This article provides some background on the components of each NFL Playoff Model. By making use of multiple models that are comprised of different modeling techniques and variables, we can better assess each game. For example, if all the models are predicting the Pittsburgh Steelers to beat the Miami Dolphins, we can feel very confident in picking the Steelers to win. If the models are split, we can explore each model individually based on their predictors to identify areas where the model may be taking into account less than perfect information. For instance, if one model emphasizes defensive performance and Seattle is currently missing Earl Thomas, maybe that model does not represent Seattle as accurately as the others. Ultimately, by looking at multiple models we are able to reduce the noise inherent in all predictive models.
The following is a walk-through of our NBA Prospecting model called Peak NBA Statline Projection (PNSP). PNSP is a prospecting tool that synthesizes numerous variables for college basketball players to predict their NBA success. PNSP seeks to project peak potential success of a college basketball player in the NBA by returning a single rating value (ranging from 0 to 100) that is derived from all available information on a given player.
Over the past three years, I have put together a series of statistical models that predict NCAA Tournament games, and, building off of each game, the tournament as a whole. The models started as an independent research project I did at St. Olaf College with Dr. Matt Richey. For a given game, the models use each team’s statistics and information from regular season games to predict (1) each team’s win probability for that game or (2) a point spread for that game. I use a handful of different models, so each game will have multiple probabilities/point spreads to consider, and will not always agree with each other.
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