![]() ![]() There are many third-parties that construct their own metrics based on the same in-game statistics, qualitative rankings from experts, historical team rankings that go back decades, and even the exact players that are on the field during each game. In addition to the standard in-game statistics, we can use external metrics within the model. Our model should include all these in-game statistics, among others. Offensive success metrics would include third- and fourth-down conversion efficiency and the number of turnovers. ![]() Less obvious metrics include total number of yards gained and total ball possession time.Obvious in-game metrics include the average amount of points a team scores and the average amount of points a team gives up to the opposing team.Because our goal is to predict the outcomes of NFL games in the 2020-2021 season, the first thing we need to define is the statistical metrics that can best determine whether a team wins or not: ![]() Doing so clarifies which data should be used, how to manipulate the data to construct a training set, and where to obtain the data. When creating a model from scratch, it is beneficial to develop an approach strategy that clearly delineates the goal of the model. The website hosts sports statistics for a myriad of professional sports, and is kept up-to-date as games are played.įor Windows users, run the following at a CMD prompt: powershell -Command "& $(::Create((New-Object Net.WebClient).DownloadString(''))) -activate-default Pizza-Team/NFL-Game-Prediction-Win"įor Linux users, run the following: sh <(curl -q ) -activate-default Pizza-Team/NFL-Game-PredictionĪll of the code in this tutorial can be found on my GitLab repository here.
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