How machine learning and SQL can help you outsmart your opponent in sequential team games

Welcome back to the final part of this three-part series on data driven pool strategy!

In Part 1, we explored the quirks of competitive team pool, and determined that player lineup selections for pool games could be improved with data science. We developed a classification model that predicts the probability that one player will beat another player based on past results.

In Part 2, we went one step further and implemented a maximin player selection algorithm in SQL which uses the model predictions to calculate optimal player selection choices.

In this final part, we will compare our machine learning-powered player selections to real world player selection strategies and determine if we really can consistently gain a competitive edge over our opponent.

Part 1: Introduction and predictive modelling.

Part 2: Strategising with SQL.

Part 3: ‘Potting’ it to the test!

#hypothesis-testing #flask #billiards #machine-learning