Horse Racing Favorites Analysis
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About this role
Python, Data Processing, Statistical Analysis, Data Science, Data Visualization, Data Analysis, Statistical Modeling, Predictive Analytics · I’m sitting on almost four years of RBD race data and want to turn it into clear, actionable insight about favourites. The goal is to quantify how often the market’s top pick wins, loses or merely breaks even, then compare that reality with the odds-implied expectation. I care about the pattern through time—daily, weekly and monthly swings—so I can spot “hot” or “cold” stretches rather than back-fitting a single global figure.
Key filters must be baked in from the start: race type, horse age, track conditions and each runner’s RBD Rank. I need the analysis to respect those dimensions without data-snooping, so whatever modelling approach you take should allow me to hold-out or cross-validate properly.
Because I ultimately want to decide whether long-term betting or laying offers the better edge, please include both win-rate and ROI style metrics in the final output. A basic understanding of book-price maths (over-round, true probability, implied margin) is essential so the results make sense in betting terms.
Deliverables • Clean, well-commented code (Python, R or similar) that loads the dataset, applies the chosen filters and produces daily/weekly/monthly summaries • A predict