The NBA Lineup Evaluator tool provides lineup estimates for various metrics we’ve developed to assist in analyzing the strengths and weaknesses of a lineup. Enter any combination of players below. For more information on the lineup components, check out the methodology article.
All posts by Jack Werner
Wait, why does PML favor the Astros?
After four games, this World Series is tied at two games apiece and shaping up for an exciting finish! According to our PML model, Houston has a 53% chance of winning it all. This might come as a surprise; the majority of other prediction models, like FiveThirtyEight’s Elo ratings, give Los Angeles a slight 56% edge—not surprising, given the Dodgers play two of the three games at home. In contrast, PML favors the away team in every game from here on out. Ultimately, the difference between the 44% chance FiveThirtyEight gives the Astros and our 53% is not huge. Whichever you prefer, the Series is a toss-up. But it’s useful to dig into why we’re a bit higher on the Astros, and in the process, get to know our PML model a little better.
World Series Predictions: PML 2017
Throughout the playoffs, Model 284 is making predictions using our PML model. You can find a brief overview of the model here and a full methodology here.
Continue reading World Series Predictions: PML 2017
NLDS Predictions: PML 2017
Throughout the playoffs, Model 284 is making predictions using our PML model. You can find a brief overview of the model here and a full methodology here.
Continue reading NLDS Predictions: PML 2017
ALDS Predictions: PML 2017
Throughout the playoffs, Model 284 is making predictions using our PML model. You can find a brief overview of the model here and a full methodology here.
Continue reading ALDS Predictions: PML 2017
MLB Wild Card Preview: PML 2017
Last year, on my personal blog, I posted about a model I created for evaluating baseball teams and predicting games. I’ve re-run this model for the 2017 season, and will use it to predict games and series throughout the playoffs, so make sure to keep checking back! If you’re interested in the full methodology, I’ve posted it here. For a quick look at how the model shakes out for the 2017 season, look here.
Rating Pitchers and Offenses: PML 2017
Last year, on my personal blog, I posted about a model I created for evaluating baseball teams and predicting games. I’ve re-run this model for the 2017 season, so let’s take a look at the results! Be sure to check back throughout the playoffs, as I’ll use this model to predict games and series throughout the playoffs. I’ve already posted wild card PML predictions.
A Poisson Maximum Likelihood Model for prediction and evaluation of MLB teams and pitchers
This article was originally published in 2016 at jackoliverwerner.wordpress.com.
Introduction
Sports are all about matchups: two teams battling it out on the court, rink, or field until one side emerges the winner. Head-to-head matchups are an especially prominent component of baseball. The game is an overarching matchup of one team against another, but it contains smaller competitions: a pitcher and a catcher trying to outscheme an opposing hitter, a second baseman playing cat-and-mouse to hold a runner close to the bag, or a starting pitcher with a scissors trying to get out of wearing throwback jerseys.
Pitcher Similarity Score Tool
Use this tool to pull up your favorite pitcher and see his top ten most similar players! Methodology for these scores can be found here.
Pitcher Similarity Score Methodology
Why Pitcher Similarity Scores?
Similarity scores, at their best, are a fun and useful way to understand patterns in athletes. We’ve dabbled in similarity scores here at Model 284 before, and now we’re applying it to Major League Baseball pitchers. Our motivation for this project is twofold.