If you’ve been around the block as long as I have, you remember Dick Clark hosting this show when you were a kid. If you’re a bit younger, Michael Strahan is the host associated with the show. Somewhere in between? Well, there have been a number of hosts, including Bill Cullen (the original Price Is Right emcee) and even Donny Osmond. Regardless of the host, the game has changed little through the years. They put up catchy titles that can be interpreted a whole slew of ways, then make their participants guess words about a topic that is relatively straight forward.
That’s a perfect backdrop for this edition of Analytics Anonymous. When you hear the phrase, “pull my finger” what image jumps into your mind? Did your Uncle Dan “pull” this on you when you were a kid? Your Dad? Or was it Aunt Betty? No matter who, it’s safe to say we’ve all been caught by this one.
So, what’s the connection to this edition of AA? Does it mean the topic stinks? I sure hope not! No, I just used the clever bait to draw you in so we can talk “Pull Rates.”
See, catchy title…but the topic is relatively straight forward!
Now that we’ve eliminated the threat of odiferous emanations transmitting through cyberspace, let me elaborate on the relevance to fantasy baseball. When I started brainstorming topics for this edition of AA, I was looking at HR rates and became fixated on Pull Rates (Pull %). We know players that tend to pull the ball generally hit it harder (and farther). So, I decided to explore it further to see if I could find a useful connection between increases in both Pull % and HR rates.
Let’s not forget:
Acknowledging the obvious, I realize there are a lot of factors that come into play when we look at HR rates. Barrel Rates, Launch Angle, Exit Velocity, Park Factors, etc. etc. etc. Simply saying increased Pull % equals more long balls doesn’t pass the logic test. However, at a macro level, there may be a quick and easy way to SCREEN and IDENTIFY players we need to take a deeper dive on based on increased (or decreased) Pull %. That’s the goal of this exercise.
Most likely, we would use this analysis pre-draft as a means to identify our power targets. Perhaps it’ll even provide us a good metric to evaluate players during the season for free agency or trades to beef up our power numbers. Or maybe there’s nothing there at all. That’s ok too. Sometimes our research won’t result in a breakthrough but it’s sure fun to see where the data takes us.
Anyway, that’s a brief explanation on how I got here. Now, on to the data…and the fun!
To get us started, I limited the analysis to the top 25 hitters with the highest change in Pull % from 2022 to 2023.
You’ll note in the table above that I also included the HR total for both years. Notice there are several players (Dansby Swanson, Willy Adames, Hunter Renfroe, etc.) who pulled the ball more in 2023 but hit fewer HRs. Let’s see if we can use some of these handy analytics to help explain why.
Expanding the data, here is the same table with additions: changes in HR/FB rate, LD%, FB% and Hard%:
Reactions:
- Of the 12 players who had an appreciable (three or more) increase in HRs, 92% also saw increases in at least three of the four other categories (HR/FB, LD%, FB%, and Hard%)
- Conversely, of the 13 players with steady (no more than two more) or decreasing HR totals, 77% also had a decrease in at least two of the other categories (HR/FB, LD%, FB%, and Hard%)
Take a look at this Figure:
For the top 25 hitters in this analysis, this Figure plots the HR rank (2023 HR – 2022 HR) versus the respective rank across the four analytics (HR/FB, LD%, FB%, and Hard%). You’ll note that the general trend tracks pretty well over this very small sample size.
Conclusions:
My first conclusion: There’s something worthwhile here. Remember, we didn’t set out in search for the Arc of the Covenant, rather we’re looking for a means to identify potential power producers. The results of this certainly makes me want to dig deeper to understand why some players surged and others were more stagnant.
My second conclusion: Return on investment. This analysis took some time and while the results are interesting, I don’t really see an efficient way to do this in real time. I may play with this again during the season once the in-season analytics stabilize. Perhaps around the All-Star Break as a means to identify potential second-half power candidates.
Regardless, this was another fun analytics exercise for me (hopefully you enjoyed it a little too). As I’ve said many times in this column, it’s fun to take a look at the data in different ways and see where it takes us.
Now, I’m on to my next brainstorming session. My offer to all fellow Analytics Anonymous members (you know who you are…), if you want me to explore an idea you have, drop the details in the comments below. Let’s have some fun!
When you come to the Razzball site, you can find me on both on the baseball and football sides. Just look for “The Lineup Builder” and you’ve found me! I’m doing QB rankings all offseason in addition to hosting these baseball sessions. No rest for the weary!
Also, be sure to follow me on Twitter/X @Derek_Favret. I’m now on BlueSky as well (@dfavret.bsky.social).
Until next time, my friends.
I have wanted to analyze the increase or decrease in players who have gone to Driveline, and other academies. What is the net of all players? Seems like a treasure trove of info to be gleaned!
I agree, that would be useful for us fantasy owners. Maybe Driveline has that on their marketing brochure!
Interesting stuff. As a right handed hitter I can pull with power, and even get lucky to right center if I am late. I cannot hit it out to straight right, so pull makes sense. So few guys have all field power.
Santander is interesting, as all his stats are better except for K’s and HR. I wonder about luck, weather, and situation. Runners on second and third, trailing by one, and the wind blowing in. Why not a line drive double? Some parts of the game are hard to quantify.
Santander is always a hard player for me not to roster. No Ks and HRs from both sides of the plate already is encouraging. That 28% LA is even better. Fun stat: Santander is SLG 2.500 on changeups in 3 games. I see a lot more sliders in his immediate future. Let’s revisit in a couple weeks but the early returns are nice.