Analyzing Quantitative Object-to-Helmet Impact Data

Windpact

Suggestions For Safety Improvement

Professional baseball players run the risk of head injury no matter what position they play, but batters are put at exceptional risk for sustaining helmet hit-by-pitch (H-HBP) injuries in a game. H-HBP injuries are alleged to account for approximately half of all concussions sustained by high school and collegiate players, according to a recent descriptive epidemiological study conducted by both Cleveland Clinic and Ohio State University researchers.

Moreover, diagnosis of concussion is often tricky to conclude directly after an H-HBP due to numerous concurrent factors leading up to the injury such as pitch velocity, the amount of time the batter was on the ground following the injury, the delay to first responder appraisal, and the number of days missed. The best way to assess player concussion risk and improve outcomes following an H-HBP is to establish quantitative and predictive models of injury that relate these factors to a diagnosis of a concussion.

Thus, the study team proposed a protocol that quantified H-HBP and its associated predictors. The team included clinical researchers:

  • Dr. Aravind Athiviraham, MD
  • Dr. Adam Bartch, PhD
  • Prasath Mageswaran, MS
  • Dr. Edward Benzel
  • Brian Perse, JD
  • Dr. Morgan Jones, MD
  • Dr. Mark Schickendantz, MD

Windpact recognizes just how crucial data-driven approaches are towards mitigating injury in sports. To address the various types of solutions cutting-edge research leads to, we look at protocols like those constructed by the Ohio State University thought leaders.

Here is how they tackled the problems of establishing risk outcomes in helmet impacts by pitch.

The reasons players hit on the head are removed from the field

Batters struck by a pitch are often taken out of the game due to safety reasons, such as running the risk of exacerbating the injury by continuing. Despite precautionary testing on helmets prior to being approved for use by athletes, concussions as a result of H-HBP can still be sustained.

As the factors that underlie H-HBP severity are being studied, the most devastating predictors alleged by the research team include:

  • Pitch velocity: traditional impact tests on helmets simulate 60mph impacts, however, pitches often exceed 60mph in real-world scenarios. This is especially true in high school, college, and professional levels of the sport.
  • Time player spent on the ground: indicative of injury severity and subjective reports of pain.
  • First responder time duration: how quickly first responders assess and triage the athlete may play a role in concussion outcomes in the short term.
  • Number of days missed: a reliable metric for establishing concussion effects on a longer time scale, post-game.

Quantitative models of these contributors are useful when discussing concussion diagnosis since they can provide necessary metrics on which healthcare providers and sports policymakers can base their recommendations. In terms of H-HBP injuries, they are especially crucial.

Injuries determinant on pitch velocity, for example, are sometimes difficult to estimate in the middle of a game and are potentially more useful in a post-hoc analysis. Velocities of pitches, despite having the forces of gravity acting on them at all times, can transfer quite a bit of kinetic energy to the helmeted skull, especially if the pitch exceeds 60 mph.

The equation for kinetic energy is given by:

K=12mv2

Where m is the mass of the object, and v is velocity. Say, for instance, a pitcher throws a fastball at the upper end of the average speed (according to MLB, over 1,000 pitches thrown between 2014-2018 exceeded 100MPH) and that pitch happens to result in an H-HBP. If we assume the mass of a baseball is the average reported by the MLB, which is 0.1451496 kg, then the equation looks like this:

K = ½ (0.1451496kg)(40.2336m/s) = 117.48 J

Where J represents joules, the universal unit of energy. While 117.48 is below the known threshold for breaking bone, the location of the injury, as well as the sudden stopping, may cause further damage.

The other components such as time spent on the ground, time to triage and the number of days missed are framing the question in the long term; the research team took a novel approach since these markers have not been used as metrics for relating concussion severity and helmet protection, previously.

So how did the Cleveland Clinic and OSU team analyze these factors and outcomes?

Using real-world scenarios to drive product design and testing

In order to gather data, the research team based their collection on video footage of MLB games, focusing on videos that showed H-HBP incidents. They limited their population to 18 MLB players, analyzing the incidents that resulted in concussion diagnosis. From each incident, the team gathered data like whether a physician-diagnosed them with a concussion, pitch velocity, time batter was on the ground, and the number of days the player missed following the incident.

The study team, being composed of a physician and three engineers experienced in the photogrammetric study of still photos, also plotted the location of the hit on the helmet against concussion diagnosis. This was the main qualitative factor of head injury the team gathered.

Findings revealed the following trends in the data:

  • Only the number of days missed showed a statistically significant correlation with concussion diagnosis. Days missed are helpful markers to assess player concussion severity, possibly because of the delayed nature of its effects.
  • Concussions were more likely to occur if the ball struck the occipital (posterior) or the temporal (lateral/side) side of the player’s helmet.
  • Despite pitch velocity showing little significance in predicting concussion diagnosis, its 25% risk value at higher velocities suggests a relatively conservative starting point for when physicians should be concerned a concussion has happened.

All other aspects held constant, these findings suggest that the most predictive variable tended to be the one with the longer time scale; the fact that pitch velocity was not the strongest outcome predictor is also interesting.

These types of research protocols hint that constant testing of variables, some of which may not even be that obvious, are critical towards educating both players and policymakers as we continue to make strides in protecting athletes at all levels.

General conclusions from these data are:

  • Pitch velocities exceeding the upper end of average fastballs (like 86MPH) should be known by first responders assessing the player.
  • Solely visual assessments of players are important, but contextual information like pitch velocity, location of the impact, and time on ground should be relayed to the triage team in order to produce better outcomes.
  • Helmets benefit from supplemental protection in areas that may not seem obvious to manufacturers, especially considering the possible damage resulting from side and posterior impacts.

Real-world scenarios were the key towards elucidating the unclear factors involved in concussion risk, and they are useful for their prediction power when you look at the data. Drawing inferences about how prospective contributors to a concussion increase risk is part of how predictive methods solve problems.

How innovative technology such as Windpacts Crash Cloud™ are addressing safety standards

Prediction metrics are only part of finding better solutions for safety equipment; action is needed. Using data drawn from real-world situations to create better products is one of the areas Windpact excels in.

Regulatory agencies and sports authorities outline standards that guide development for sports equipment; this guidance is informed and made better by continuous testing and adjustment.

Windpact is well-suited to contribute towards this continuous adjustment by way of our superior speed, flexibility, and dependence on validated data.

Given the relatively common incidence of H-HBPs (among other types of impacts) poses additional questions, namely:

  • Do designers prioritize extra padding at the risk of reducing comfort and wearability?
  • What costs are associated with redesigning and researching despite NOCSAE already providing standards to go off of?
  • How do engineers maintain the protective integrity of the entire helmet?

Windpact is poised to answer with, firstly, the Crash Cloud. The technology’s strength, among being a superior choice for engineers to improve impact absorption of their designs, is in its versatility. Crash Cloud is compact and meshes well with nearly any helmet or equipment shell. Its size and versatility ensure that comfort is preserved.

While we’re talking about helmets from baseball in this paper, when catchers helmets were put to the test with Crash Cloud, results revealed a significant improvement in impact performance across different scenarios. On average, there was a ~50% improvement in performance.

Graph showing improvement in impact performance in Baseball helmets using Crash Cloud.

These findings demonstrate not only the power of Crash Cloud’s ability to distribute and mitigate force, but also its versatility. With the ability to be placed virtually anywhere, the technology improves helmets at nearly any position on the head.

Beyond the Crash Cloud, finding real-world, realistic solutions for helmet safety is made possible through basing recommendations on the latest research. Our team’s reliance on using data to inform the design process means every client will know their prototype aligns with the current standards.

The MLB and sports institutional bodies in general remain champions of using science to inform equipment and rule recommendations. By propelling the field forward through basing research on real-world scenarios, physicians and engineers are ensuring that their recommendations and designs are optimized to perform on the field.

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