“The hit tool sits atop the great pyramid of tools, trumping its own off-spring–power–as well as the three remaining tools in a position prospect’s physical cache: speed, glove, and arm. The hit tool is the simple measure of how often a ball is properly squared up, driven with authority, and deposited into the field of play.”

Jason Parks, Extra Innings: More Baseball Between the Numbers from the Team at Baseball Prospectus, 2012

In the world of baseball scouting, a hit tool grade for a prospect reigns supreme. A relatively small difference in the grade could be the difference between a player with world-class power hitting 40 home runs at the major league level or having trouble surpassing High-A. In the public realm, whether it be prospect analysis, fantasy baseball, writers, or just general fans of the game, the term is integrated in public player evaluation.

I could be wrong and maybe this is just my evaluation, but it does feel like the term gets oversimplified by getting directly tied to how many a times a player swings-and-misses or strikes out. It’s easy to assume that a player that strikes out a fourth of the time they come up to bat has a below-average hit tool, I’ve probably been guilty of this same exact thing in the past. But it’s not always the accurate assumption.

But as shown in the quote above, hit tool is more than just a hitter’s swings-and-misses and strikeout totals. The analysis for hit tool doesn’t stop when a player *does* make contact. Things like a hitter’s barrel control need to be considered more in the public realm.

Why barrel control doesn’t get as much attention as contact-skills is very understandable. Analyzing it isn’t as simple as putting total strikeouts over total plate appearances. In the Statcast-era, fans and public analysts have been overwhelmed with all the new measurements produced for player evaluation. But for hitter evaluation, there’s unfortunately not many data points past exit velocity and launch angle that are publicly available.

Bat speed is a very crucial measurement for a hitter’s development. While Trackman and Statcast do not track it, other technological resources such as BlastMotion do. And thanks to the past research of many smart people, we can reverse engineer a hitter’s bat speed using publicly available Statcast data to generate a hitter’s estimated bat speed, with a useful level of confidence that it is measuring what it is intended to measure.

Back in 2003, Dr. Alan Nathan wrote a research paper that characterized the performance of baseball bats (there’s lots of advanced physics terms in this, but I highly recommend the read). In that paper, he gave out his formula for calculating a hitter’s exit velocity.

**EV = e _{A}v_{ball} + (1 + e_{A})v_{bat}**

This is where *e _{A}* equals the collision efficiency of the bat,

*v*equals the velocity of the incoming pitch, and

_{ball}*v*equals the speed of the swing.

_{bat}David Marshall over at the community page of FanGraphs did a good job of breaking down the process of how he reverse engineered bat speed, citing that because collision efficiency and and exit velocity have a linear relationship, and that because we can assume that the average of the upper-echelon and lower-echelon of a hitter’s exit velocities can respectively match up with a 0.21 and -0.10 collision efficiency, a linear regression formula can be ran on a player’s average exit velocity to calculate their average collision efficiency.

I calibrated

efor each hitter with at least 100 BBE in a season by estimating that the average of the top 15 BBE by exit velocity corresponds to_{A}e=0.21 and the average of the bottom 15 BBE by exit velocity corresponds to_{A}e= -0.1 for each player. Since_{A}eand EV are related linearly, we can compute_{A}efrom EV for each player. Finally, I will assume that every player uses a standard 34 in., 32 oz. bat. Since Nathan’s study used a 34 in., 31 oz. bat, I subtracted 0.42 MPH from the estimated swing speeds, because every extra ounce reduces that bat speed by about 0.42 MPH._{A}

To perform this analysis myself, I decided to calculate the average 8th percentile exit velocity for each hitter in a season since 2015, along with their average 92nd percentile exit velocity. With the 92nd percentile average exit velocity, a fair enough assumption can be made that the hitter is squaring the ball up perfectly (collision efficiency = 0.21), while with an 8th percentile average exit velocity, the hitter is missing the ball with the barrel of the bat significantly (collision efficiency = -0.1).

As for the results, here were the top 20 hitters since 2015 in average 92nd percentile exit velocity (minimum 50 batted balls total). The total amount of qualified hitter seasons equals 2,662.

Player Name | Season | Batted Balls (n=) | 92nd Percentile Exit Velocity |

Giancarlo Stanton | 2020 | 52 | 116.0 |

Giancarlo Stanton | 2016 | 270 | 115.5 |

Giancarlo Stanton | 2015 | 181 | 115.1 |

Giancarlo Stanton | 2018 | 390 | 115.0 |

Aaron Judge | 2017 | 338 | 114.6 |

Giancarlo Stanton | 2017 | 437 | 113.9 |

Franchy Cordero | 2018 | 80 | 113.6 |

Aaron Judge | 2018 | 247 | 113.3 |

Nelson Cruz | 2017 | 425 | 111.9 |

Nelson Cruz | 2018 | 377 | 111.9 |

Aaron Judge | 2019 | 227 | 111.8 |

Gary Sanchez | 2019 | 262 | 111.7 |

Joey Gallo | 2017 | 252 | 111.7 |

Miguel Sano | 2020 | 99 | 111.6 |

Daniel Palka | 2018 | 250 | 111.5 |

Joey Gallo | 2018 | 275 | 111.5 |

Mark Trumbo | 2016 | 428 | 111.5 |

Joey Gallo | 2019 | 125 | 111.4 |

Jorge Alfaro | 2018 | 203 | 111.3 |

Chris Iannetta | 2019 | 91 | 111.2 |

Next, the bottom 20 in average 92nd percentile exit velocity.

Player Name | Season | Batted Balls (n=) | 92nd Percentile Exit Velocity |

Dee Strange-Gordon | 2020 | 61 | 93.4 |

Ender Inciarte | 2020 | 93 | 94.4 |

Jake Elmore | 2016 | 55 | 94.7 |

Billy Hamilton | 2019 | 212 | 94.9 |

Luis Sardinas | 2015 | 71 | 95.0 |

Billy Hamilton | 2018 | 350 | 95.2 |

Ronald Torreyes | 2018 | 82 | 95.4 |

Pete Kozma | 2015 | 66 | 95.7 |

Dee Strange-Gordon | 2017 | 524 | 96.0 |

Jake Elmore | 2015 | 113 | 96.1 |

Billy Hamilton | 2015 | 300 | 96.1 |

Daniel Castro | 2015 | 91 | 96.2 |

Billy Hamilton | 2017 | 434 | 96.2 |

Tony Kemp | 2020 | 81 | 96.3 |

Ben Revere | 2017 | 268 | 96.4 |

Jesmuel Valentin | 2018 | 56 | 96.4 |

Billy Hamilton | 2016 | 299 | 96.5 |

Chris Stewart | 2017 | 103 | 96.6 |

Darwin Barney | 2017 | 269 | 96.6 |

Billy Burns | 2016 | 263 | 96.6 |

Doing the same for average 8th percentile exit velocity, here’s the top 20.

Player Name | Season | Batted Balls (n=) | 8th Percentile Exit Velocity |

Corey Seager | 2018 | 85 | 80.3 |

Giancarlo Stanton | 2015 | 181 | 79.6 |

Kyle Schwarber | 2015 | 152 | 78.6 |

Justin Ruggiano | 2015 | 78 | 78.3 |

Mike Brosseau | 2020 | 55 | 78.2 |

Greg Bird | 2015 | 105 | 78.2 |

Matt Olson | 2018 | 397 | 77.9 |

Mitch Haniger | 2016 | 76 | 77.6 |

Nelson Cruz | 2016 | 424 | 77.4 |

Joey Gallo | 2018 | 275 | 77.4 |

Joey Wendle | 2016 | 74 | 77.1 |

Miguel Sano | 2015 | 176 | 76.9 |

Mike Trout | 2020 | 144 | 76.9 |

Miguel Cabrera | 2018 | 108 | 76.8 |

Alex Avila | 2016 | 91 | 76.8 |

Tommy La Stella | 2015 | 76 | 76.7 |

Tyler Saladino | 2018 | 80 | 76.7 |

David Ortiz | 2015 | 446 | 76.6 |

Teoscar Hernandez | 2016 | 66 | 76.5 |

Jose Bautista | 2015 | 447 | 76.5 |

The bottom 20 in average 8th percentile exit velocity.

Player Name | Season | Batted Balls (n=) | 8th Percentile Exit Velocity |

Roman Quinn | 2020 | 68 | 34.7 |

Cedric Mullins | 2020 | 102 | 35.2 |

Garrett Hampson | 2020 | 107 | 40.9 |

Adalberto Mondesi | 2020 | 148 | 41.7 |

Danny Jansen | 2020 | 91 | 48.0 |

Giancarlo Stanton | 2020 | 52 | 50.2 |

Alex Gordon | 2020 | 126 | 51.3 |

Ryan Rua | 2017 | 77 | 53.9 |

Hanser Alberto | 2020 | 189 | 54.0 |

Charlie Tilson | 2018 | 85 | 54.3 |

Nicky Delmonico | 2017 | 107 | 54.8 |

Andrew Romine | 2018 | 75 | 55.0 |

Ichiro Suzuki | 2017 | 160 | 55.1 |

Trent Grisham | 2020 | 151 | 55.2 |

Nick Franklin | 2017 | 83 | 55.8 |

John Hicks | 2017 | 122 | 56.4 |

Roberto Perez | 2017 | 145 | 56.7 |

Lane Adams | 2017 | 71 | 57.0 |

Edward Olivares | 2020 | 72 | 57.2 |

Dee Strange-Gordon | 2020 | 61 | 57.2 |

Now with the 8th and 92nd percentile exit velocity data, it’s time to run separate linear regressions for each player to estimate their average collision efficiency. Let’s use 2020 Fernando Tatis Jr. as an example. The x-values for his regression would be his 8th and 92nd percentile exit velocities, so 75.9 and 110.1. Those are matched up against the y-values for collision efficiency, -0.1 and 0.21. With a slope (0.009) and intercept (-0.788) generated for him, we can plug his average exit velocity (95.9) into a linear regression formula and have a fitted value of 0.082, his estimated average collision efficiency.

Player Name | Season | Batted Balls (n=) | Average Collision Efficiency |

Danny Jansen | 2020 | 91 | 0.109 |

Luis Torrens | 2020 | 54 | 0.106 |

Josh Rojas | 2019 | 76 | 0.105 |

Martin Prado | 2017 | 119 | 0.100 |

Kelby Tomlinson | 2017 | 143 | 0.097 |

Elvis Andrus | 2020 | 88 | 0.096 |

Asdrubal Cabrera | 2017 | 395 | 0.096 |

Willie Calhoun | 2020 | 84 | 0.096 |

Nick Ahmed | 2017 | 128 | 0.095 |

Zack Granite | 2017 | 85 | 0.095 |

Joey Bart | 2020 | 62 | 0.095 |

Ty Kelly | 2017 | 67 | 0.095 |

Ke’Bryan Hayes | 2020 | 64 | 0.095 |

Freddie Freeman | 2020 | 176 | 0.095 |

DJ LeMahieu | 2020 | 176 | 0.095 |

Steve Clevenger | 2016 | 51 | 0.094 |

Dixon Machado | 2015 | 53 | 0.094 |

Nomar Mazara | 2020 | 92 | 0.094 |

J.P. Crawford | 2018 | 77 | 0.094 |

Jose Abreu | 2020 | 186 | 0.094 |

Now with an estimated average collision efficiency and publicly available pitch velocity and exit velocity data, we can rework Dr. Nathan’s exit velocity formula to create an estimated bat speed formula.

*Estimated Bat Speed = ((Average Exit Velocity – ((Average Pitch Velocity) * Average Collision Efficiency)) / (1 + Average Collision Efficiency)) – 0.42*

Finally, here are the results. The top 20 estimated bat speeds since 2015.

Player Name | Season | Batted Balls (n=) | Average Bat Speed |

Giancarlo Stanton | 2015 | 181 | 88.4 |

Corey Seager | 2018 | 85 | 86.0 |

Joey Gallo | 2018 | 275 | 85.0 |

Giancarlo Stanton | 2016 | 270 | 84.6 |

Steven Moya | 2016 | 57 | 84.5 |

Nelson Cruz | 2016 | 424 | 84.4 |

Kyle Schwarber | 2015 | 152 | 84.4 |

Aaron Judge | 2017 | 338 | 83.9 |

Franchy Cordero | 2018 | 80 | 83.9 |

Matt Olson | 2018 | 397 | 83.8 |

Pedro Alvarez | 2016 | 236 | 83.6 |

Mitch Haniger | 2016 | 76 | 83.1 |

Aaron Judge | 2019 | 227 | 83.1 |

Jose Bautista | 2015 | 447 | 83.0 |

Nelson Cruz | 2019 | 307 | 82.8 |

Jorge Soler | 2016 | 157 | 82.8 |

Miguel Sano | 2015 | 176 | 82.8 |

Matt Chapman | 2018 | 378 | 82.7 |

Jung Ho Kang | 2019 | 114 | 82.7 |

Nelson Cruz | 2018 | 377 | 82.7 |

With estimated collision efficiency and bat speed values, there are now measurements to help assess a player’s ability to control the barrel of their bat and drive the ball, two key components of the hit tool. Just needing contact frequency skills, what if they all could be combined to create a more complete quantitative measurement of the hit tool?

That’s where Smash Factor comes into play.

**Smash Factor = 1 + (Exit Velocity – Bat Speed) / (Pitch Speed + Bat Speed)**

A few days ago, Noah Thurm, Dan Aucoin, and Max Dutto of Driveline released a highly-informative piece that explained the metric.

Smash Factor measures the

collision efficiencyof 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.

They also detailed how the metric can be a more complete measurement of a hitter’s hit tool once foul balls and whiffs are considered.

Considering Smash Factor just on batted balls, however, only covers the

qualitypart 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.

Finally, with all the data and estimations in place, Smash Factor (with the fouls and whiffs considered) can be calculated, giving what seems to be the most complete statistical measurement of a hitter’s hit tool. Since 2015, here are the top 20 Smash Factors in a season, minimum 200 batted balls. As you can see, Ben Revere, Andrelton Simmons, and David Fletcher reign king in this metric.

Player Name | Season | Batted Balls (n=) | Overall Smash Factor |

Ben Revere | 2016 | 300 | 0.642 |

Ben Revere | 2015 | 512 | 0.605 |

Andrelton Simmons | 2018 | 490 | 0.605 |

Ben Revere | 2017 | 268 | 0.600 |

Andrelton Simmons | 2015 | 459 | 0.589 |

Mookie Betts | 2017 | 552 | 0.588 |

Andrelton Simmons | 2016 | 401 | 0.585 |

David Fletcher | 2019 | 500 | 0.584 |

David Fletcher | 2018 | 250 | 0.578 |

Ben Zobrist | 2015 | 428 | 0.575 |

Mookie Betts | 2016 | 585 | 0.566 |

Martin Prado | 2016 | 527 | 0.563 |

Johnny Giavotella | 2016 | 299 | 0.561 |

Eric Sogard | 2015 | 286 | 0.559 |

Denard Span | 2015 | 215 | 0.559 |

Michael Brantley | 2015 | 461 | 0.557 |

Andrelton Simmons | 2019 | 329 | 0.557 |

J.J. Hardy | 2016 | 337 | 0.554 |

Martin Prado | 2015 | 435 | 0.550 |

Tommy La Stella | 2019 | 256 | 0.548 |

Investigating the year-over-year correlation for overall smash factor shows the metric is a consistent skill measurement for hitters, as expected. The r^2 for hitters with back-to-back seasons with at least 200 batted balls equals 0.7.

With a new and improved measurement for hit tool, we can stack the new metric up against more simple and traditional stats tied to hit tool, such as strikeout rate. Looking specifically at 2019 hitters with at least 250 plate appearances (n = 208), there seems to be a very strong relationship between strikeout rate and Smash Factor (r^2 = 0.8).

But while there’s a solid relationship between the two, some hitters on the extreme find themselves as outliers, as Smash Factor would either lower or heighten their perceived hit tool (K%+). Some examples being…

- Jeff McNeil: 139 K%+, 98 Smash Factor+
- Luis Arraez: 165 K%+, 131 Smash Factor+
- Daniel Vogelbach: 83 K%+, 103 Smash Factor+
- Javier Baez: 73 K%+, 96 Smash Factor+
- Adalberto Mondesi: 70 K%+, 87 Smash Factor+

The hit tool is the most important skill a hitter can have in this game. Yet, varying definitions of the term and lack of publicly available data have made it something not easy to analyze. But thanks to the great research of others, estimated bat speed, collision efficiency, and Smash Factor make it easier.

**Related Work**

- Smash Factor: A Data-Driven Approach to Assessing the Hit Tool, Driveline Baseball, By Noah Thurm, Dan Aucoin, and Max Dutto
- Reverse Engineering Swing Mechanics from Statcast Data, Community FanGraphs, By David Marshall
- Characterizing the performance of baseball bats, By Dr. Alan Nathan
- Debunking Bat Speed Myths, Driveline Baseball, By Max Dutto, Alex Caravan, and Dan Aucoin