Data-Driven Underdog Bets: The Trench Run Algorithm

Inspired by Luke Skywalker's improbable trench run in Star Wars, this project aims to uncover hidden value in sports betting, focusing on underdog teams or individual athletes in less-popular sports. Imagine 'Case' from Neuromancer developing sophisticated algorithms, not for hacking, but for predicting sports outcomes. The project involves building a web scraper to gather publicly available data from websites covering niche sports like darts, esports (smaller games), amateur leagues, or even obscure statistics from major league games (e.g., specific player matchups within a basketball game). This data will include things conventional sports statistics overlook: social media sentiment, weather conditions at the event location (if applicable), recent injury reports from less reliable sources (where the information might be faster but riskier), and even forum discussions about the teams or players. The scraped data is then fed into a simple machine learning model (think regression or a basic neural network) trained to identify patterns correlating unconventional data with upset victories. The output is a 'Trench Run Score' for each underdog, indicating the probability of an upset based on the analyzed data. Users can then use this score to place informed bets, focusing on underdogs with surprisingly high Trench Run Scores. Earning potential comes from affiliate links with betting platforms or selling premium access to the algorithm's predictions for specific niche sports. The low-cost aspect comes from utilizing free web scraping libraries (Beautiful Soup, Scrapy), open-source machine learning frameworks (scikit-learn), and cloud services with free tiers (Google Cloud, AWS). The niche focus reduces competition and allows for specialization, increasing the model's accuracy and profitability.