{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Introduction","metadata":{}},{"cell_type":"markdown","source":"\n\nInjury prevention is a critical component in modern athletics. Sports that involve animals, such as horse racing, are no different than human sport. Typically, efficiency in movement correlates to both improvements in performance and injury prevention.\n\nA wealth of data is now collected, including measures for heart rate, EKG, longitudinal movement, dorsal/ventral movement, medial/lateral deviation, total power and total landing vibration. Your data science skills and analysis are needed to decipher what makes the most positive impact.\n\nIn this report , the reporter will create a model to interpret one aspect of this new data, which including X/Y coordinate mapping of horses during races. Using the data, the reporter might analyze jockey decision making, compare race surfaces, or measure the relative importance of drafting. \n\nThe New York Racing Association (NYRA) and the New York Thoroughbred Horsemen's Association (NYTHA) conduct world class thoroughbred racing at Aqueduct Racetrack, Belmont Park and Saratoga Race Course.\n\nThe goal of this report is to analyze horse racing tactics, drafting strategies, and path efficiency. The reporter will make an EDA basically, and then develop a model using never-before-released coordinate data along with basic race information.\n\nThis work will help racing horse owners, trainers, and veterinarians better understand how equine performance and welfare fit together. With better data analysis, equine welfare could significantly improve.\n\nThis report aims to solve the following problems:\n+ Create a horse rating measuring expected finish position versus actual finish position. How does a horse’s expected finish position change through the running of a race? Does this metric rely solely on a horse’s own position or is it influenced by the position of competitors?\n+ What are optimal racing strategies? Considering different venues, surfaces and race distances. Create a jockey rating based upon path efficiency?\n+ Create a surface measure model which would rate the fairness of different paths on a racecourse that may be beneficial or harmful to finish position based. This may be a result of un known barometric, weather or maintenance factors.\n+ Create a model measuring the existence (or not) and relevance of a drafting benefit.\n+ Create a model reveal optimal gait patterns. Does the model differ for such factors as age, distance, race section or surface?\n\n","metadata":{}},{"cell_type":"markdown","source":"# Data","metadata":{}},{"cell_type":"markdown","source":"This competition provides 4 datasets,whcih contain a total of 18 variables, including latitude and longitude data, continuous variables and categorical variables.Detailed information please click on [this](https://www.kaggle.com/competitions/big-data-derby-2022/data). Additionally, [weather data](https://www.kaggle.com/competitions/big-data-derby-2022/discussion/346096) and [horse data](https://www.kaggle.com/datasets/themarkgreen/big-data-derby-2022-global-horse-ids-and-places) are also included.","metadata":{}},{"cell_type":"markdown","source":"# EDA","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"+ [EDA: race_date & post_time](https://www.kaggle.com/code/yangq369/eda-race-date-post-time)\n\n+ [EDA: jockeys](https://www.kaggle.com/code/yangq369/eda-jockey)\n\n+ [EDA: Longitude & latitude & trakus_index](https://www.kaggle.com/code/yangq369/eda-longitude-latitude-trakus-index)\n\n+ [EDA: race_number & program_number](https://www.kaggle.com/code/yangq369/eda-race-number-program-number)\n\n+ [ EDA: rdistance_id & run_up_distance](https://www.kaggle.com/code/yangq369/eda-distance-id-run-up-distance)\n\n+ [EDA: course_type & track_condition & race_type](https://www.kaggle.com/code/yangq369/eda-course-type-track-condition-race-type)\n\n+ [EDA: weight_carried & odds & purse](https://www.kaggle.com/code/yangq369/eda-weight-carried-odds-purse)\n\n+ [EDA: weather](https://www.kaggle.com/code/yangq369/eda-weather)\n\n+ [EDA: horse](http://www.kaggle.com/code/yangq369/eda-horse)","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"##","metadata":{},"execution_count":null,"outputs":[]}]}