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.
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
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.
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.
This article was originally published in 2016 at jackoliverwerner.wordpress.com.
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.
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.
Select any 2017 drafted NBA player from the dropdown menu to view their top 10 most similar college basketball players historically. Similarity Scores provide insight into how a player will translate to the NBA based on how similar college basketball players in production, physical measurements and experience have performed in the NBA. For a background on the calculation of the similarity scores, see here. This model is one of three pieces that we use to evaluate the NBA potential of college players, with the other two being PNSP and NBA Role Probability Model.