With the promotion of Elly De La Cruz this week, the fantasy baseball world is understandably pumped for yet another top rookie making an immediate impact. For the one or two of you out there who haven’t been following Elly this week, he is indeed making an immediate impact, to the tune of .364/.462/.909 through Thursday games and completely filling out the sheet with a 2B, 3B, HR, SB, and a pair of steaks.
Because of Elly, I watched 2 Reds games this week – which is 2 more than I have so far this season. I suspect I’m not alone there. My conclusion: the Reds are going to be a fun team to follow this summer…and I’m here for it!
Now that so many rookies are in The Show and making meaningful contributions to their respective teams, the race for AL and NL Rookie of the Year (ROY) promises to be one worth watching. Speaking of ROY voting, there’s no specific criteria for selecting the most deserving player. Some voters base their selections on individual production (HRs, SBs, etc.), some pick based on what their eyes tell them, while others dabble in our world of analytics (like, WAR).
There’s really no right or wrong formula, although some are clearly better than others. As you may have guessed, I favor the data-driven ones and that’s going to be our topic to explore today.
If you read my weekly articles, you know I have a list of favorite advanced analytics to explore. I’ll revisit many of those today as we look specifically at this year’s crop of rookies to see which are primed to make the most impact. This one will be fun and I’m ready to jump right in.
Batted Balls
Let’s start with batted balls. Going back to my May 26 article, I Ain’t In No Slump, I Just Ain’t Hitting, I shared batters who were both overachieving and underperforming based on their xBA-BA numbers.
As a reminder:
BA – H/AB. Pretty simple.
xBA – Expected BA is not quite so simple. xBA is a Statcast metric that measures the likelihood that a batted ball will become a hit. Each batted ball is assigned an xBA based on how often comparable balls, in terms of exit velocity, launch angle and even sprint speed (for “topped” or “weakly hit” balls). Lucky for us, this stat is readily available to us for all players. We’ll make good use of that today.
So, how do the rookies fare here?
The Overachievers
We’ll start with the players whose BA is currently outperforming their xBA.
The Underperformers
And now, let’s look at the players whose BA is underperforming their xBA.
Just as we did in the May 26th article, you’ll want to also look at such metrics like Contact Rate (Ct%), Batting Eye (Eye), Ground Ball (GB), Line Drive (LD), Fly Ball (FB) and Batting Average on Balls in Play (BABIP) to get more of a full picture of these BA trends. Again, refer back to that article as a quick guide.
wRC+
Sticking with putting balls in play, we next move to wRC+. I wrote about this one on May 5 in Blake’s Gone Fish’n For Carp’s wRC+. In that one, I broke down this complex calculation and talked a bit about Park Factors. That’s way too much information for today’s article but I encourage you to go back and read up on this very useful analytic.
As a short reminder though, Weighted Runs Created Plus (wRC+) is a statistic which attempts to credit a hitter for the value of each outcome (single, double, etc.) rather than treating all hits or times on base equally, while also controlling for park effects and the current run environment.
wRC+ is scaled so that league average is 100 each year and every point above or below 100 is equal to one percentage point better or worse than league average. In theory, this makes wRC+ a better representation of offensive value than AVE, RBI, OPS, or WOBA.
Here are the rookies with a current wRC+ > 100:
Power
Switching to power, specifically Isolated Power (ISO), you’ll have to go way back to February 25 for my initial ISO article, In Search Of…ISO, or read the update to it from April 21, Still In Search Of…ISO. I know, such original titles, right?
Anyway, you may recall from those masterpieces that ISO is calculated one of two ways:
- ISO = (2B + 2 x 3B + 3 x HR) /ABs
- ISO = Slugging % (SLG) – Batting Average (BA)
Here is how we rate ISO:
So, which rookies have ISOs > .200, you ask? I have that for you right here:
More Power
Just a week ago, I went on a journey in search of more power, Did You Say…More Power.
Here’s an excerpt from that Pulitzer-worthy piece, “I set out looking at differences in Batted Ball data – Hard% versus Soft%. Barrels always pull me in like a magnet, so I added those to my spreadsheet as well. EV? Yep, need to sift through those too! Add in a dash of HardHit% and I think our recipe is all set. Throw it all in the Excel blender and let’s see what comes out.”
In summary, I set some thresholds for the various analytics and looked at how the top HR hitters were ranking amongst the leaders in each. It produced some interesting results and I’d recommend a quick read to see for yourself. I’ll do the same number crunching for the rookies here.
But first, I want to show this graphic explaining what a barrel is. This is by far the best illustration of a barrel that I’ve found (thanks again, @RotoClegg) and I like to use it any chance I can:
Now to the findings:
Hard% – Soft%
- 14 rookies have Hard% rates > 22% their Soft%
- Of the top 15 ranked HR hitters, 46% have rates > 22%
Barrel%
- 8 rookies have Barrel% > 12%
- Of the top 15 ranked HR hitters, only 20% are > 12%
EV
- 4 rookies have EV > 92 mph
- Of the top 15 ranked HR hitters, only 13% are > 92 mph
Max EV
- 2 rookies have a max EV > 114 mph
- Of the top 15 ranked HR hitters, only one, or 7%, have a max EV > 114 mph
HardHit%
- 7 rookies have HardHit% > 48%
- Of the top 15 ranked HR hitters, 13% are > 48%
The one thing that really sticks out here, and is intuitive if you think about it, these raw power numbers reflect rookies (mostly in their early 20s) who are still growing. If we continued to track these, I have no doubt the numbers would move closer to the MLB numbers discussed last week.
Here is the summary chart:
Is Luke Raley the favorite for AL ROY then? We have one more category to look at then we can ponder the bigger questions.
Speed
SBO (Stolen Base Opportunity)
To read my original piece on SBO, refer back to the February 18th article, My Name Is TLB And I’m An Analytholic.
As the abbreviation implies, the SBO attempts to quantify the opportunity a player has to steal a base. Since most SBs occur when a player steals second base, it is heavily dependent on how often the hitter reaches first base. SBO is calculated as:
SBO = (SB + CS)/(1B+BB)
A SBO of 20% (0.20) or more is a common threshold for identifying players to target. Ultimately, we want players who not only have the OPPORTUNITY but also a high EFFICIENCY for achieving SBs. So, instead of just looking at SBO or SB%, I will typically target players who meet both of my thresholds.
10 rookies have SBO values greater than 20%. Remember, that’s OPPORTUNITY. What about results?
Of the top 11 rookie swipers (7 or more SB), 64% have SBO values greater than 20%.
Here is the breakdown:
Summary
We covered a lot of ground here from batted balls to power and speed. I can hear you out there, “That’s great and all, but what is the bottom line?”
To answer that question, I ranked all the rookies in each of the categories we’ve covered today, then summed the ranks across the board. Lowest cumulative number (in ranks), wins. Note: for xBA-BA, I gave weight to the underperformers, giving benefit of the doubt that their AVG will correct to the positive.
With that, here are the very first, completely subjective, official Analytics Anonymous listing of top 3 ROY contenders for both the AL and NL (drum roll, please):
AL:
- Luke Raley, Tampa Bay Rays
- Josh Jung, Texas Rangers
- Gunnar Henderson, Baltimore Orioles
NL:
- Corbin Carroll, Arizona Diamondbacks
- Jordan Walker, St Louis Cardinals
- Matt Mervis, Chicago Cubs
Before I end this, and because I love working with data, I give you one extra table with the respective ranks for these 6 ROY contenders:
Author’s Note: Here’s a real-time picture of Grey seeing Jordan Walker make the top 3 ROY contenders list.
Anyway, there you have it…another fun journey through the world of baseball analytics. As I always say, keep sifting through the number and see where it takes you. Sometimes the conclusions are obvious and other times, not so much. No matter what though, it’s always a fun ride (at least for me anyway).
Don’t forget you can follow me on Twitter: @Derek_Favret.
Until next time, my friends!
