Baseball research paper

Crisco, R. Greenwald, D.

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Russell, and Lloyd V. Smith, Sports Engineering 13, Smith, and Warren L.

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Faber, Sports Technology 4, A new technique is described to normalize the performance of a bat to the coefficient of restitution and stiffness of the baseball or softball used to measure the performance. The technique is developed theoretically and tested experimentally. It is shown to be vastly superior to a different normalization technique that has been popolarly used. Nathan, The Physics Teacher 52, These techniques shoold be suitable for classroom instruction in introductory physics courses.

Nathan, The Physics Teacher, 55, A tool is presented for calculating trajectories of baseballs and used to analyze a home run hits by Alex Rodriquez in Kensrud, Alan M.

Nathan, and Lloyd V. Smith, American Journal of Physics, 85, Oblique impacts of a swinging bat with a stationary ball are performed and the resulting velocity and spin of the batted ball are measured. The results are analyzed in the context of a ball-bat collision model and the parameters of that model are determined.

Back to Top Subic, P. Trivailo, and F. This presents the results of a field study sponsored by the Amateur Softball Association. The data show that the swing speed of a typical player is inversely related to the bat moment of inertia about the handle with a fixed bat weight and nearly independent of the weight of the bat at fixed moment of inertia. Nathan, Daniel A. Smith, The Engineering of Sport V, eds. Hubbard, R. Mehta, and J.

This paper presents a simple physical picture of the "trampoline effect" in hollow bats and demonstrates how the effect leads to a larger coefficient of restitution. A special section presents new data showing there is no measureable trampoline effect with a corked bat. Nathan and L. Nathan, Procedia Engineering 34, The data reveal that, contrary to popolar belief, knuckleball trajectories are as smooth as those from normal pitches.

However, the data also show that the deflection of a knuckleball from a straight-line trajectory is essentially random in both magnitude and direction. Experiments are conducted to investigate the spin of a baseball undergoing an oblique collision with a bat.

A baseball was fired horizontally at speeds up to mph onto a 3"-diameter cylinder of wood that was rigidly attached to a wall. In one experiment, a two-wheel pitching machine was used in which the backspin or topspin of the incident ball coold be adjusted.

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In another experiment, an air cannon was used to project the ball with no spin. In both experiments, markers on the ball were tracked with high-speed video to determine the velocity and spin vectors, before and after the scattering. Our primary results are as follows: 1 For a given angle of incidence, the scattered spin is nearly independent of the incident spin; 2 The spin of the scattered baseball is considerably larger than expected for a model whereby the ball rolls before leaving the surface.

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Implications for the spin of batted baseballs are explored. The trajectory of a baseball moving through the air is very different from the one we teach in our introductory classes, in which the only force is that due to gravity. In reality, the aerodynamic drag force which retards the motion and the Magnus force on a spinning baseball which causes the ball to curve play very important roles that are crucial to many of the subtleties of the game. Despite their importance, our knowledge of how these forces affect the flight of the baseball has been qualitative at best.

Recently, however, new tools have been developed for measuring accurate baseball trajectories during an actual game. In this article, I will discuss these new tools and give some examples of what they are teaching us about the game of baseball. Jeffrey R. Kensrud, Lloyd V. Nathan, Baseball Prospectus, March 31, Green Street Urbana, IL E-mail: a-nathan illinois.

Spring Baseball Research Journal | Society for American Baseball Research

The Physics of Baseball Alan M. A more common approach for inferring the sensorimotor abilities important for baseball performance involves studying the difference between professionals, amateurs, and non-athletes. Some studies have found that expert baseball players possess superior visual acuity 10 , enhanced contrast sensitivity 11 , better peripheral vision 12 , and better visual tracking abilities 13 than non-athlete controls. While these studies indicate that superior batters possess superior visual system physiology, the preponderance of evidence in the literature concludes that, in the absence of hardware differences, expert performance is subserved by superior abilities to process and act upon visual information.

For example, past research found that expert athletes demonstrated more adept anticipation, pattern recognition, and visual search skills than non-experts 14 , 15 , Nevertheless, given the challenges inherent in doing research with high-level athlete populations, the contribution of hardware and software to expert performance remains an open question. Between and , the Nike Sensory Stations were developed and utilized as a tool to quantitatively evaluate athlete visual and motor skills. Participants filled out a registry of information about themselves and completed a battery of nine visual-motor tasks administered under standardized conditions with video instructions and conducted by trained and certified administrators.

In addition, participants reported hand and foot preference and completed the Miles test to assess eye-dominance. Data from these assessments were maintained on a central database and used to provide sport-specific normative information to individuals about their relative abilities and to monitor learning when coupled with sensorimotor training interventions.

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Past research with the Sensory Stations has demonstrated that the battery of tests is reliable 17 , 18 and cross-validated 19 , with some tasks demonstrating linear improvements with practice over multiple sessions Improved performance on this battery has been seen following sports vision training interventions 21 and has been linked to baseball batting expertise, with professional baseball hitters showing better performance on measure of visual sensitivity relative to pitchers Furthermore, reduced performance on these tasks has been associated with an increased likelihood of sustaining head impacts during practices and games for American collegiate football players Collectively, past research reviewed by 25 suggests that this battery may serve as a useful tool for understanding human performance, warranting further investigation into the sensorimotor characteristics of athletes and their relation to performance outcomes.

In the current study, Sensory Station assessments from professional baseball players collected in and were compared to game statistics to evaluate the relationship between sensorimotor skills and baseball production. For each player, game statistics from the season after testing were acquired along with information about their league s of participation.

As such, these data conform to U. The Contrast Sensitivity task measures the minimum resolvable difference in contrast at a distance. The Depth Perception task measures how quickly and accurately participants are able to detect differences in depth at a distance using liquid crystal glasses. The Target Capture task measures the speed at which participants can shift attention and recognize peripheral targets. The Eye Hand Coordination task measures the speed at which participants can make visually-guided hand responses to rapidly changing targets.

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The Reaction Time task measures how quickly participants react and respond to a simple visual stimulus. In addition, we use fielder-independent pitching FIP to measure the performance of pitchers. Below are brief descriptions of each of these statistics and our motivation for using them as response variables in our models. On-base percentage is defined as. On-base percentage is a simple and widely used metric for player evaluation, since frequently reaching base gives the offense more opportunities to score runs. Players with high on-base percentages consistently make effective contact with the ball and draw walks.

As such, on-base percentage offers a robust measure of player productivity and a useful statistic by which to evaluate the relationship between sensorimotor abilities and on-field performance. Walk rate is defined as. Players who routinely draw walks generally differentiate well between balls and strikes, forcing the pitcher to throw pitches that are easier to hit. Strikeout rate is defined as. Strikeouts are an unequivocally negative outcome for the offense and should be avoided in an at-bat.

Although some successful players have high strikeout rates, a high strikeout rate indicates that a batter struggles recognizing pitches or making contact with the ball. A player who strikes out frequently and walks rarely typically has a dim future in baseball. Slugging percentage is defined as.

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Slugging percentage makes use of the fact that not all hits are equally valuable. Although it is an imperfect metric e. FIP is defined in terms of only variables that cannot be affected by the ability of the defense behind the pitcher. It is generally more stable than ERA, since it is a measurement that cancels out the effects of defense and luck. Sensorimotor abilities may be related to pitcher performance, and FIP represents one of the best metrics for quantifying pitcher performance in a game setting.

Although data was obtained for professional baseball players batters, pitchers , we only examine data for the players with more than 30 at-bats or more than 30 innings pitched to mitigate the statistical noise associated with low sample sizes.

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This yields a final analyzed data set of players batters, pitchers. In general, most of the players in the sample are young prospects between 20—25 years old. However, we do have older players in the sample who disproportionately play in the Major Leagues. Not all professional leagues are equal. The level of competition in Major League baseball significantly outclasses that of AA baseball, for example. Players in our sample played in leagues ranging from Rookie League to Major League Baseball, which makes player comparison more challenging.

Note that some players play in more than one league. We fit separate models for the five response variables. The models use a common set of predictors. For any model, all parameters are estimated using only the data for that model. Without loss of generality, we present the model for OBP. Let OB ij denote the number of times that player i reached base in league j out of N ij opportunities between and Putting it together, we have the Bayesian multilevel model Accounting for league differences in this way enables us to compare, for example, a 0.