Building a VR data visualization with Statcast batted ball data

Virtual reality (VR) and its proliferation into our lives is a popular topic right now and is being greeted with healthy doses of both excitement and skepticism. I for one am bullish on VR! Having been fortunate to play with an HTC Vive in the office, I find that even mundane things, like mini golf, become mind … More Building a VR data visualization with Statcast batted ball data

Batted-ball data visualization using an alternative to a heatmap

The availability of tracking data has been an exciting development in the world of baseball statistics and analytics in the last several years. Most significantly this includes pitch f/x and, more recently, batted ball data from MLBAM Statcast. The data I am working with here are a limited set from the hit f/x system, from April, 2009, … More Batted-ball data visualization using an alternative to a heatmap

Power ranking & multi-year park effects using Markov chain Monte Carlo

I’ve recently had the opportunity to use pymc, a python module for Bayesian statistical modeling, including hierarchical models. It’s so much fun to use, that I find myself looking for models to apply it to. One example is building a power ranking model, including a home field advantage and a park effect. This applies very generally, … More Power ranking & multi-year park effects using Markov chain Monte Carlo

True talent estimate using Gaussian process regression

A while back I wrote about estimating true talent using two correlated samples. The resulting equations, for talent in time period one, and talent in time period two, are, As an example, let’s look at Mike Trout’s 2012 & 2013 seasons. His wOBA was 0.409 in 639 PA, and 0.423 in 716 PA in 2012 … More True talent estimate using Gaussian process regression

2016 BBHOF candidates as Chernoff faces

A few weeks ago a blog post about Chernoff’s faces came across my twitter feed http://www.futilitycloset.com/2015/11/29/chernoffs-faces/ I thought this was intriguing and thought it’d be fun to apply this to some data set – and the metrics for the 2016 candidates for the baseball hall-of-fame  is perfectly suited. Before I show my result, I want … More 2016 BBHOF candidates as Chernoff faces

Principal components analysis for baseball HOF voters

As I talked about in my previous post on sportsVU player-tracking data, principal components analysis (PCA) is a technique that can be used for both dimensionality reduction (describe the data effectively using fewer numbers) and to reveal something about the nature of the data. To apply PCA to voting patterns for the baseball hall-of-fame, we can consider … More Principal components analysis for baseball HOF voters