadsheet organization. Not only this, but I also have a strong grip on APIs and data structuring, ensuring efficient delivery of your project. ➡️ Let's have a quick chat to discuss your project in detail and let me show you some of my previous work. Looking forward to our conversation! ➡️ Skills & Experience: ✅ Data Engineering ✅ Historical Data Collection ✅ Data Validation ✅ Python Programming ✅ API Integration ✅ Spreadsheet Organization ✅ Data Structuring ✅ Data Analysis ✅ Quality Assurance ✅ Documentation ✅ Data Cleaning ✅ Sports Data Management Waiting for your response! Best Regards, Zohaib

@ShaikhAneesa

Hi there, I understand you need a historically accurate MLB dataset for validation and backtesting where every record reflects only the information available before first pitch, while post-game results remain isolated for grading. I am confident I can deliver a research-grade dataset suitable for reliable model validation. My approach will be to build the dataset using a multi-source validation pipeline rather than relying on a single provider. Using Python (Pandas, Requests, BeautifulSoup), APIs where available, and Excel, I'll normalize all fields into a canonical schema, timestamp each record against game start time, and reconcile discrepancies between MLB, Baseball Reference, FanGraphs, Covers, OddsPortal, Rotowire, and weather sources. I'll preserve both opening and closing market snapshots, validate probable/confirmed lineups and starting pitchers against historical reports, capture bullpen usage from prior games, and document every field with its source. Missing values will be explicitly flagged, never inferred, and automated validation rules will identify inconsistencies before delivery to ensure the dataset is suitable for downstream backtesting. The deliverable will be a validated one-row-per-game historical MLB dataset with complete source attribution, missing-data flags, and a structured Excel/CSV output ready for model validation. Do you have a preferred MLB season or historical date for the paid sample? Warm Regards, Aneesa.

@onlinejob247

Hello, I have thoroughly reviewed the project requirements for the Sports Data Engineer position at ISO Sports Intelligence. I understand the need for clean, structured historical datasets for validation and backtesting purposes. Let's chat and discuss it further. To handle your project, I will start with curating a historical MLB slate with the required fields in a one-row-per-game format. I will ensure accuracy, document all sources, and flag missing information instead of guessing. My expertise in sports data, spreadsheet organization, and Python will be instrumental in completing this task effectively. The deliverables for this project include a well-organized historical MLB dataset with all the necessary fields and information for validation.

@w3saddam

Hello, I'm interested in your Sports Data Engineer project. I have experience with sports data research, web scraping, Excel, Python, and data validation. I can deliver clean, accurate historical MLB datasets with documented sources and clearly flagged missing data. I'm ready to complete the paid sample and can start immediately. I look forward to working with you. Thank you!

@EightVeer

Hi there, We will deliver a validated historical MLB sample slate from one date, with 5–10 games in a one-row-per-game format, source documentation, and missing-data flags where information is unavailable. We will focus on pre-first-pitch fields such as opening and closing lines, starting pitchers, confirmed/probable lineups, weather, and ballpark status, while keeping the final score separate for grading. We have public Freelancer review history covering AI adoption, data management and analytical engagements. The proposed initial deliverable is data review, quantitative analysis and prioritised findings for Sports Data Engineer / Historical Sports Data Curation; any subsequent implementation would require a separate Freelancer milestone. Best Regards, 8veer

@TanvirKhanPaple

Hi there! Project is very clear to me and I can build a clean, structured historical MLB dataset with documented sources, validated pre-game information, final scores, and clear missing-data flags. I have experience organizing sports datasets for analysis, backtesting, and quality validation with strong attention to accuracy. Just message me I am ready to start now and i will show you few data sample before start. Thank you.

@tmoi06

Hello Sir, I can build clean, well-structured historical sports datasets for validation and backtesting by carefully collecting, verifying, and documenting every data point while maintaining complete transparency about missing information. ✅ Why Me? ✔ Extensive experience in data research, data validation, spreadsheet organization, and structured database creation ✔ Proficient in Python, Excel, Google Sheets, APIs, and data processing for large historical datasets ✔ Strong attention to detail to verify odds, lineups, weather, injuries, and game results from reliable sources ✔ Deliver clean, one-row-per-game datasets suitable for validation, analysis, and backtesting workflows ✔ 500+ projects completed | 5.0-star rating I'll prepare the historical MLB sample exactly in your required one-row-per-game format, capturing pre-game information such as opening/closing odds, starting pitchers, probable lineups, weather, bullpen usage, injuries, and ballpark conditions, while recording the final score separately for grading. Every field will be sourced from reliable historical records, clearly documenting the source used, and any unavailable information will be explicitly flagged rather than inferred. I'm happy to complete the paid MLB sample (5–10 games) first so you can validate the dataset quality, formatting, and documentation before moving forward with the larger MLB and NFL historical data projects. Best regards, Ayan

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@DeveloperStation

Hi, The trickiest part here is not the pull, it's the point-in-time discipline. Opening and closing lines drift, lineups get confirmed late, and injuries update through the day, so the row has to reflect only what was known before first pitch. I'd anchor every field to a timestamped source and flag anything I can't verify rather than infer it. I build Python data pipelines with API pulls and web scraping, plus source documentation. For the sample I'd deliver one MLB slate in one-row-per-game format with a data source column and explicit missing-data flags, so you can validate every cell against origin. One question: for opening lines, do you want the earliest posted number or a fixed reference book? Adil

@corpmember29

Hello!! I have seen your project post as you need a Sports Data Engineer to collect, validate, and structure high-quality historical MLB datasets for backtesting and sports intelligence. I have 10+ years of experience in Python, data engineering, API integrations, ETL pipelines, SQL, data validation, and large-scale dataset processing. I can build accurate, well-structured historical datasets by collecting pre-game information from reliable sources, validating data integrity, documenting every source, flagging missing values, and delivering clean one-row-per-game datasets ready for analysis and backtesting. I am experienced in data extraction, transformation, spreadsheet automation, and creating reproducible workflows that prioritize accuracy, consistency, and maintainability. I WILL PROVIDE 2 YEARS OF FREE ONGOING SUPPORT, COMPLETE SOURCE CODE, WE WILL WORK WITH AGILE METHODOLOGY, AND I WILL ASSIST YOU FROM INITIAL DATA COLLECTION THROUGH FINAL DELIVERY. I am ready to complete the paid sample and discuss the larger MLB and NFL dataset requirements. I eagerly await your positive response. Thanks, Christina

@topcreator100

Hello, With over a decade's worth of experience in data engineering, I know exactly what it takes to build clean, structured datasets that pass rigorous validation. I understand the importance of accuracy over speed, and my meticulous nature aligns perfectly with your project's emphasis on data quality. Alongside my comprehensive command of spreadsheet organization, APIs, and data engineering, I've also worked extensively with sports-related data throughout my career. This means I'm well-versed in curating and documenting sports datasets from various sources while also highlighting any missing information intelligently instead of resorting to guesswork. My knowledge extends beyond just data engineering and includes front-end, back-end, and mobile app development. This cohesive skillset will aid me greatly in translating your business needs into robust technical solutions. Additionally, my strong background in database design and performance tuning will ensure not just the accuracy but scalability of the datasets I'll produce for you. In terms of tools, Python is one of my fortes - so much so that I'm often regarded as a Python specialist. Ultimately, I'm excited for the chance to contribute to your project by not only delivering a high-quality sample but by building trust through consistent communication and meeting deadlines. If you desire a reliable partner who can usher your project from idea to production efficiently,strongly b Thanks!

@emilesd617

Hi, I am a sports data engineer with 8 years of rich experience in software development. I am familiar with Python, web scraping, APIs, Excel, data processing, data validation, data modeling, and data management. I can build the MLB sample in a clean one-row-per-game format, cross-check opening and closing lines against documented sources, separate pre-game information from final results, and clearly flag any unavailable data instead of estimating it. I'm an individual freelancer and can work in any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@TeraByte26

Hi, I will deliver one historical MLB slate with 5-10 games, including required fields like Date, Home Team, and Final Score. I commit to a 3-day turnaround within the $250-750 budget. Can I start with a sample game? Waiting for your response in chat! Best Regards.

@iautomationus

I have existing experience with sports data, sports betting, data remuxing and backtesting. I'm able to build historic datasets built from your source and format it according to your template. Can provide sample 1 day after hire. Questions: - Do you have a preferred source for opening/closing lines? Cost / timeline: Est cost: $600 Est turnaround time: 1-7 days Experience: Web Scraping, App scraping, Data Parsing, Lead Generation, Browser Automation, Api Integration About me: I'm a data scraping expert with a wide range of experience. My goal is to build stable and re-usable applications that work for you. Feel free to message me for any questions. --- - Scripts, solutions, frameworks - Over 300 scrapers completed

@trm66614

I am excited about the opportunity to work with ISO Sports Intelligence as a Sports Data Engineer. Your project focuses on building clean, structured historical datasets, which perfectly aligns with my expertise in data quality and organization. I understand that accuracy is paramount for your needs, especially when it involves crucial information such as Opening/Closing Moneylines, Run Lines, and other relevant data fields for MLB games. I have a strong background in sports data curation and am proficient in using Python and APIs for data extraction and organization. Moreover, I prioritize meticulous documentation of sources and appropriate flagging of missing information, ensuring the dataset remains reliable and useful for validation and backtesting. As part of the sample phase, I am keen to deliver a comprehensive historical MLB slate according to your requirements to demonstrate my capability. I would like to ask: Could you clarify the specific date range for the historical MLB slate you need?

@ImranAli01

Hello, I got that you need a validated historical MLB slate with game-level pre-game data, including opening and closing odds, pitchers, lineups, weather, bullpen usage, injuries, and final scores, while clearly separating information available before first pitch. This is what I can help you with, let's chat. My approach is to use Python with structured web/API data collection, then normalize each source into a one-row-per-game Excel dataset with documented source links and missing-data flags. I’ll cross-check critical fields such as odds, lineups, pitchers, and weather across reliable sources and keep final scores isolated for post-game validation. Most importantly, I’ll timestamp and verify the information against the historical game date so no post-game knowledge contaminates the pre-game dataset. As final deliverables you will receive the 5–10 game MLB sample, Excel-ready structured data, opening/closing market fields, starting pitchers, lineups, weather, roof status, bullpen usage, injuries, final scores, source documentation, and explicit missing-data flags. I’d be happy to complete the paid sample carefully and discuss the larger MLB/NFL dataset afterward. Best Regards, Imran

@rezamahi

I’d be happy to complete the paid sample and build a clean, validation-ready historical MLB dataset with accuracy as the top priority. I have strong experience collecting, cleaning, validating, and organizing structured datasets using Python, spreadsheets, APIs, and web research. For the sample, I will deliver a one-row-per-game dataset (5–10 games from the same historical date) containing all requested fields, including opening/closing odds, run lines, totals, starting pitchers, confirmed/probable lineups, weather, ballpark/roof status, bullpen usage, injuries, final score, documented data sources, and missing-data flags. Any unavailable information will be clearly marked instead of estimated, ensuring the dataset reflects only pre-game information available before first pitch, with the final score separated for validation. I pay close attention to consistency, source documentation, and data integrity, making the dataset suitable for backtesting and historical validation. My workflow is reproducible, well-organized, and designed to scale efficiently for larger MLB and NFL historical data projects after the sample is approved. I’m ready to begin immediately and can provide a high-quality sample for review. I look forward to working with your team.

@hirujiyu

Hi, Based on your requirements, I have experience working with structured datasets, API integrations, data validation, Python automation, and building clean historical datasets where accuracy and traceability are critical. Key task I will implement :- ✅ Historical sports data collection and normalization ✅ One-row-per-game dataset structure with documented sources ✅ Pre-game vs post-game data separation to avoid look-ahead bias ✅ Missing data flagging and validation checks ✅ Python-based data extraction, cleaning, and quality assurance workflows ✅ CSV, Excel, and database-ready outputs ➥ A few quick questions: 1. Do you already have preferred data providers/sources, or should I recommend the best options? 2. What output format do you prefer (CSV, Excel, JSON, or database import)? 3. Will future phases require automated collection pipelines or manually validated datasets only? ♾️ That's all for now. I can commence immediately. I am open to a chat to proceed forward with the next step. Thank You.

@mayr81

ISO Sports Intelligence needs decisive historical sports data engineering focused on data quality for validation and backtesting. I will deliver the Phase 1 paid sample: one historical MLB slate (5-10 games) from a single historical date in strict one-row-per-game format. Fields will be populated only from pre-first-pitch information; final scores will be included separately for post-game grading. I will document every data source, preserve opening vs closing values (moneyline/run line/total), and capture starting pitchers, confirmed/probable lineups, weather, ballpark/roof status, bullpen usage, key injury information, and data-source provenance. Missing items will be handled via explicit missing-data flags rather than estimates. Python-assisted cleaning and validation logic will enforce consistent team naming, date integrity, and schema conformance so your backtesting pipeline can trust the dataset.