Chicago Cubs: A statistical analysis of the National League Central

Let’s get down and dirty – metrics style

Be forewarned. There are lots and lots of numbers that will follow here. Numbers are boring. They are sterile. Numbers can be manipulated. But numbers are also necessary when taking personal opinion and the dreaded “eye test” out of the equation, which is what I’ve tried to do here. Rather than run from those numbers, I will offer a bit of an informational olive branch by trying to define the numerical values up front better. To that end:

Notes:

• Cumulative stats – such as WAR – are expressed as per game averages, unless otherwise noted. This is important to remember for context throughout the article.
• Per game averages reflect data covering a four-year sample size from 2014 to 2017 (where applicable; obviously younger players with limited service years allow only for review of however many years they’ve had Major League roster time). Using an expanded time scale helps mitigate some of the risks that comes with only assessing the immediate past. Baseball is a sport of trends and consistency where, as the saying goes, water tends to find its level. As such, utilizing a broader sampling of data helps account for the trending.
• Because cumulative stats are broken down to their lowest level (per game average), the rankings herein use a “Play Percentage” estimate for each individual likely to take up a roster spot in 2018. That value was used to determine the approximate number of games played for each of those individuals. Through these means, a fairly representative baseline was created which allowed a very linear evolution from per game average to predicted 2018 season-long results.
• Key sabermetric statistics such as WAR (Wins Above Replacement), UZR (Ultimate Zone Rating), ISO (“ISOlated power” – measure of extra base hitting tendencies), BsR (Base Running measure of additional runs created), FIP (Fielding Independent Pitching), wOBA (weighted On Base Average) and BABIP (Batting Average on Balls In Play) are leaned on heavily throughout these rankings.

Let’s begin, shall we?

Sabermetric statistics were developed to look “beyond the box score” and to allow value to be quantified in a different, cleaner, more comprehensive way. It marries a variety of disparate data elements together to create a more singular, concise way to measure player value. To learn more about these statistical measures, visit Fangraphs, which was used as a source for this article.

We’ll start the evaluations with the catching and infield positions. Subsequent articles will follow in each of the next two days to round out the outfield, as well as pitching staffs.