Audit and Fix Football Prediction ML Pipeline

@AwaisChaudhry

As a highly experienced Machine Learning (ML) engineer, I applaud your emphasis on realism and transparency in football prediction systems, which aligns perfectly with my work philosophy. At ZAWN Tech, we've been delivering software solutions across various industries, including sports analytics. We've mastered the art of building scalable and robust API integrations, comprehensive data analysis strategies, and sophisticated ML models using diverse tools like Python and its associated libraries like scikit-learn, XGBoost/LightGBM/CatBoost/PyTorch—an ideal fit for your project needs. Beyond your outlined requirements, our team has gained deep insights into working with large datasets such as those you have in football prediction systems. This encompasses not only auditing the codebase but also identifying and troubleshooting complex model issues that may arise from initial training and testing stages—addressing them effectively is crucial to ensure accurate predictions. Moreover, our expertise includes backtesting capabilities—a critical tool in validating the effectiveness of models on unseen data using real odds.

@OutsourceMan

I sincerely appreciate your transparent approach in seeking an ML engineer who can provide honest, critical feedback on your football prediction project. As the head of CnELIndia, a premier web and app development firm with over 18 years of industry experience, I believe we are well-positioned to meet your needs. Our expertise in Python, PyTorch, scikit-learn, and FastAPI aligns seamlessly with the core elements of your project. At CnELIndia, we take great pride in our ability to meticulously audit existing codebases. We understand the potential implications of data leakage and incorrect training/testing procedures - issues you've specifically highlighted as a concern, and are keen to rectify. Our vast experience also enables us to adeptly handle feature alignment problems and resolve discrepancies that could be causing the repeated 95% confidence issue. Drawing on our understanding of football predictions, model validation, and probability calibration we can conduct a thorough analysis of your system's performance and make informed decisions on whether a rebuild is necessary. Additionally, we'll leverage our skills in API integration to ensure that you have reliable real odds from bookmakers through The Odds API integration for better betting analytics. Choose us because we're not just all about 'magic betting models,' we're tech experts who prioritize effective problem-solving tailored to specific client needs. gemeinsam wären wir unschlagbar!

@softsolution2000

I have thoroughly reviewed your project requirements and it seems like you need an experienced ML engineer to audit and potentially fix issues in your football prediction system. I am confident in my ability to address the current issues such as repeated predictions, feature alignment, and model failures. I can also verify API integrations, conduct backtesting, and provide a detailed report with recommendations. Rest assured, I will work within your budget and prioritize your project's success. Please review my extensive 15-year profile to see my expertise. Let's discuss the project details and get started right away.

@diektech

Hi, This is Elias from Miami. I checked your project description and understand you’re looking for an ML engineer to audit and potentially fix an existing football match prediction pipeline. It sounds like an exciting challenge to enhance the accuracy and performance of your model. I have experience in developing and optimizing machine learning pipelines, which includes working with predictive analytics and backtesting. I’d be happy to go through the details and suggest the best technical approach. I have a few questions to get a better understanding: Q1 – What specific issues are you facing with the current pipeline? Q2 – Are there any particular data sources or APIs you're currently using for predictions? Q3 – Do you have any existing user roles or permissions that need to be considered in the solution? Looking forward to hearing from you.

@LiveExperts

Hello, As a seasoned ML engineer and the leader of Live Experts LLC, I venture to say that our team has precisely what you're looking for. We have solid experience ensuring codebase integrity and auditing ML projects, a skill demonstrated by our thorough understanding of feature pipelines, data leakage, model validation, and backtesting. Our proficiency in Python and familiarity with key ML libraries like scikit-learn, XGBoost/LightGBM/CatBoost/PyTorch maps fittingly with your requirement for trained model artifact reviews. Moreover, we understand the true essence of football prediction as itinerated in your project description; probability calibration and the ability to grapple with 'unseen label errors' are close to our hearts at Live Experts. Buttressing these skills are a profound grasp of big data analysis which is pertinent for aligning features accurately and understanding data patterns behind real odds as well as calibration probabilities. We also believe that trust and communication are pivotal in any successful project and we strive to meet these standards consistently throughout our engagements. Ultimately, what makes our offering especially appealing is not just our broad array of technical capacities but also our deep commitment to the happiness and satisfaction of our clients. Whether it's identifying problems, devising solutions or managing project pipelines - we're equipped to handle all your requests efficiently Thanks!

@kamran2012

This looks like a great fit, I will audit your full pipeline — feature engineering, model training, and inference — then deliver a written report with root causes and fixes for each issue. The repeated 95% confidence almost certainly points to a calibration or postprocessing bug where probabilities are being clipped or a fallback path is triggered when feature columns mismatch. I will trace that path first, then reconcile the 103 vs 122 feature gap by diffing your training schema against the live API payload. Once alignment is fixed, I will run a chronological backtest on held-out fixtures with real Odds API lines to give you honest accuracy and calibration metrics. Questions: 1) Are the trained model artifacts versioned alongside the training scripts, or were they saved separately without the preprocessing pipeline? Ready to start whenever you are. Kamran

@ZohaibRoy

⭐⭐⭐⭐⭐ Expert ML Engineer to Audit Your Football Prediction System ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project needs and see you are looking for a skilled ML engineer to audit your football match prediction system. Look no further; Zohaib is here to assist you! My team has successfully handled 50+ similar projects, focusing on machine learning and model evaluation. I will carefully analyze your codebase, identify issues, and provide solutions or recommendations based on my findings. ➡️ Why Me? I can efficiently audit your existing football prediction system as I have 5 years of experience in machine learning, specializing in model evaluation, feature alignment, and data validation. My expertise includes Python programming, FastAPI, scikit-learn, and various ML models. Moreover, I have a strong grip on API integrations and probability calibration, ensuring a thorough approach to your project. ➡️ Let's have a quick chat to discuss your project in detail. I can share samples of my previous work and showcase my expertise in ML engineering. Looking forward to chatting with you! ➡️ Skills & Experience: ✅ Python Programming ✅ FastAPI Development ✅ scikit-learn ✅ XGBoost ✅ LightGBM ✅ CatBoost ✅ PyTorch ✅ Model Evaluation ✅ Feature Engineering ✅ API Integration ✅ Data Validation ✅ Backtesting Waiting for your response! Best Regards, Zohaib

@AITSoft

Hi there, I understand you want a thorough audit of your football prediction ML pipeline and clear, actionable fixes. I will review the Python/FastAPI setup, the feature pipelines, and the trained artifacts, verify data integrity, and check for leakage, calibration, and backtesting methods. I will assess the Odds API integration, feature alignment across live and trained models, and the reliability of odds sources. If salvageable, I’ll fix feature mismatches and refit/calibrate as needed; if not, I’ll provide a concrete rebuild plan that preserves proven components. Finally, I’ll deliver a concise report with findings, fixes, backtests, and recommendations, ensuring probabilities are calibrated and the analytics are trustworthy. I will present a practical approach in plain language: audit, diagnose, fix or replace, validate with backtests, and document clearly. What is the single most critical outcome you need from the audit (e.g., reliable calibration, ability to reuse model components, or a clear decision on rebuild vs fix) and are there any non-negotiable constraints I should know? Key questions I will ask you include data sources, model types used, how you generate and store features, logging and versioning, and your evaluation metrics for calibration and EV. These will guide the work without flooding you with irrelevant details. Best regards, Shamshad

@intelliresponse

With over 13 years of industry experience, I am confident in my ability to not only audit but also recommend the best solution for your football prediction system. Having successfully undertaken a variety of web automation projects, I am well-versed in aligning features and treating complications that arise in live predictions due to changing dynamics. My high proficiency in Python, PHP and FastAPI, matches well with this project’s requirements. Similarly, my extensive knowledge of scikit-learn, XGBoost/LightGBM/CatBoost/PyTorch models allows me to comfortably assess your already built models and identify broken links. I share your vision of incorporating realistic expectations with the ML project. My approach is always focused on giving honest technical feedback while keeping the clients' goals in perspective. Therefore, I believe that with my skill set and commitment towards finding dependable solutions, I can offer what you require: a comprehensive analysis alongside feasible fixes for your football/soccer match prediction system. Excited about the opportunity to work with you!

@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

@teodorgeorge

Hello, I can take a clear and honest look at your football prediction system and go through the models, features, and odds integrations to pinpoint why the predictions look off. I’ve worked with similar pipelines before, so checking feature mismatches and suspicious confidence spikes will be straightforward. I can review the Python/FastAPI code, the trained model files, and the data flow to see what’s salvageable and make the system’s predictions more trustworthy. Once I sort out the issues, I’ll summarize everything clearly in a short report. Thanks, Teo

@Feriver

Hello, I can perform a full audit of your football prediction pipeline, focusing on model integrity, data flow, and probability validity. I will review the Python/FastAPI codebase, trained artifacts, and API integrations to identify root causes behind repeated 95% confidence, feature mismatches, and unreliable outputs. My approach includes validating the training pipeline (feature engineering, splits, leakage risks), fixing feature alignment between training and inference, resolving missing/unseen label issues, and verifying Odds API integration. I will also analyze model behavior (class imbalance, calibration, bias toward “Under/BTTS No”) and correct probability estimation using proper calibration techniques. I will deliver a clean backtesting setup using unseen chronological data with real odds, plus a concise report outlining what is broken, what is fixable, and whether rebuilding is justified. Expect clear, practical recommendations without unrealistic claims. Thanks, Asif

@Herculesz

Hi I can audit your existing Python/FastAPI football prediction system with a focus on model integrity, feature pipeline consistency, probability calibration, and bookmaker-odds reliability. My experience covers scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch, feature engineering, label encoding, API integration, and chronological backtesting for sports models. The main technical risk here is that the current outputs suggest pipeline corruption or validation flaws, including feature mismatch, unseen-label failures, missing CatBoost fields, fallback odds usage, and possible leakage affecting confidence scores. I will trace the full prediction path from raw API inputs to saved model artifacts, identify exactly where the 95% confidence repetition and market bias are being introduced, and determine whether the model layer is repairable or should be rebuilt. If salvageable, I can correct feature alignment, encoder consistency, odds ingestion, and inference logic so live predictions match training expectations. I will also verify The Odds API integration, test calibration against unseen chronological fixtures, and produce a concise technical report with findings, fixes, and realistic recommendations. My goal is not to overstate model performance, but to give you an honest ML audit grounded in reproducible validation and trustworthy analytics. Thanks, Hercules

@c0909h1179

Hi there, I am the best here, your Doctor here :) Please check out my profile and see what others have to say about the work I've done related to the skills you're looking for. Hope to work together soon. Thanks!

@vishal1145

Your model is showing classic symptoms of overfitting and feature drift - the 95% confidence spam means your probability calibration is broken, and the feature mismatch (103 vs 122) will cause silent failures in production that corrupt every downstream prediction. Before I dig into the codebase, I need clarity on two things: What's your current train/test split methodology? If you're using random splits instead of strict time-based cutoffs, you've got data leakage baked into every model file. Do you have access to historical odds data from The Odds API, or are you only pulling live lines? Without historical closing odds, your backtest will be meaningless because you can't calculate true EV on past predictions. Here's the diagnostic approach: - FEATURE PIPELINE AUDIT: Trace every transformation from raw API-Football response to model input and identify where the 19 missing features disappeared - likely a schema change or conditional logic that fires differently in training vs inference. - PROBABILITY CALIBRATION: Run isotonic regression on your holdout set to check if predicted probabilities match actual outcomes - the 95% clustering suggests your models are outputting raw scores without calibration. - CATBOOST FEATURE HANDLING: CatBoost expects categorical features encoded as strings during training but you're probably passing integers at inference - I'll align the feature types and retrain if the artifacts are corrupted. - ODDS API INTEGRATION: Verify your API key limits and implement fallback logic that logs when real odds aren't available instead of silently injecting placeholder values that break Kelly calculations. - CHRONOLOGICAL BACKTEST: Build a walk-forward validation framework using only matches after your training cutoff date, with actual closing odds where available, to measure calibrated Brier score and ROI under realistic conditions. I've debugged similar pipelines for 2 sports betting clients where the models looked great in notebooks but failed in production due to feature leakage and timestamp issues. I don't promise profitability - I promise you'll know exactly what's broken and whether the foundation is worth saving. Let's schedule a 15-minute call to review your training logs and model registry before I commit to a fix vs rebuild recommendation.

@Apurva510

Hello, I can audit and stabilize your full football prediction pipeline built in Python using FastAPI with models from scikit-learn, XGBoost, LightGBM, CatBoost, and PyTorch. Approach: • Full code + model artifact audit (feature pipeline, leakage, label issues) • Fix feature mismatch (103 vs 122), CatBoost schema errors • Investigate repeated 95% confidence (likely calibration/logit bug or leakage) • Validate API-Football + Odds API integration and fallback logic • Rebuild backtest with true historical odds + unseen fixtures • Provide honest assessment: salvage vs rebuild recommendation Deliverable: • Debugged pipeline (if fixable) • Clean backtest report + probability calibration check • Technical findings document with root causes Question: Do you have access to the original training dataset versions used for model creation, or only the trained artifacts? With Regards! Apurva

@marjanahmed13

Hi! My name is Marjan and I'm here to offer you my services as a skilled applicant with over a decade of experience working on Freelancer.com. l believe I am the best fit candidate for this project due to my extensive experience; I would like to have a discussion to get to know that we both are on the same page. Once the scope will be locked, I will start working on it right away.

@toriquldev123

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@hoangnn90

✅Full Experience in Football Match Outcome Prediction Model with Python Programming(AI/ML)✅. ✳️I am very confident that complete your project perfectly. ✳️I can guarantee the quality of the job and deliver the result on time. I hope we will discuss in more detail via chat. Best regards!