Collect ATP Tennis Statistics via Web Scraping

@imraz2016

Hi There, I have strong experience with Python, web scraping, sports data extraction, and Excel automation. I can build the dataset match-by-match, carefully matching each player to the correct TennisTourData record and validating similar/same-surname players manually where needed. For TennisTourData, I would use Python with requests/BeautifulSoup where possible and Selenium/Playwright only if the comparison pages require dynamic interaction. For actual match aces, I would use a reliable match-statistics source such as Flashscore or another source that provides complete per-match ace data, then cross-check questionable records. I can provide the requested 5–10 match sample first, include Excel formulas for Aces/Match and Ace Against/Match, add a Sources sheet, and provide the Python code as well.

@ShaikhAneesa

Hi there, I understand you need a highly accurate Excel dataset covering approximately 450–500 ATP 2026 main-draw matches, linking each match to the correct players, TennisTourData ace/return statistics, and actual match aces. I am confident I can build this as a reproducible Python scraping and data-matching workflow rather than relying on manual copy-paste. My approach is to first build and validate the process on 5–10 matches, ensuring player names are matched correctly, including duplicate surnames and initials. I’ll then automate extraction from TennisTourData using the required Last 52 Weeks, Hard, Main Draw settings, calculate the per-match metrics with Excel formulas, and collect actual match aces from a reliable results/statistics source. Each record will be cross-validated before being added to the final workbook. I’ll structure the Excel file exactly around your 22 required columns, with a separate Sources sheet containing the URLs used. I’ll also include the Python source code, so the workflow remains transparent and reusable for future tournaments or seasons. Would you prefer the initial 5–10 match validation sample to be delivered first for approval before I process the complete dataset? I’m ready to start immediately. Warm Regards, Aneesa

@Tanmoy236

Warm greetings! I’m an expert in Python web scraping, data extraction, and Excel automation, with over 9 years of experience handling large, accuracy-critical datasets. Here's how I can help: * Scrape TennisTourData using the exact filters and player matching required * Collect actual match Aces from a reliable tennis statistics source * Build the 22-column Excel dataset with formulas for calculated fields * Add a Sources sheet with URLs and validation checks * Test 5–10 matches first before scaling to all 450–500 matches * Provide clean Python scraping/processing code and documentation I can offer a fixed-price quote after reviewing the 5–10 match test scope.

@hassannaweed4

As a data ninja with a solid grasp on web scraping and data extraction, I bring to the table a wealth of experience that validates my ability to meet your meticulous project needs. I can reliably scrape the Tennis Tour Data site for ATP tennis statistics across 450-500 matches, with a keen eye on accurate player matching—a particular challenge when it comes to repetitive or similar names. My skills in Python, automation and data management will ensure an error-free dataset that correctly links match, players, statistics and actual match Aces. For your unique project, I propose using selenium and BeautifulSoup to accurately gather ATP men's singles main-draw matches from the various tournaments in 2026. This way, I will be able to handle player names accurately giving the importance of initials in some cases. Furthermore, I will incorporate Excel formulas to calculate derived statistics like "Aces/Match" and "Ace Against / Match" as per your request. Regarding actual match Aces data extraction, my experience suggests sources like Flashscore or TennisTemple guarantee up-to-date and reliable information. In line with my reputation for excellence and practical solutions, my best fixed-price offer for your project is $100 as well as providing you with the Python code. With my dedication towards robust systems that don't just work but scale seamlessly, I estimate a timely delivery of 5 days that won't compromise on the 100% accuracy needed for your project.

@kelvinm094

Hello, I’ve reviewed your ATP stats scraping brief and I’m confident I can deliver a clean, Excel-ready dataset. With a statistics background and Python-driven data extraction, I’ll build a robust workflow to pull 450-500 main-draw matches across the nine events, normalize player names (including initials to distinguish siblings), and output a single, well-structured workbook. I will apply Web Scraping, Data Mining, and Data Management practices to ensure accuracy and repeatability. I will implement data plumbing for TennisTourData - Aces and Aces Against, compute Aces/Match and Ace Against/Match, and populate the actual match Aces with reliable sources for verification. A test run on 5-10 matches will validate accuracy across all fields before full delivery; a provenance sheet with URLs can be included. Next steps: I can begin immediately and deliver within 3 days. Best regards, Freelancer

@hiberokpars

Hello! I will create a PHP script to scrape data you need I have extensive experience in writing PHP scripts for Tennis data scraping Please see my reviews for reference.

@maxscender

I can help you. I’ll treat data accuracy and player matching as the core problem. My approach: normalize player names, handle same-surname players like the Cerundolos by using first initials plus match context, and build a clean join between match-level aces and TennisTourData stats. For TennisTourData, I’ll automate the compare URLs with the correct settings, parse the stats tables directly from the page, and add validation/retries so missing or mismatched player rows are caught. For actual match aces, I’ll pull from Flashscore match pages, using TennisTemple as a fallback, and cross-check player names against the schedule to avoid mismatches. The Excel output will have one row per match with your exact column structure, formulas for calculated fields like Aces/Match and Ace Against/Match, and a second Sources sheet with URLs. I’ll validate the pipeline on 5–10 matches first so you can confirm the matching is solid before the full run. Yes, I’ll provide the Python code plus the final Excel file.

@sagarwal3005

Collecting ATP tennis statistics into an Excel file requires accurate web scraping to ensure that data is structured precisely as you need. I will utilize Python libraries like BeautifulSoup or Selenium to extract player statistics from the TennisTourData website while ensuring the proper entry of player names, especially for those with similar surnames. The extracted data will be meticulously organized into the specified Excel structure for straightforward analysis. I specialize in web scraping and Excel automation. With a 4.9-star rating across 200 client reviews and 220 projects completed, I have a proven track record of delivering high-quality results. Which specific dates for the matches need to be targeted for accurate data extraction?

@TeraByte26

Hi francoisalex, I'll deliver 450-500 ATP men's singles main-draw matches with required statistics from TennisTourData and actual match Aces. I'll complete this within 3 days for 150 EUR. I can start now, do you want a test on 5 matches first? Waiting for your response in chat! Best Regards.

@muneeb6767

I can build this Python scraping workflow with accurate player matching, TennisTourData extraction, actual match-ace verification, Excel formulas, and a dedicated Sources sheet. I’ll first validate 5–10 matches end-to-end before scaling to the full dataset, and I’ll provide the reusable Python code;

@mayankinnovative

As an experienced Python developer with a strong background in web scraping, I am well positioned to successfully complete your ATP tennis statistic collection project. Over my 20 years of experience, I have honed the skill of precise data extraction and integration, qualities that are exceedingly important for your project given its inter-dependent nature. For TennisTourData scraping, I propose a multi-step process that ensures optimal accuracy. By using the comparison and sorting functions combined with the specific filters you've specified, I will scrape and compile not just individual numerical values but entire datasets that accurately and comprehensively represent the statistics you require.

@TheSecondSource

Hey! We are a team of 62 professionals specializing in Python web scraping and data extraction, with 9+ years of experience collecting structured sports data and delivering accuracy-focused Excel datasets. Here’s how we can help: * Scrape TennisTourData statistics with accurate player matching * Collect actual match Aces from reliable tennis sources * Build Excel formulas for calculated statistics and validation * Deliver a Sources sheet alongside reusable Python code Could you clarify which match-results source you prefer, or should we select the most reliable option?

@rosscarter1011

Hello, The difficult part here is not scraping the numbers. It is reliably joining three different datasets without silently matching the wrong player, tournament, or statistic. I would build the process around a normalized player identity and match key, then validate every match against the tournament draw before collecting TennisTourData statistics and actual match aces. Names such as the Cerundolo brothers would be handled using full available player identifiers rather than simple surname matching. Python would handle extraction, cleaning, validation, and Excel generation, with formulas retained for Aces/Match and Aces Against/Match. I would also maintain a Sources sheet so every external value can be traced back to its origin. I would start with the requested 5–10 match validation set and compare every field before processing the remaining matches. This catches source-specific formatting or player-matching issues early instead of discovering them after the entire dataset is built. I can also add validation checks for missing statistics, duplicate matches, unexpected player names, and inconsistent tournament data. Would you like the 5–10 match test to cover different tournaments? Should the final Excel contain one consolidated sheet plus the separate Sources sheet?

@ImranAli01

Hi there, I got that you are looking for an experienced Python/web scraping freelancer to collect and combine ATP tennis statistics into a clean Excel file for approximately 450-500 ATP men’s singles main-draw matches from various tournaments. This is what I can help you with, let's chat. My approach is to utilize Python for web scraping and data extraction to gather the required statistics from TennisTourData and other reliable sources accurately. By using custom scripts tailored to extract and organize the data efficiently, I will ensure that the final Excel file contains all the necessary information, including player statistics, match aces, and calculated fields. The most challenging part will be accurately matching players with similar names to their respective statistics. As final deliverables, you will receive a comprehensive Excel file with detailed statistics for each match, player, and specific metrics as outlined in the project description. One thing I'd like to confirm before we start: How would you prefer the data to be presented for matches with players having the same surname? Looking forward to discussing this project further with you. Let's connect. Regards, Imran

@toriquldev123

Hello There! I'm Md Toriqul Islam, and I'm excited to partner with you & I can dive into your project immediately. I'm a Python developer experienced in web scraping and data extraction, with a strong focus on accuracy when matching player and match level statistics. I understand you need ATP aces and return statistics scraped from TennisTourData for about 450 to 500 matches across 9 tournaments, combined with actual match aces from a reliable source, all matched carefully by player and structured into a formula driven Excel file with a sources sheet. I've handled similar sports data scraping and matching projects before. I am skilled in Python, BeautifulSoup and Selenium for scraping, data validation for accurate player matching, and Excel formula setup. I can start with a 5 to 10 match test, and share my approach, source for match aces, price, and delivery time once we connect. I'm ready to start right away and happy to discuss any details. Looking forward to hearing from you. Best regards, Md Toriqul Islam

@protoak

Hi, I've built similar sports statistics scrapers. Happy to match your budget for long-term collaboration. Ready when you are.

@mayr81

I can build an accurate Python-based web-scraping pipeline to compile ATP men’s singles main-draw match rows (450-500 matches across Montreal, Washington, Los Cabos, Miami, Indian Wells, Dubai, Acapulco, Delray Beach, Doha) into your Excel schema. Approach (Python/web scraping + matching): - Scrape TennisTourData “Aces” and “Aces Against” compare pages with exact player-name handling (including first initial) and consistent tournament/draw/surface/year parameters (Tour: All, Year: Last 52 weeks, Surface: Hard, Draw: Main). - Use a deterministic player-key strategy to avoid same-surname collisions (e.g., Cerundolo brothers) by matching on the full name string used by TennisTourData. - Generate Excel columns with formulas for Aces/Match and Ace Against / Match, plus Ace% from the scraped counts/percent fields. Actual match aces source: - Use Flashscore (match stats) as the primary source for per-match “aces per player,” then map to the same player keys. Excel delivery: - One row per match with correct linkage: Match → Players → TennisTourData Aces → TennisTourData Aces Against → Actual match Aces. - Second “Sources” sheet containing the exact URLs used. - Test run first on 5-10 matches, showing all fields and the matching logic before full extraction. I’ll provide the Python code alongside the deliverable so you can audit reruns and data provenance.

@demivision

Hi there, Employer, Thank you for sharing such a well-structured and detailed project brief. I completely understand the importance of data accuracy and precise player matching for ATP men’s singles main-draw matches from the specified 2026 tournaments. With over eight years of experience in Python development, web scraping, and data analysis, I have successfully delivered similar sports data extraction projects, ensuring both reliability and data integrity. For this project, I will leverage Python (using requests, BeautifulSoup, and Selenium where needed) to systematically extract the required player statistics from TennisTourData, carefully handling name disambiguation—especially for players with similar surnames. To ensure clean and accurate mapping, each match and player will be cross-referenced using unique identifiers wherever possible. For the match-specific ace counts, I recommend sourcing from Flashscore due to its comprehensive and up-to-date tennis stats, but I am also familiar with scraping from TennisTemple and can adapt based on your preferred source. My approach will involve automating the data extraction process, thorough validation at each stage, and compiling the results into an Excel file structured exactly as you described. Calculated fields such as Aces/Match and Ace Against/Match will be implemented with Excel formulas for transparency and ease of review. I will also provide a separate ‘Sources’ sheet listing all URLs used, supporting full traceability. As part of my standard process, I am happy to provide a sample of 5–10 matches to demonstrate accuracy and methodology before proceeding with the full dataset. The Python scripts used for scraping and data processing will be included, ensuring full reproducibility and future flexibility. I look forward to discussing how we can make this dataset as robust and reliable as possible. Thank you for your consideration!

@ianb65

Hi there, I can build the exact Excel dataset you need for ATP match aces with accurate player matching, TennisTourData scraping, and source tracking. I have strong experience with Python, web scraping, Data Analysis, and Data Visualization, and I’ll use Python to collect the 450-500 main-draw matches, normalize player names carefully, and cross-check every row against the tournament draw and stats pages. I’ll scrape TennisTourData for the hard-court last-52-weeks compare views, calculate Aces/Match and Ace Against/Match with Excel formulas, and create a clean workbook with a Sources sheet for every URL used. For actual match aces, I’d use Flashscore or TennisTemple, then validate any name collisions like Cerúndolo-style cases before delivery. I can also provide the Python code so you can rerun or audit the process. Thanks, Ian