Smash Factor: A Data-Driven Approach to Assessing the Hit Tool
Assessing the hit tool poses a real challenge for coaches and scouts alike, given the lack of consensus amongst talent evaluators about how to define it. Since there’s no single definition for what it encompasses, most in the industry will lean on some combination of raw tools, contact rate, and strikeout avoidance to grade out players. This approach may risk viewing all contact as good contact, as well as overlapping with the power tool before raw strength is considered.
Like with bat speed and the power tool, a single, consistent metric would be hugely beneficial to accurately assessing, training, and measuring improvement in both the frequency and quality of contact. Thanks to Blast Motion sensors, at Driveline we can bring the various components of hitting skill under a single, data-driven roof with Smash Factor.
What is Smash Factor?
Smash Factor measures the collision efficiency of the bat and ball at contact, in essence telling us how much of a swing’s bat speed was converted into exit velocity. In simpler terms, balls that are “squared up” with minimal deflection or glancing at contact will have the highest collision efficiencies, and therefore the highest Smash Factors.
These batted balls generally have lower spin and higher exit velocities, avoiding “lazy” flyouts or line drives that “balloon” or “hang” rather than carrying into the gaps. “Flush” contact is a good indicator that a hitter’s bat path is not cutting across the zone or lacking adjustability, making it a key point of analysis in training the hit tool. The full equation for Smash Factor comes from Alan Nathan’s work on bat performance, and is as follows:
Smash Factor =1 + (Exit Velocity – Bat Speed)/(Pitch Speed + Bat Speed)
By considering both bat speed and exit velocity, Smash Factor gives unique insight into how well a player puts their strength and raw tools to use. It is also easy to apply in non-game training environments where hit tool stalwarts like contact- and K-rates are much harder to measure accurately and translate to a game context.
As we’ve covered in the past, Smash Factor reaches reliability within just 20 balls in play, giving us the confidence to evaluate bat-to-ball skills long before the tens or even hundreds of at-bats necessary for outcome-based hit tool measures like K% or BABIP to become reliable.
Smash Factor also outperforms discipline-based hit tool metrics like Z-Contact and O-Contact rates on reliability because it accounts for performance on all pitches together, rather than specifying strikes or balls one at a time.
Evaluating the past three months of in-gym data with Cronbach’s Alpha, our preferred statistical test for measuring self-correlation, suggests that Smash Factor is the most reliable of the three metrics. A metric is deemed “reliable” once Alpha eclipses 0.7:
Considering Smash Factor just on batted balls, however, only covers the quality part of our contact skill evaluation. By assigning whiffs and fouls a Smash Factor of 0, taking a player’s average describes both how often and how well they make contact. This is where the value added by Smash Factor is clearest—K% focuses on at-bat level performance, BABIP describes batted-ball luck, and Z- and O-Contact rates only apply to proportions of all pitches seen. Smash Factor is usable on every pitch a batter swings at, making it faster to reliability and a more robust single measure of hitter skill.
Smash Factor in Action
Rather than relying on a “5-Tool” model of player evaluation (or none at all), we focus on a “Big 3” of skills that every hitter needs—Bat Speed, Bat-to-Ball, and Swing Decisions. We’ve covered at length why bat speed matters and how we train it, as well as our approach to quantifying swing decisions and plate discipline. Completing the picture, then, is Smash Factor and its reliable description of bat-to-ball skill.
Using launch monitors like HitTrax and Rapsodo in addition to in-house contact- and result-tracking tools helps us monitor Smash Factor and players’ bat-to-ball skill in batting practice, mixed-pitch training, and live at-bats. Paired with skill-specific programming such as drills using Driveline Hitting Plyo Ball ®, contact skill deficiencies can be directly addressed, instead of defaulting to the bare accumulation of reps to drive adaptation.
Using just one extra equation we can stretch the benefits of bat sensors beyond analyzing swing characteristics. Smash Factor helps contextualize other bat sensor feedback points like Attack Angle, Connection, and Vertical Bat Angle by connecting them with the quality of contact they produce for an individual.
There are few universal “good” or “bad” elements to bat path, and having quantitative contact quality feedback via Smash Factor helps narrow down the desired range of each metric for each athlete. Bringing objectivity and consistency to evaluating the hit tool can be a tall task for any trainer or analyst, but Smash Factor unifies both the language and data used at Driveline to monitor the previously-nebulous bat-to-ball skill.
I have said for years that smash factor was the optimal metric. This obviously comes from golf but a 1.5 is optimal in golf based on club and ball technology. Although it doesn’t incorporate spin rate or launch angle based on what club is used.
I think the future of baseball hitting metrics is smash factor combined with launch angle and then spin rates. We overlook spin in hitting but there is a reason some swings produce optimal outcomes. Baseball is easier because we have a standard bat and ball. Imagine we add in spin and launch angle, then we can have exact expected outcomes for analytics. I’m surprised it has taken this long for baseball to catch up. I wrote a few articles about this in my website http://www.caphitting.com a few years ago.
I looked at what the optimal outcome should be based on launch angle. Then divided that with the actual outcome and came up with smash factor. This is because we didn’t know in game bat speed metrics.
It’s also interesting for bat fitting and what bats, lengths, weights produce the optimal smash factor.
Then we can look at things like optimal outcomes based on pitch type and pitch location vs hitters hitting locations. It’s all super interesting and the future of baseball. I should have probably kept working on this years ago. I tried to email my data to driveline and Nathan. He and I talked a lot in the past. His outcomes were simply based on bat speed and pitch speed but I always argued that bat mass, player mass, and player skill (ended up being smash factor) are extremely important. I’ve seen players with lower measured bat speed hit balls further than higher swing speed consistently. There is a reason for that.