An Introduction to the Luck Factor in Baseball

By Scott Barzilla
Updated: February 19, 2011

Photo taken from Google Images

There is a schism in baseball between those that we might call traditionalists and those we would call sabermetricians. Traditionalists believe in intangibles. They believe in character, chemistry, and pure athletic skills. Unexplained events are actually explained by clutch performance or what some people call “choking”, Moreover, we shouldn’t try to define it or quantify it. After all, if you played the game or watched the game you would know.

Those that analyze the game with statistics understand that everything cannot be explained by numbers. This doesn’t have to stop us from trying. Fielding metrics have come a long way in the past two decades and continue to progress at an alarming rate. Even more advanced metrics like zone rating are soon to be passé. With this background I introduce the luck factor. Essentially, the luck factor is a temporary measure. Luck is a generic term some analysts use to label phenomenon that can’t be explained through statistics. The idea is that all of these things will follow a regression model.

Regression to the mean is one of those official sounding terms designed to make us seem smart. Essentially, it is fancy way of saying, “I’ve got a hunch he’s due.” Imagine a stock broker telling a client to buy a stock because it has tanked lately and is due to rebound. Why? Well, I just have a hunch it’s due. Imagine a student neglecting to study for a test because he got a 50 on the last test. He’s due for a 90 right? A regression model works in most instances, but is not a long-term solution.

Luck factor is simply an addition of the statistics in hitting and pitching that are most volatile. Hitters and pitchers typically can replicate walk and strikeout rates. Home run rates are a little less stable and included in luck factor. However, it is a small factor. The biggest factor is BABIP on the hitting side and DER on the pitching side. On the hitting side we add double plays and on the pitching end we add left on base percentage.

A quick word on methodology is needed. All lineups and pitching staffs assume 500 plate appearances for hitters and equal innings for pitchers. Also, lineups, starting rotations, and bullpens use 2010 numbers, but are with their current teams. Obviously, this is what took the most time to compile. Today, we will look at National League lineups. In later installments we will look at American League lineups, rotations, and bullpens. Finally, the luck factor is based on the league average of the lineups. That will become readily apparent when we see the chart below.

I hate to bore people with details, but some details are necessary. What we are doing here is including every possible factor that could impact batting average on balls in play. Luck is the last possible explanation we want to go with. Imagine any statistician going to a general manager and going ahead with the “luck theory.” We have included strikeouts, walks, home runs, line drive rates, ground ball rates, fly ball rates, and double plays. The goal is to determine whether any of those are linked to any of the batted ball statistics.

I will be profiling the luck factor for individual positions on my blog at http://breathingorangefire.com. Fantasy players should take note of the luck factor. It isn’t an exact indicator of what a player will do in 2011, but it is a strong indicator of regression. In other words, a team like Arizona enjoyed good luck even with a 13^th place finish in GPA (a good indicator of actual offensive production). The number 25 is not exact, but it is a solid indication that 2011 might not be so good for the Diamondbacks.

We begin with the quality of the lineup. Obviously, the higher the GPA the more likely a team would enjoy good luck. At least, that would make sense. Three of the top four teams had negative luck factors, so that wouldn’t seem to be a strong correlation. Three teams had line drive rates at 20 percent. None of them had BABIP rates at the league average or above. Typically, line drive rates produce higher batting averages. We see something similar with double plays. Double plays should be higher with higher groundball rates. Seven teams had more double plays than league average. Only two had groundball rates above average. So, we are really nowhere closer yet to knowing. We’ll keep working on it though.