Advanced ML Football Betting Market Predictor

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a Football Match Prediction Model with ML for Betting Markets ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project needs and see you're looking for a football match prediction model. Look no further; Zohaib is here to assist you! My team has successfully completed 50+ similar projects in sports analytics. I will create a robust model using Football Manager data and sports news to provide calibrated probabilities for betting markets. ➡️ Why Me? I can easily do your football match prediction model as I have 5 years of experience in machine learning, data analysis, and sports analytics. My expertise includes data extraction, model building, and validation techniques. I also have a strong grip on relevant technologies like Python, CatBoost, and data pipelines. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ Machine Learning ✅ Data Analysis ✅ Sports Analytics ✅ Python Programming ✅ Data Extraction ✅ CatBoost ✅ REST API Development ✅ Backtesting Techniques ✅ Data Pipeline Creation ✅ Web Scraping ✅ Statistical Analysis ✅ Model Validation Waiting for your response! Best Regards, Zohaib

@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 Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .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

@intelliresponse

As a highly skilled professional with over 13 years of experience in Python and Web Scraping, I am confident that I can deliver all the requirements of your project in a timely and efficient manner. My track record of completed projects highlights my expertise in data extraction from various sports platforms, which will be crucial for acquiring the necessary football match and player data for your Football Betting Market Predictor. My comprehensive skills in Machine Learning (ML), Python, Web Scraping, and AI put me at a vantage point in delivering outstanding results for your project. I've previously developed custom ML models on tabular sports data including betting models which allowed me to have an understanding of how these markets work. With regards to pricing, my aim is to provide you with both a lean and professional option with detailed cost estimates of recurring monthly expenses for your choosing.

@flapjakc

Hi, Mateo here, from Toronto. I'm a full stack developer with 8+ years of experience working with Python, Machine Learning, Web Scraping and REST API. I understand you need a time-safe football oracle using 10,000 matches, FM data, timestamped news and context to produce calibrated probabilities and value bets. The key challenge is preventing future-data leakage across FM editions and news timestamps. FM25 needs a documented interpolation method, not future attributes mixed into 2024/25 data. A 50%+ hit rate on odds above 2 should be validated, not assumed. My approach: first, map match, FM, news and context sources. Then, use Dixon-Coles/Poisson plus boosting, with LLM news scoring. Finally, walk-forward test against closing odds, compare paid news vs RSS/GDELT, and document lean/pro recurring costs plus an MVP path. For this pipeline, I'd keep every feature traceable to its source timestamp and match cutoff, including the FM25 reconstruction and player-market inputs. A few questions to better understand the scope: Q1 - Closing odds as benchmark only, or also a feature? This determines leakage controls. Q2 - For FM25, use FM24/FM26 interpolation with FM26 restricted to later data? This keeps editions time-safe. Q3 - Which leagues matter most for your Italian market? I'll prioritize data quality and market coverage there. Happy to go over the details here in chat and suggest the best approach. Looking forward to hearing from you.

@sunainawbst

Sports Analytics | ML Prediction | Football Data | Betting Models | Python Hi, Thanks for sharing the details. You have explained your requirements very clearly. I understand you want to build a football prediction model that combines Football Manager data, historical match/odds data, sports news and ML to generate calibrated probabilities and identify potential value bets. I have experience working on sports prediction and betting platforms, so I’m already familiar with odds, prediction logic, historical data, APIs and data-driven models. I can help you build the complete pipeline from data collection to modelling, backtesting and deployment. For the model, I would use a combination of goal-distribution models such as Poisson/Dixon-Coles with ML models like CatBoost where appropriate. For the missing FM25 data, I would avoid using future information and create an estimated dataset using only information that would have been available at that point, with interpolation between surrounding editions where appropriate. For news, I can build an LLM pipeline that converts pre-match news into timestamped structured features, while strictly filtering anything published after kickoff. I can also provide walk-forward backtesting, calibration metrics, bookmaker comparison and ROI analysis so we can honestly measure whether the model has an edge. Let’s connect and discuss the data sources and MVP scope. Thanks! Purnima

@corpmember29

Hello, ADVANCED ML FOOTBALL BETTING PREDICTOR {{{ I HAVE BUILT SIMILAR SPORTS ANALYTICS, ML PREDICTION & LLM DATA PIPELINES BEFORE AND CAN SHOW YOU }}} I carefully reviewed your requirements and understand that you need a leakage-safe football prediction system using Football Manager data, historical match/odds data, contextual features, and timestamped sports news. I have 11+ years of software/AI development experience and can build a modular pipeline covering data acquisition, FM data versioning, feature engineering, LLM-based news scoring, probability modelling, walk-forward validation, calibration, bookmaker comparison, and deployment. For modelling, I would combine CatBoost/gradient boosting with structured goal-distribution models so probabilities across markets remain mathematically consistent. Corners, cards and player markets can use specialized models. All features will be timestamped and strictly restricted to information available before kick-off to prevent look-ahead bias. For the missing FM25 database, I would evaluate interpolation between FM24/FM26 attributes alongside real-world historical data, with the methodology documented and tested rather than blindly estimating values. News can be processed through a paid sports-news API or compliant RSS/GDELT sources, with an LLM converting articles into timestamped structured signals for injuries, morale, transfers, statements, incidents, etc. PHASES: Data acquisition → News/LLM pipeline → Feature engineering

@PINESCRIPTERR

Hi, I understand you want a football prediction system that combines historical match data, Football Manager attributes, contextual information and structured sports-news signals to produce calibrated probabilities across multiple markets, then objectively compare them with bookmaker prices. I can design the pipeline around strict time-aware data handling so each prediction only uses information available before kick-off. For modelling, I’d use a hybrid architecture: a goal-distribution component such as Dixon-Coles/Poisson to maintain consistency between goal-related markets, with CatBoost or similar models for contextual features and separate models for corners, cards and player events. Calibration would be treated as a core requirement rather than simply optimising classification accuracy. For the missing FM25 dataset, I would avoid blindly filling values with future information. I’d reconstruct a time-consistent 2024/25 feature set from FM24/FM26 where appropriate, using player transfers, ageing and attribute changes to interpolate or estimate values while clearly flagging reconstructed features. For news, I’d build an ingestion layer that collects timestamped articles and passes them through an LLM extraction pipeline to produce structured event/impact features. The pipeline would enforce a strict publication-time cutoff so post-kickoff information cannot leak into training or prediction. Best regards, Houssame

@maxscender

I’ll build this as a modular pipeline: data acquisition, news/LLM feature extraction, modelling, and validation/deployment. For the missing FM25 season, I’ll treat it as a latent variable—train a small model on FM24 and FM26 squad attributes plus historical match performance to estimate the missing ratings, then feed the interpolated data into the match model with a time-decay weight so the model learns to discount it relative to real editions. For news, I recommend a paid RSS aggregator like NewsAPI for reliable coverage and rate limits, combined with targeted crawling of club sites and transfermarkt for injury and lineup updates; the LLM will extract structured event scores (morale, injury impact, incidents) with timestamps, and only pre-match news will be used, with recency weighting.

@shrec

Hi, I’d build this as a leakage-safe probabilistic forecasting pipeline, with strict date/version controls so each match only uses information available before kick-off. For match outcomes and goals, I’d consider a Dixon-Coles/Poisson foundation combined with gradient-boosted features, keeping probabilities consistent across related markets. Separate models can handle corners, cards, and player markets. For the missing FM25 season, I’d avoid simply filling it with future information. I’d construct time-valid estimates from FM24/FM26 attributes where appropriate and clearly test the effect of that reconstruction. The news pipeline would timestamp every item, use an LLM to convert relevant pre-match information into structured features, and prevent post-kickoff leakage. I’d validate with walk-forward testing, log-loss, Brier/RPS, calibration, closing-odds comparisons, and simulated ROI. I’d also separate the project into data acquisition, news pipeline, modeling, validation, and deployment phases so an MVP can be tested before the full system is built. Best, Ivane

@dvcontact

Hello Sir, Would you be interested in a demo of our advanced football match prediction oracle solution before making any commitment? Our approach combines cutting-edge machine learning with comprehensive data sources, ensuring precise probability outputs across all betting markets while identifying value bets compared to bookmaker odds. Let's discuss a detailed plan and demo to showcase how we can revolutionize your betting insights with our innovative solution. Regards, Smith

@JustinJcob

I got you! Your football prediction oracle needs a leakage-safe data pipeline, calibrated multi-market probabilities, FM-based squad intelligence, news-to-features, and bookmaker value comparison. I’d build a goal-distribution core using Dixon-Coles/Poisson + CatBoost features for consistency, with separate calibrated models for cards, corners, and player markets. For missing FM25, I’d estimate attributes by interpolating FM24→FM26, adjusted with real minutes, transfers, injuries, and form. News can be handled via API/RSS/GDELT crawling + LLM timestamped sentiment/event scoring. Phases: data 2–3 weeks, news 1–2, modelling 3–4, backtest 1–2, deployment 1 week. Lean monthly costs: $150–$500; professional: $1k–$3k+. MVP possible: 1X2, O/U, BTTS, no news first. I’m young, a fast learner, and available 24/7. Get the demo first before you pay. Which leagues and bookmaker odds sources do you prefer? Let’s chat and discuss the answers. Kind regards, Haroon Z

@AndrewJSMAN

I've built sports prediction models using Football Manager data and ML before, so this is right in my wheelhouse. I'll extract FM data via community exports, interpolate the missing 2024/25 season by averaging FM24 and FM26 attributes, and ensure no future leakage. For news, I'll use a combination of RSS feeds from BBC Sport and Sky Sports for free data, with an optional API like SportsMonk for higher quality. The LLM pipeline will process news into structured scores using a fine-tuned model like BERT, with timestamps enforced. For modeling, I'll combine Poisson goal models with CatBoost for probabilities, validating with walk-forward backtests and calibration plots. The pipeline will include daily updates via Airflow, with a FastAPI endpoint for outputs. I can outline the full timeline after reviewing the dataset. Thanks, Andrii.

@saba106

I am an expert statistician, Research Writer, and data analyst with more than eight years of experience. I have full command of Excel analysis, SPSS, STATA, R LANGUAGE, AND PYTHON. I am an expert in creating time series prediction models, working with survey data, conducting marketing analysis, building estimators, and medical analysis. I am a perfect match for your project share other details of the work so I can start working on your project. Will complete task on time.

@emanz03

I’d avoid training separate market models in isolation. I’d build one leakage-safe feature pipeline, a calibrated goal-distribution layer for 1X2/O-U/BTTS/correct score consistency, then separate CatBoost-style models for cards, corners and player props. FM25 can be reconstructed from FM24→FM26 attribute trajectories with transfer/age constraints, never using post-match data. News would be timestamped, entity-linked and converted by an LLM into structured injury/morale/availability features. I’d phase this as data acquisition, news pipeline, modelling, walk-forward backtesting, then API/dashboard deployment, with a cheaper MVP first on 1X2/O-U/BTTS to test whether a real edge exists.

@SmartCodeDev

Hi there This oracle build fits work I have done before. Football betting ML, FM data, odds scraping, LLM news scoring, all familiar territory. FM data: extract per season, only use the edition available before each match. For missing 2024/25 I blend FM24 and FM26 ratings with real transfers and minutes, league-normalized so backtests stay leak-free. News: free RSS and club feeds first, paid Sportmonks or NewsAPI if you want cleaner injury data. LLM turns headlines into timestamped morale or injury scores, kickoff cutoff enforced. Models: Dixon-Coles goal core keeps 1X2, O/U and BTTS aligned, CatBoost for corners and cards, minute-weighted xG for player props. Walk-forward backtest with log-loss, Brier, RPS, calibration plots and closing-odds ROI. 250 EUR covers Phase 1 in 4 days: FM extraction, match scrape, missing season plan, MVP on 1X2 and O2.5 with first backtest report. Cheaper MVP skips news and extra markets but still checks for edge honestly. Monthly lean 40 to 80 EUR. Pro 150 to 250 EUR with paid news and weather APIs. Past work: https://www.freelancer.com/projects/python/Football-Betting-Platform-Milestones https://www.freelancer.com/projects/api/Pocket-Quotex-Trading-Bot https://www.freelancer.com/projects/api-developmet/Python-Data-Engineering-for-Brisbane

@TeraByte26

Hi, I will deliver a football match prediction oracle that extracts FM attributes, match stats, news sentiment and weather, trains a CatBoost‑Poisson model and returns calibrated 1X2, Over/Under and BTTS probabilities via a REST API. MVP in 4 weeks for 650 EUR. I can send a free sample dataset now—lean or professional recurring‑cost preferred? Best regards Waiting for your response in chat! Best Regards.

@muneeb6767

I can build this football prediction pipeline using Python/ML, CatBoost, Poisson/Dixon-Coles, historical odds, web scraping and an LLM-based news feature pipeline, with strict timestamping and walk-forward validation to prevent data leakage; I’ll also handle the missing FM25 season through interpolation/reconstruction and deliver automated data, calibrated probabilities, value-bet analysis, backtesting and deployment.

@HamparProd

The biggest modelling risk here is future leakage, especially around the missing FM25 season. I would not reconstruct 2024/25 using FM26 attributes because that injects information unavailable at prediction time. I’d use the last valid FM snapshot plus only contemporaneous pre-match data, or treat FM features as missing for that season. For goals I’d build one coherent score-distribution model, with CatBoost/nonlinear features feeding expected scoring rates, then derive 1X2, O/U, BTTS and correct-score probabilities from the same distribution. Corners/cards and player props would be separate models. Validation would be strictly walk-forward, with calibration fitted only on prior data, de-vigged bookmaker baselines, Brier/log-loss/RPS, reliability plots and ROI confidence intervals. News would be timestamped and converted by the LLM into auditable structured features, never directly into betting probabilities. For the posted budget I recommend a calibration-first MVP covering 1X2, O/U, BTTS and correct score without news/player props. I can start immediately after award with the historical match/odds data and exact league/market priorities.

@juanpablomacf

Hello, I have already developed this project. Let's discuss about your project in detail via chat. Juan Pablo