Baseball is a game of numbers. That is one of the beautiful things about the sport. We can look up a random game in 1930s, and know exactly what happened. The same language has been used since then. Average (AVG), Runs Batted In (RBI), On Base Percentage (OBP), Slugging Percentage (SLG), Home Runs (HR) have been historically the go-to stats of how good a player has been. We can still use those numbers today to get a good idea of how a player or team performs.
Recently, new stats have been introduced to help develop a more detailed picture of the game. These advanced stats have allowed teams to gain advantages in areas that were always available, but were never known.
At DRaysBay, we use a lot of those advanced statistics to paint a better picture of what is going on, and what we can expect moving forward. Hopefully this guide will help those who aren’t familiar with the terminology that we use here at DRB.
Start of a Sabrevolution
The advanced stats era had its beginning with Bill James’ book The Bill James Baseball Abstract, which ranked players according to different lists like Runs Created and Win Shares. Bill James’ book kicked off the saber-metric revolution, lead to more developed stats, and influenced many young minds that now inhabit baseball’s most prominent front offices. Baseball Reference said this about James:
Billy Beane of the Oakland Athletics famously used Bill James’ findings, and other deep statistical analysis, to field an A’s team that went 103-59 in 2002, winning 20 consecutive games in a row, and clinching the AL West. All of these events are covered in Michael Lewis’ Moneyball, which was later turned into an Oscar-nominated film starring Brad Pitt and Jonah Hill.
Both Moneyball and The Bill James Baseball Abstract are highly recommended if you want an easy introduction to advanced statistics and sabrmetrics. If you would like a more Rays-oriented look, I would recommend Jonah Keri’s The Extra 2%: How Wall Street Strategies Took a Major League Team from Worst to First, about the transformation of the Tampa Bay Rays’ after Stu Sternberg and Matt Silverman’s takeover. An obvious staff favorite.
I want to begin with some metrics that you all see ALL the time here at DRaysBay. I don’t want to go into too much detail on how these stats are created, but more of the context in which you should take these numbers. I will be providing links to other sites that do explain how the number is created, but that is too much math for one day.
We will also be using a lot of 2017 and earlier stats. Although Small Sample Size (SSS) is fun, it doesn’t allow us to see the best picture. In most cases, it takes about 150-200 PA for stats to stabilize.
We all like when teams score runs right? That usually leads to wins! Weighted On-Base Average or wOBA allows us to see how a player contributes to those runs. It ONLY counts production at the plate.
“Well Jared, couldn’t you use OPS (On-Base Percentage plus Slugging Percentage) to do the same thing?”. Kind of. OBP does reward getting on base and walking, and SLG applies weights to non walk results, but not as accurate as possible.
Do you remember in the movie Moneyball where all Billy Beane cares about is if guys get on base?
Billy knew that walks (and OBP) are more valuable than SLG, and that all hits are not created equal.
One of my favorite stats, Weighted Runs Created Plus (wRC+) is one of the best ways to measure offense. It adjusts for park effects, and compares it to league average offense.
wRC+ is a way to say “This player is X percentage points better/worse than league average,” where the average is 100 wRC+.
The great thing about wRC+ being league- and park-adjusted is that we can compare different players from different eras.
Bryce Harper’s 2015 MVP year was 197 wRC+, or 97% better than league average. Babe Ruth’s first year as a Yankee? 239 wRC+. Good LORD.
Here’s some SSS fun: Mallex Smith currently leads the Rays at 167 wRC+. MLB Leader is Mookie Betts at 243 wRC+!
What is it good for? Having a single metric to measure a player’s overall contribution to the team. Offense, defense, baserunning, etc. WAR stands for Wins Above Replacement. It tries to give a single number value to the question “How valuable has this individual player been to his team?” or “How much more/less valuable has player x been to his team in comparison to player y on his team.”
There are a couple of important points to make about WAR. First, and this applies to all stats, it shouldn’t be used by itself. We need to use multiple stats, including WAR, to see the entire picture.
Secondly, there are two different types of WAR. Fangraphs’ calculation of WAR known as fWAR, and Baseball Reference’s version of WAR, rWAR. You will see slight differences between the two (and Baseball Prospectus’ WARP), based on how the calculations are valued.
For example, Kevin Kiermaier’s 2015 resulted in a 4.3 fWAR, but a 7.5 rWAR due to Baseball Reference valuing Kiermaier’s defense higher than Fangraph does.
Lastly, WAR does NOT mean how many wins the player contributed to the team. It means how much better is this player than a marginal bench player or a AAAA player. This is a huge misconception about WAR.
I hope you enjoyed our journey into the world of Advanced stats. It can be confusing, and overwhelming, but hopefully we were able to explain some ideas clearly.
What other stats would you like to see us cover? Do you have a favorite advanced stat to use? Let us know!