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Breaking Down Advanced Statistics: Hitters

Written by Skye Paul (@BravesFromLA on Twitter), September 9th 2021


Atlanta Braves GM Alex Anthopolous speaking with the media during Spring Training (2020)


Within the last 20 years, baseball has seen a complete transformation in player evaluation and scouting. Beginning with Bill James and first adopted by Billy Beane, so called "advanced analytics" have taken over the business side of the game. Teams have moved on from the eye test and basic statistics like batting average to more complex processes of evaluation, such as exit velocity, wRC+, and expected stats. While front offices across the league have greatly benefitted from this new data, baseball fans have been slow to catch on, many question the legitimacy and effectiveness of the modern philosophy.

The term "advanced analytics" has been coined by the media to describe the modern baseball philosophy, somewhat villainizing it to explain a team or player's struggles. Many fans think negatively of these more complex player evaluation methods because they don't understand them, and believe they are far too complex to be useful in analyzing a simple game like baseball.

Names like "wRC+" may look intimidating at first, but these new analytics are actually somewhat easy to understand for the average baseball fan with some practice, and I'm here to explain them. I won't be discussing strategy in this article (such as the decline in bunting and base stealing), I'll only be breaking down the statistics themselves and what they mean. Let's get started!



When looking up many advanced stats or projection systems on google, you'll find that most of them trace back to a stat called "wOBA" (Weighted On-Base Average), a stat that very few baseball fans are familiar with. What is it and how is it calculated?

wOBA is similar to OPS, a combination of on-base and slugging to generate a number to evaluate how good a player is at doing these two things. However, wOBA is calculated much differently and much more precise than OPS. Instead of using total bases (single is one, double is two, etc.) and on-base percentage (% of time a player reaches base), wOBA evaluates each individual outcome of a plate appearance and assigns it a value, based on how effective it is in creating runs. Here's the formula:

That might look like a long and complicated formula, but if you take a closer look, you'll see each offensive outcome, unintentional walk, hit by pitch, single, double, triple, and home run, each with a number next to it. The numbers represent the value of each outcome. Why? Because who's to say that a double is twice as valuable as a single? Or a homerun being four times as valuable as a single? wOBA uses researched values rather than the arbitrary values slugging percentage uses. wOBA also accounts for the offensive production created by walks and hit by pitches, which slugging percentage and batting average don't account for.

Once the total offensive production is accounted for, the stat divides that number by the number of plate appearances in the sample (not counting intentional walks) in order to look at the average amount of runs a player is creating per plate appearance. Meaning, a player with a .340 wOBA averages 0.34 runs created each time he steps into the box.

Let's calculate Ronald Acuña Jr.'s wOBA as an example:

Up until his season ending ACL injury on July 10th, Acuña had 49 walks, nine HBPs, 40 singles, 19 doubles, one triple, and 24 home runs in 358 plate appearances (not counting IBB). Plugging that into the formula looks like this...

((.692 x 49) + (.722 x 9) + (.88 x 40) + (1.243 x 19) + (1.57 x 1) + (2.012 x 24))/358 = 149.082/358 = .416

This means that Acuña generated, on average, 0.416 runs per plate appearance he had in 2021 according to wOBA.

*Keep in mind that scales for wOBA change yearly, make sure you use the appropriate weights according to the year if you want to calculate this yourself*


Proof that it's more accurate than generic offensive stats (skip if math isn't your thing):

Going about proving if such a stat works is quite simple to do. At the end of the day, wOBA seeks out to project runs scored as a whole and is not park adjusted, so why don't we see how well team wOBA correlates with total runs scored?

In the chart on the left, each team's wOBA and total runs scored from the 2021 season is plotted, and the trend line showing the relationship between these two stats is very strong (r^2 of 0.762, meaning 76.2% of the variability in total runs can be explained by wOBA).

In the chart on the right, I did the same thing, but used team batting average as my independent variable instead of wOBA. As you can see, the data is far more scattered, and holds only a somewhat weak r^2 of 0.334 (meaning only 33.4% of the variability in total runs can be explained by batting average).

Clearly, wOBA is effective in projecting the amount of runs a player generates per plate appearance, far more effective than generic statistics like batting average... and it makes sense why this is the case. wOBA uses high amounts of data to come up with an accurate value for each type of outcome. Batting average ignores productive offensive outcomes such as walks and hit by pitches, and weighs each hit equally.



wRC+ stands for Weighted Runs Created Plus. Sound familiar? Yup, wRC+ is based off of wOBA, and is a more advanced version of it. What wRC+ tries to accomplish is to compare players offensive productions. It does this by first adjusting a player's wOBA according to the stadiums they play in. Unlike other major sports, the MLB does not mandate a set field design. Each major league team creates their own field, leading to some stadiums being more hitter or pitcher friendly than others. Not only that, the altitude at which the stadium lies on has a significant effect on how much the baseball carries. Stadiums like Yankee Stadium have very short porches down the lines, extending as close at 314 feet from home plate (very short compared to most stadiums). Coors Field in Denver plays at over 5,000 foot elevation, where the air is much thinner than normal, allowing the baseball to fly much farther when hit.

Park adjustments are complex and include more than just park dimensions, however it is important to acknowledge that they exist. In wRC+, a player's wOBA is knocked a few points up or down depending on their home stadium. (you'll notice Rockies players get knocked down significantly). wRC+ then puts the adjusted wOBA on a league average scale, where 100 is average. Each points above or below 100 represent the percentage above or below average a player's adjusted wOBA is.

For example, a player with an 80 wRC+ means a player has been 20% less effective at creating runs than the league average hitter, and a 130 wRC+ means a player has been 30% more effective at creating runs than the league average hitter. On this scale, below 70 is terrible, 70-80 is poor, 80-95 is below average, 95-105 is average, 105-115 is above average, and so on. All-stars usually have wRC+ 130 or above, and MVPs usually have a wRC+ above 150. You can see any players wRC+ on the Fangraphs website.

wRC+ is an excellent stat to use when comparing players offensive production. It is a results based statistic, and is very easy to use and understand. The name might look intimidating, but once you get used to using wRC+ I promise you will never look back.


Statcast Data

While wOBA and wRC+ do an excellent job of capturing a player's offensive production from a results point of view, they don't capture the shear variance that happens in baseball. Players can get lucky or unlucky, and the pure results don't always paint an accurate picture of a player's future production. This is where Statcast comes in, MLB's data site which primarily focuses on batted ball data. They measure exit velocity, launch angle, etc. to assess the skills of a major league hitter. Let's break down the main statistics they track:

Average Exit Velocity / Max Exit Velocity: This one is quite simple. Average exit velocity takes the average velocity of all balls put in play by a hitter, and max exit velocity measures the highest exit velocity a player has obtained (only balls in play) that season. Hitting the ball hard is good for obvious reasons, and comparing how hard a player is hitting the ball to league average can be useful in determining whether or not a player is getting lucky or unlucky.

Barrel %: A barrel is defined as a a batted ball with an exit velocity of over 98 MPH and a launch angle (the angle at which the ball is elevated with respect to the ground) between 26 and 30 degrees. Barrels usually lead to extra base hits, and barrel % is a batter's percentage of balls in play which are barrels. Players with high barrel % are not only hitting the ball hard, they are elevating the ball, and are likely to see results soon if the results aren't as great.

xBA, xSLG, xwOBA: These stats aim to predict the result based stats (batting average, slugging percentage, wOBA) using batted ball data. By measuring the exit velocity and launch angle of every ball in play, Statcast uses its data (previous batted balls with similar exit velocity and launch angle) to determine the usual result such batted ball. For example, a batted ball with an exit velocity of 110 MPH with a launch angle of 30 degrees will almost always be a home run, the expected batting average on such ball is probably .900 or above (meaning over 90% of these batted balls end up being hits). Contrary, an infield pop up with exit velocity of 90 MPH and launch angle of 80 degrees with almost always be an out. Therefore, the expected batting average will be .050 or less. Statcast uses the same method of calculation for xSLG and xwOBA. These stats paint a picture of what a player's results theoretically should be, and major discrepancies between the expected and actual results can be signs of future progression or regression. *NOTE: The lateral angle of batted balls is not tracked by Statcast in these measurements, and therefore does not account for defensive shifting. This is a shortcoming of the expected stats*


That wraps up all the advanced hitting metrics I will cover in this article. I will create a sequel to this article covering advanced pitching metrics, keep an eye out for that one in the coming weeks!

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