Athleticism Metric: Setting the Ground Work
With so much sabermetric work already completed, I’m creating a ton of work for myself to see if a hitter’s athleticism influences how they age? Additionally, do these “athletes” age better? I tried to jump the gun a few nights ago with an ill-fated Twitter thread where I thought about reverse-engineering the stats. Instead, I’m going to put a value on a hitter’s athleticism using some readily available metrics.
I began my search by using some advice from Bill James who commented on my Tweet.
Let's assert as Point A that "athleticism" consists of four things: Speed, strength, agility and quickness. "Strength" is the most troublesome, because you can have strength in many different body parts, and people who are strong in one area may be relatively weak in others.
— Bill James Online (@billjamesonline) June 7, 2020
He just rattled the traits off and since he’s likely forgotten more about baseball than I’ve ever learned, I’ll just focus on them. I’m guessing he’s already investigated the subject.
Compiling this information even a few years ago would have included more guesstimating but the available StatCast data simplifies the process. The first two factors, power and speed, are easy to measure using max exit velocity and sprint speed. Agility and quickness are a little tougher to nail down, but I settled on the defensive component of WAR.
The data is/are far from perfect. The first adjustment I made was to remove the pitch-framing element from catchers. I kept all the other elements including the positional and fielding adjustment. If a player can only play first base (one hit to his athleticism), he better do a great job of it. Additionally, I adjusted the fielding value to a per-game rate so all hitters are on a level playing surface since defensive WAR is effectively a counting stat.
I did consider adding a couple of factors. One was height. I read up on the ideal height for any athlete and there seems to be a link between height and injury. I hoping to use height, but in the next step. It’s going to be one of several factors.
The other input that I’m still unsettled on is a hitter’s ability to swing a bat and make contact (Contact%). After a few private debates, I think this trait falls more in the hand-eye coordination basket. I can be talked into being wrong and flip-flop on my decision.
So to get my athletic value, I took all the hitters from the past five seasons (length of available StatCast data) with a minimum of 200 PA and calculated the z-score for the three traits. I weighted each trait evenly for now. And again, I could be talked into weighing one trait more or less than the others.
For a final value, I add the three values together. At some later date, I might set these values on a 100 scale (with 100 is league average) or possibly the 20-80 scouting scale. I’ll nail down the formula first before moving on.
It’s time to start doing the idiot check. Here are the top-10 players from the past two seasons (full list).
Rank | Name | Season |
---|---|---|
1 | Francisco Lindor | 2018 |
2 | Mike Trout | 2018 |
3 | Harrison Bader | 2018 |
4 | Aaron Judge | 2018 |
5 | Matt Chapman | 2018 |
6 | Jorge Alfaro | 2018 |
7 | Trea Turner | 2018 |
8 | Giancarlo Stanton | 2018 |
9 | Kevin Kiermaier | 2018 |
10 | JaCoby Jones | 2018 |
1 | Byron Buxton | 2019 |
2 | Aaron Judge | 2019 |
3 | Javier Baez | 2019 |
4 | Harrison Bader | 2019 |
5 | Matt Chapman | 2019 |
6 | Trevor Story | 2019 |
7 | Jorge Alfaro | 2019 |
8 | Adalberto Mondesi | 2019 |
9 | Ronald Acuna Jr. | 2019 |
10 | Mike Trout | 2019 |
The two names that seem out of place are Stanton and Judge. Both are highly rated based on three traits. These two examples are why I want to jump ahead to include height showing how big bodies breakdown. I just need to tap the breaks and get athleticism down first.
Here are the bottom ten from the two seasons (worst is listed first).
Rank | Name | Season |
---|---|---|
1 | Victor Martinez | 2018 |
2 | Albert Pujols | 2018 |
3 | Kendrys Morales | 2018 |
4 | Brian McCann | 2018 |
5 | Luis Valbuena | 2018 |
6 | Justin Smoak | 2018 |
7 | Tony Kemp | 2018 |
8 | Daniel Murphy | 2018 |
9 | Mark Reynolds | 2018 |
10 | Yonder Alonso | 2018 |
1 | Albert Pujols | 2019 |
2 | Brandon Belt | 2019 |
3 | Yonder Alonso | 2019 |
4 | Justin Smoak | 2019 |
5 | Miguel Cabrera | 2019 |
6 | Domingo Santana | 2019 |
7 | Isan Diaz | 2019 |
8 | Luis Arraez | 2019 |
9 | Kendrys Morales | 2019 |
10 | Neil Walker | 2019 |
While not a household name, Tony Kemp seems out of place. Just digging into his inputs, he has almost no power with average to below-average speed and defense. Maybe Kemp is the type of player this process is hoping to find.
So readers, what seems off? Please scrutinize the full list and nitpick everything. I’ll remain active in the comments and can quickly create new rankings if needed for comparison. Once I get athleticism done, I can then start investigating if athletic hitters age better or not.
Jeff, one of the authors of the fantasy baseball guide,The Process, writes for RotoGraphs, The Hardball Times, Rotowire, Baseball America, and BaseballHQ. He has been nominated for two SABR Analytics Research Award for Contemporary Analysis and won it in 2013 in tandem with Bill Petti. He has won four FSWA Awards including on for his Mining the News series. He's won Tout Wars three times, LABR twice, and got his first NFBC Main Event win in 2021. Follow him on Twitter @jeffwzimmerman.
I don’t know if Tony Kemp is the type of player you’re looking to find. You might just be finding weak or injured hitters! Your model would be most useful if it identifies good players who are high risk. For example, how does Freddie Freeman compare to Anthony Rizzo? Why is the projected 2022 WAR differential between the two 30 year old first basemen higher than the projected 2020 WAR differential, and is this a decline-related prediction for Rizzo?
My suggestion:
1. Perhaps change defensive value to an aspect of the defense that you think matters, like range.
2. We’re getting ahead of ourselves perhaps, but have you looked at which factors are correlated with injury or decline? That may help you select features for further investigation (as in my 1st suggestion).
3. I know you’re looking for predictors of decline, but perhaps the best predictor of future decline is a recent decline in a subset of these metrics. It may answer a different question (Which player has started his decline?) than what you originally set out to answer (How will X player decline based on what we know about him?), so you may not decide to pursue it. Or, does the recent Trout speed score decline portend bad things for him? That’s a potentially very interesting question!
I like the range recommendation a lot.
My initial ‘yeah but’ while reading is that the distinction between agility and quickness seems awfully slight. But range (or the full OAA) could address both.
1. I still think the positional adjustment needs to be incorporated. The average range at 1B is not the same as the average range in centerfield.
2. I’ve gone over the factors several times with no luck. That’s why I’m examining it with a possible new input (links at the beginning):
https://fantasy.fangraphs.com/batter-injuries-and-future-performance/
3. You jumping ahead to the next step. I want to grade a player’s athleticism first without considering the next step. The grade could be used for other studies.