Category Archives: Regression

Field testing the motusTHROW

We went up to SmartKage headquarters in Tyngsboro, Mass., and put the sleeves on Nate, a local high school junior. We then fired up Motus Global’s bullpen mode – first in the new motusTHROW app, and then in the legacy mThrow app. At the app’s direction, Nate threw a mix of fastballs, curveballs, and changeups. A voice from the iPhone instructed him which pitch to throw, whether to throw from the windup or the stretch, and what part of the strike zone to aim for. Each of the two sessions consisted of 21 pitches, simultaneously tracked by SmartKage’s PITCHf/x system.

Read the rest on Beyond the Box Score


2016 college baseball season preview

Last year, I introduced my open-source college baseball database (which I’ve recently updated), and showed a few example applications. I looked at win probabilities, how the new flatter seams helped increase offense, the stolen base breakeven point, and the value of bunting (honest).

But this time, I want to use someone else’s data. Chris Long (now with the Detroit Tigers) has his own collection of useful college baseball tools on his GitHub. Let’s use them to generate a season preview.

Read the rest on Beyond the Box Score

Combining Technologies to Measure Swing Development

As hitters develop, their mechanics evolve over time into a swing that both shares many commonalities with other players and is unique to their own game. But tracking a player’s progress on that journey to a consistent swing has always been tricky. Scouting and video analysis can give players a sense of how repeatable their mechanics are, but these are expensive, time-consuming, and limited to players at the highest level, whom we would expect to already have the most consistent mechanics.

Enter technology. Technological developments, including inertial bat sensors and camera-based ball tracking systems, should make it possible to develop a quantitative measure of consistency readily available to a wider range of players, with a wider range of abilities. This will allow young hitters to better measure their progress while also giving scouts and coaches a tool to judge prospective players.

In this article, we look for a way to quantify that relationship between consistency and hitter quality. We measured over 1,500 individual swings from 25 hitters, ranging in age from Little Leaguers to NCAA Division 1 players. We also collected different kinds of swings from each hitter, having each player hit off a tee and a pitching machine, with the goal of hitting first for power and later for contact.

Read the rest in The Hardball Times Baseball Annual 2016

(Collaboration with Dan Kopitzke, K-Zone Academy, Apex, NC)

Do hard-hit balls produce more errors?

…And whereas the home team’s fielding percentage decreases on harder-hit balls, the road team stays oddly consistent — and relatively error-free! — over the meaty part of the curve.

These data suggest home teams get the benefit of the doubt on would-be errors: a ground ball hit at the same speed is more likely to be called an error if the home team is fielding than if it is batting. If the relationship were flipped, you could argue that some of it was due to the visitors’ inexperience with the nuances of an individual ballpark. But it seems unreasonable to argue that visiting defenders get more reliable away from their home grounds. Besides, scorers are incentivized to turn close calls for home batters into hits (to boost batting averages), and close calls for visiting batters into errors (to help keep down ERAs).

Read the rest at Beyond the Box Score


MLB 2015 over/under win totals: Vegas vs. regression

February is the time when baseball news starts peeping through the snow like seedlings, giving little reminders that warm weather is just around the corner*. The sports book at the Atlantis offered one such sign Friday when they posted the first over/unders of the 2015 season.

* – Sorry for the bad metaphor. We’ve gotten an Altuve and a half of snow over the last three weeks and I’m going a little stir-crazy.

Read the rest at Beyond the Box Score

Pitching Backwards: Designing a Bullpen Usage Critique

Collaboration with Jeff Long

Perplexed, I posed a simple question to a colleague, Bryan Cole. I wanted to know how realistic it is for a manager to use recent performance to ‘predict’ a reliever’s next performance. Bryan built out a series of scatter plots that quickly illustrate how difficult it would be to say with confidence that recent performance was especially significant. We selected three relievers (one elite, one middle-of-the-road, and one poor) to quickly take a look at how recent performance predicts the results of a pitcher’s next outing.

Read the rest of Jeff’s article at Baseball Prospectus ($)

Elo rankings for international baseball

In this article, we present a predictive power ranking of national baseball teams based on the Elo rating system. This system objectively measures teams’ relative strength based on past performance, strength of schedule, run differential, and the event importance. To our knowledge, this system is unique: although the International Baseball Federation (IBAF) releases yearly rankings of national baseball teams based on performance in international competitions, this ranking includes results across multiple age levels and is not designed to predict future results.

Read the rest at Beyond the Box Score

Presented at 2014 Saberseminar

  • Longer version (PDF format)
  • Poster (PDF format)