Player Technical Performance Analysis

@chirgeo

Turning raw event logs and video timestamps into per-player passing and shooting breakdowns is exactly the kind of work I do regularly, so the scope here is clear to me. 1. What format are the event logs in — Opta F24/F7 XML, Wyscout JSON, a custom CSV export, or something else — and do the events already include coordinates, or will spatial data need to be manually tagged from the video? 2. How many players are being assessed, and should each player get a standalone section in the report or a comparative layout that ranks them against one another? 3. For xG per shot, do you have a preferred model already (e.g., a specific provider's values embedded in the feed), or should I build a simplified model from the shot attributes present in your data? 4. The deliverable includes reusable scripts — is the priority a clean, well-commented Python workflow someone can run locally, or does it need to sit inside a shared environment like a Google Colab notebook so non-coders on your staff can re-run it without setup? Share those details and I can map out the exact approach before we kick off.

@HelpingHut

Technical performance analysis on match footage requires precise event extraction and statistical rigor—especially when coaches need actionable intel from passing and shooting metrics. This project sits squarely in data-driven performance evaluation. The workflow here is standard: parse event logs (Opta, Wyscout, or raw CSV), calculate completion rates, progressive passes, key-pass frequency, shot accuracy, and xG per shot, then structure findings in a coach-ready format. Python or R will handle the calculations; Tableau or Power BI can surface the key visuals without noise. Deliverables are clear: cleaned dataset (Excel/CSV), reproducible scripts, a concise PDF or Google Slides report with charts that matter, and the underlying logic documented so you can extend or modify the analysis later. One week is realistic for match-level analysis with a round of clarification questions built in. Ready to start immediately once match video, event logs, and lineup data are shared. This is straightforward technical work with measurable outputs.

@nrajuu07

I'll process your match footage and event data to extract every pass and shot for the selected players, calculating completion rate, progressive pass count, key-pass frequency, shot accuracy, xG per shot, and supporting technical indicators. Deliverables include a concise coach-friendly PDF/Slides report highlighting strengths, weaknesses, and actionable tweaks, a cleaned CSV/Excel dataset, and reusable Python/R scripts or a Tableau/Power BI dashboard for future use. Comfortable with Opta-style feeds and Wyscout exports. Turnaround: within one week.

@Adnan777

Hi, I have solid experience in data analysis as well as visualization using python language and I have successfully handed over data analysis based projects with 5 star review. see my portfolio: https://www.freelancer.com/u/Adnan777 Inbox me for detailed chat. Thanks.

@amgadmourad18

Hello , I'm an AI Engineer with 4 years of experience. i have done your project before as the graduation project from my college so i can done it perfectly and even much more . if you are ready to start drop me a message please.

@vishal1145

Your event data will produce misleading insights if the coordinate system isn't normalized before calculating progressive passes. Most Opta and Wyscout feeds use different pitch dimensions, which means a 15-meter forward pass in one dataset might register as 12 meters in another. This throws off your xG models and progressive action counts. Before I map out the analysis pipeline, I need clarity on two things. First, what's the source format of your event data - are we working with Opta F24 XML, Wyscout JSON, or StatsBomb-style feeds? The parsing logic changes completely depending on the schema. Second, do you have xG values already baked into the shot data, or am I building the model from scratch using shot location, body part, and defensive pressure variables? Here's the technical workflow: - PYTHON + PANDAS: Parse event streams, filter pass/shot events by player ID, and normalize coordinates to a standard 105x68 meter pitch to ensure accurate distance calculations. - STATISTICAL MODELING: Calculate completion rates with confidence intervals, identify progressive passes using a 10-meter forward threshold, and compute xG per shot if values aren't pre-supplied (I'll use a logistic regression model trained on 50K+ historical shots). - DATA VISUALIZATION: Build 2-3 matplotlib or seaborn charts per player showing pass maps with completion zones, shot locations color-coded by xG, and a radar chart comparing technical output to positional benchmarks. - DELIVERABLES PACKAGE: Export cleaned CSV with every pass/shot tagged by outcome, Python scripts with inline comments for reproducibility, and a 5-page PDF report structured as strengths/weaknesses/recommendations per player. I've done this exact workflow for two academy programs analyzing U-19 match performance, where we reduced scouting review time by 60% by automating the technical breakdown. The coaching staff specifically valued having the raw scripts because they could re-run analysis on new matches without waiting on external consultants. I don't take on projects where the data quality is unknown upfront. Let's do a 10-minute call where you share a sample of the event logs so I can verify the schema and confirm turnaround time before we lock in scope.

@mayankinnovative

Hi, As per my understanding: You need a detailed technical performance analysis of selected football players using match footage and structured event data. The focus is strictly on passing and shooting output, including metrics like pass completion, progressive passes, key passes, shot accuracy, xG per shot, and other technical indicators. You also need a coach-friendly report, cleaned datasets, and reusable analysis assets such as scripts or dashboards for future extension. Implementation approach: I will combine the match footage with your event logs and lineup data to validate and enrich all passing and shooting actions for the selected players. Using Python-based analysis workflows, I will calculate technical KPIs, identify efficiency patterns, and generate concise visual summaries focused on actionable coaching insights rather than unnecessary tactical commentary. The final delivery will include a PDF or Google Slides report, cleaned CSV/Excel datasets, reproducible scripts, and optional Tableau/Power BI dashboards for future scalability and internal analysis. A few quick questions: 1. Which event data format are you providing (Opta, Wyscout, StatsBomb, CSV, JSON, etc.)? 2. How many matches and players should be included in this first analysis? 3. Do you already have preferred definitions for progressive passes and key passes? 4. Would you like comparisons against team averages or only player-specific evaluations?

@Ekarthaan

Hi Mahamat H., Last week I did a very similar analysis for two midfielders using Wyscout + video, so I’m confident to handle this really well. i would like to know the below. - What exact event format do you have (Opta F24/StatsBomb/Wyscout CSV/JSON) and are timestamps synced to the match video? - Do you want your own definitions for “progressive pass” and “key pass,” and should I use your xG model or my standard logistic xG (location + body part + assist type)? I think we should. - Lock metric definitions in a short data dictionary + config so future matches are one-click reproducible and consistent. - Add automatic QA checks (player IDs, sequence order, timestamp gaps) and spot-check 5–10% events vs video to ensure accuracy. Small cost, big reliability. Lets follow a plan like this. - I ingest and normalize your event logs to a clean schema, map players, and sync to video time. - I extract every pass/shot per selected players and compute completion rate, progressive passes, key-pass freq, shot accuracy, xG/shot, plus on-ball loss after pass/shot. - I validate edge cases with quick video checks and fix any parsing issues fast. - I deliver a clear coach-friendly PDF/Slides with a few sharp charts, the cleaned CSV/Excel, and reproducible Python scripts (optionally a lightweight Tableau/Power BI view). May I know if you are the project owner or part of the direct client team? I usually work directly with the customer and dont engage through middle parties

@alizain721

Most player-analysis requests fail because they treat event logs and video as separate tasks; the real problem is aligning each pass and shot to a single, verifiable timeline so every technical metric is reproducible and auditable. I’ll map every pass and shot in your footage to the event feed, compute completion rate, progressive passes, key-pass frequency, shot accuracy, xG/shot, and a couple of robust technical indicators (pass danger index, shot placement buckets). I’ll present a concise coach-friendly slide/PDF with bullet insights and 1–2 charts per player, plus the cleaned CSV/Excel and the analysis scripts so you can reproduce or extend the work. Preferred stack: Python (pandas, numpy, scikit-learn or a lightweight xG model), OpenCV/ffmpeg for timestamp alignment if needed, and optional Tableau or Power BI for the dashboard. I’ll document assumptions and provide reproducible scripts/notebooks. Delivery: complete package within 1 week for $500, with a short follow-up for tweaks. Similar work: built an ETL + scoring engine for CrowdAxis that normalized multiple feeds, produced repeatable metrics, and shipped dashboards and API endpoints for coaches/stakeholders. Ready to start once files are shared. Are your event logs Opta/Wyscout-style exports (CSV/JSON) or a custom format?

@revival786

Greetings! I will analyze your match footage and raw event data focusing on passing and shooting accuracy for selected players. I will extract every pass and shot, then calculate completion rate, progressive pass count, key pass frequency, shot accuracy, and xG per shot. Findings delivered in a concise coach friendly PDF or Google Slides report with strengths, weaknesses, and actionable tweaks. I will also supply the cleaned dataset as CSV or Excel plus any Python scripts used. I am comfortable with Opta style feeds and structured data. Turnaround within one week. Thanks, Revival

@Microlent

Read your brief about turning match footage and raw event data into technical player assessments. I see you need to focus strictly on shooting and passing metrics for individual players while skipping the broad physical stats. Raw event logs from sources like Opta or Wyscout can be incredibly dense. It is often hard to bridge the gap between that raw data and something a coach can actually use on the field. I will build a Python workflow that cleans your event logs and calculates specific technical indicators like progressive passes and xG. My goal is to turn that unstructured data into a repeatable process so you get a clear report without the manual headache. I definitely understand the one week deadline. I will make sure the scripts and datasets are handed over so you can run this again yourself later. Reference work: https://www.freelancer.com/portfolio-items/11349812-web-scraping-automation Are you free for a 15-min call to lock the scope? I am free Tue/Thu in your timezone. ~ Rajesh

@NayaPakistan

Hello, I can help analyze your match footage and event data to provide a clear, numbers-driven assessment of player technical output. My approach involves extracting all passes and shots, calculating key technical indicators, and presenting findings in a concise, coach-friendly report with a cleaned dataset and scripts. Could you confirm the preferred format for the coach-friendly report (PDF or Google Slides)? Regards, Muhammad Azeem

@Deepikaparihar4

Hello, I am a certified tableau desktop and expert data scientist specialist having over 4 years of experience in creating powerful and interactive dashboards for data analysis and insights generation. I can perform the data cleaning and transformation using statistical method and advanced features to gain insights from data. I have worked on projects of various domains and I can guarantee an insightful dashboard according to the requirements. I offer best practices of business intelligence with the help of BI tools like tableau and power BI , having experience in creating goal driven dashboard for strategic business decisions. My area of expertise is the analysis, design, development and deployment/ publishing of dashboard reports to cater to the business requirements and troubleshooting needs. I offer reliable services, check my portfolio to know more :)

@Georgeoustar

Hi, I’m an experienced football data analyst with strong experience working with match video, event logs, Opta-style data, Wyscout-style exports, Python analysis, and coach-friendly player reports. I can turn your match footage and raw event data into a clear technical assessment focused on passing and shooting output. I’ve done similar football analysis projects where I extracted and cleaned pass and shot events, calculated completion rates, progressive passes, key passes, shot accuracy, xG per shot, shot locations, pass direction, final-third involvement, and player-level technical strengths and weaknesses. For your project, I can use the event logs and lineup data to isolate selected players, validate key moments against the video where needed, and produce a concise report with practical coaching notes. I’ll deliver the cleaned CSV or Excel dataset, calculation scripts in Python or R, and a PDF or Google Slides report with simple charts that highlight performance clearly without overcomplicating the analysis. Best regards, George

@goranm16

Hi, this is a good fit for me because the real job is turning raw match events into a clean, repeatable analysis workflow — not just making charts. I’d start by checking the event log against the video for the selected players, then build a Python pipeline to clean passes/shots, calculate the key metrics, and output both the dataset and a coach-friendly report. The main risk is misclassifying actions or using unclear metric definitions, so I’d lock the definitions early and keep the calculations transparent in the CSV/scripts. For example, I’ve worked on messy data/reporting workflows where the useful result came from cleaning inconsistent raw inputs first, then producing a simple report stakeholders could actually use. I can keep the visuals practical: completion rates, progressive passes, key-pass frequency, shot accuracy, xG per shot, and clear notes on strengths/weaknesses with actionable tweaks. Thanks!

@Vsion2

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@SolutionMart

Converting raw match event data into actionable technical metrics requires precise extraction logic and statistical rigor—many analysts miss nuances in pass progression classification or xG calculation methodology. This project sits directly at the intersection of data processing, statistical analysis, and sports performance reporting. The workflow here is straightforward: parse structured event logs (Opta, Wyscout, or equivalent), validate data integrity, calculate completion rates and progressive pass thresholds, quantify shot accuracy against expected goals models, then surface insights through Python or R automation. All cleaned datasets, replicable scripts, and visual dashboards will be version-controlled and documented so the coaching staff can iterate independently. Delivery cadence is one week with follow-up rounds built in. Quality hinges on calculation accuracy and clarity—flashy visuals take a backseat to numbers that hold up under scrutiny. Have handled similar football analytics projects with Wyscout exports and custom event feeds; the methodology scales across different data structures. Ready to start immediately upon receiving match footage and event logs.

@johnedwardl

Hi, I will extract the key passing and shooting data from your match footage and event logs, focusing on metrics like completion rates, progressive passes, and shot accuracy. With extensive experience in sports analytics and working with Opta-style feeds, I'm confident in delivering precise and actionable insights that meet your requirements. My approach will involve a thorough analysis of the footage, followed by calculations of relevant technical indicators. I will compile the findings into a clear, concise report that highlights each player's strengths and weaknesses. Additionally, I will provide the cleaned dataset in CSV format along with any scripts or dashboards used to ensure you can replicate or extend the analysis as needed. I understand the importance of accuracy and clarity, and I can deliver everything within one week of kickoff. Once we start, I’m ready to address any follow-up questions you have regarding the calculations or visual emphasis. Thank you.

@HNamdar

Your project requires extracting and quantifying technical output from match footage and raw event data, zeroing in on passing and shooting accuracy for each player. This level of focused analysis, covering completion rates, key-pass frequency, xG per shot, and progressive pass counts, is exactly the kind of numbers-driven work I do regularly. With 13 years of experience in data analysis and a PhD research background, I work across Python, R, and Excel to process raw sports datasets and produce clean, coach-ready summaries. I have handled event-level data before and can structure the findings in whatever format works best for your review process. Happy to discuss the dataset format and timeline to get started.