Power, speed, and an evolving landscape
Introduction
There’s something uniquely electrifying about players with power and speed that catches the eye. Somewhere along the line, the baseball community has drawn a line in the sand, stating that 20 home runs and 20 steals symbolize power/speed athleticism. This cutoff has been reasonably exclusive throughout many eras.
As you can see from this funnel plot, under 1,000 players have achieved the exclusive 20-20 club in history. Shohei Ohtani did the impossible this season and is the only player ever to achieve the 50-50 club. This is the most impressive showing of power and speed we’ve ever seen. This got me thinking though…
- How does age affect power/speed?
- What eras/years have been best for power/speed?
- With the changed rules, what’s the new 20-20?
How does age impact your ability to get a 20-20 season?
What a beautiful bell curve! The highest number of 20-20 seasons seem to peak around the time of a player’s prime, from ~25-28, which is not surprising at all. In fact, there’re more 20-20 seasons from players aged 25-28 than players of any other age. Can you guess the youngest and oldest 20-20 seasons?
Youngest:
- Vada Pinson (age 20) in 1959, Mike Trout (age 20) in 2012, and Jackson Chourio (age 20) in 2024
Oldest
- Gary Sheffield (age 38) in 2007, and Paul O’Neill (age 38) in 2001
Interestingly, that was Paul O’Neill’s sole 20-20 season, which makes it extra impressive. It’s never too late to hit your power/speed stride.
Now that we’ve covered some interesting 20-20 cases, can you think of who has the most 20-20 seasons?
If you guess Bonds, you’re right about both of them. Bobby and Barry Bonds are holding down the fort at the top with 10 seasons each. That’s some serious father-son destruction
Bobby Abreu’s next in line with 9, 20-20 seasons himself. Not too shabby.
Power and speed over the years
There’s no debate that 20-20 seasons are interesting and important, but how are they changing?
20-20 seasons took off in the 1950s due to the league-wide increase in power, reaching a peak in the 90s and 00s. In the 10s swiping bags started to decline, likely due to an increase in the three true outcomes: walks, home runs, and strikeouts. But, duly so, the MLB attacked this problem with some rule changes to increase the action on the field. Adding the pitch clock, larger bases, and a finite number of pick-off attempts completely changed the stolen base environment.
In fact, since the change of rules in 2023, you can see that 2024 has tied for the highest number of 20-20 players, and 2023 wasn’t far behind. The 20s are on pace to be the most prolific power/speed decade of baseball’s history. We’ve entered a new era of power and speed, which begs the question, what’s the new 20-20? I’ve decided to attack this question from a scarcity perspective.
Estimating the new 20-20
In this plot, you can see how from 2000-2022 the average number of players with 20-20 seasons averages 8.8. From 2023 and 2024, you can see a huge jump in the average number of players with 20-20 seasons to 18. The abundance of 20-20 seasons is our new reality, and I’m curious if we can easily model what the new threshold for power and speed should be. What’s the new 20-20?
With scarcity in mind, I decided to do a simple thresholding experiment. What stolen base cut-off can we use to get back to ~8.8, 20-20 players per season?
From this plot, you can see that we can match the scarcity seen in the past 20 years by increasing the threshold to about 30 steals while keeping the power the same. Answer: 20-30 is the new 20-20.
I opted not to change the power numbers in this thresholding to keep things simple and because I didn’t see any rule changes in 2023/2024 that have affected the state of power in the same way as speed. With the new rule changes, speeds are more prevalent and we should consider changing our biases about the pinnacle of power and speed.
Let me know if you have a better idea for analyzing how the power/speed landscape’s changing. Part of me wanted to do some predictive modeling with savant data to identify what attributes are most predictive of power/speed. If you’d like to glance at any of my code: https://github.com/sharkweekshane/baseball_modeling
Thanks for reading,
Dr. Shane Simon
