ABSTRACT
Background:
An objective classification system for studying youth baseball players in the U.S.A. was published in 1996. Professional baseball is composed of greater than 25% international players a majority of whom come from five countries. Many youth baseball players are injured in early years play, both in the U.S.A. and internationally. There is no international classification system to study youth baseball pitching injuries, biomechanics, or maturation, but one is needed in order to compare and combine pitchers in multi‐center studies. Uniform domestic and international pre‐injury normative data is optimum. Ideally, data collection should be practical requiring inexpensive equipment and limited time demands.
Hypothesis:
The mathematical model, developed in 1996 on 853 boys and validated on 114 boys in the Mid‐Atlantic Region, U.S.A., is internationally applicable, allowing easy classification of youth baseball pitchers and levels throughout the world.
Methods:
Seven‐hundred‐twenty‐one international pitchers, ages 8‐14, threw five full‐speed pitches recorded with a calibrated radar gun and four maximum distance throws on a marked field. Demographics included age, height, weight, and years pitched. Collection sites included foreign national baseball clubs (Dominican Republic, Venezuela, Puerto Rico, Japan and the Philippines), the Mexican national youth tournament, and a multinational tournament (Brazil, Peru and Colombia). The mathematical model developed in 1996 was used to generate predicted distances for this sample for comparison with actual distances. In addition to the overall analysis, adequate sample sizes were available for comparing predicted and actual distances by country for four of the countries.
Results:
The correlation between predicted distance using the mathematical model and actual distance was 0.90. The mean of the international players was 1‐2 standard deviations above the USA mean for speed and one standard deviation above the mean for distance. There was no systematic over or under prediction indicating that both relative and absolute fit for the model was excellent.
Conclusions:
The mathematical model developed in 1996 on U.S.A. baseball players is robustly generalizable to international youth baseball pitchers.
Clinical Relevance:
Pre‐injury distance/speed data allows for classification of youth baseball player of multiple levels between the ages of 8‐14. International and regional comparisons are now possible for multi‐center studies in order to better define risk factors, compare studies, and combine data based upon pre‐injury maximum long toss data. Data collection requires only a field, a few balls, and a tape measure.
INTRODUCTION
The American Orthopaedic Society for Sports Medicine's – Sports Trauma and Overuse Prevention (STOP) sports injury program was developed to address the dramatic increase in traumatic and overuse injuries in youth sports.1,2 Youth baseball injuries, particularly in young pitchers, have been growing exponentially.3 Pitching speed, pitching volume, fatigue, and year‐round play have all been identified as risk factors for injury in youth baseball pitchers.2,4‐9 The ability to throw at high speed increases the forces about the shoulder and elbow.10,11,12 The growth plates of the shoulder and the growth plates and ligaments of the elbow are therefore at risk.4,10,13,14 Little League™ baseball is the best known of the youth baseball organizations and has addressed the rise in youth injuries with strict limits on pitch counts and mandatory rest days. What remains, however, is how to lower the risk to the high velocity youth pitchers.
Little League™ baseball expanded to a worldwide organization in 1954 and each year hosts a tournament with players from 8 regions in the United States and 8 international regions. Japan has 7 Little League™ World Series titles, Mexico 3, Venezuela 2, South Korea 2 and Chinese Taipei (Taiwan) 13.15 Major League Baseball (MLB) has scouts around the world. More than 25% of professional baseball players are from outside the 50 United States (according to the MLB operational definition of “international”). A majority of international players in MLB are from the Dominican Republic, Puerto Rico, Mexico, Venezuela and Japan. In 2013, there were more than 850 major league players on opening day rosters; 89 were from the Dominican Republic and 63 from Venezuela.16 MLB teams invest upwards of $76 million in the Dominican Republic (DR), of which $15 million is used in the operation of official MLB team baseball academies based in the DR. Most MLB teams own academies in the DR, where new talent can legally qualify for admission to an academy as early as age 14 and this largely unregulated industry has been called “baseball's puppy mill.”17
International youth pitchers may be at more risk than US players. The lay press has reported for years on the how the careers of young Latin American players often end in injury and failure.18 More recently the Japanese orthopaedic literature has begun to focus on youth baseball injuries.5,19‐21 Risk factors are similar to those identified for the USA. The authors developed a mathematical model that allowed the use of maximal throwing distance as a surrogate for pitch speed, obviating the need for a radar gun. The result was a data‐based and validated functional and practical classification system to identify at risk young baseball players requiring only baseballs, a tape measure, and a field, and the data were published in 1996.22 Using a table of normative data from the USA sample where age and means of maximum pitching speed and maximum throwing distance were presented with the standard deviations, a business size card was developed. (Figure 1) Each level, 1‐5 reflects the number of standard deviations above the USA mean. The authors have classified Levels 3‐5 as elite and at risk extrapolating from 80‐85 mph risk speed for injury in high school age players.2,6 Our perceptions that those who throw faster are more at risk led us to test whether our classification system was robust enough to use in identifying elite and at risk youth pitchers around the world.
Figure 1.
Front and back of business size card that summarizes normative data for throwing speeds and distance by age of the original US sample.
Identifying elite pitchers early and advising them and their families about pitching excessively is the most sensible way to protect them.23 If the U.S.A. classification system is applicable to the international youth baseball player, true epidemiologic comparisons can be made. Injury risk, biomechanics and pitching maturation studies can also use the classification system to define their populations. The purpose of this study therefore was to determine if the classification system developed on young USA baseball players is generalizable to an international sample of youth players from countries that regularly contribute players to the Little League™ World Series and MLB.
METHODS
Seven‐hundred‐twenty‐one international pitchers, ages 8‐14, (8 years, 32; 9 years, 46; 10 years, 73; 11 years, 148; 12 years, 171; 13 years, 128; 14 years, 123) were studied in conjunction with coaching clinics conducted by the staff of the American Baseball Foundation at local youth baseball clubs in the DR, Venezuela, Puerto Rico, Japan, and the Philippines, as well as a national tournament in Mexico and at a multinational tournament in Brazil (Brazil, Peru and Colombia) between May of 2007 and September of 2009. The proposal to use de‐identified data in this study was evaluated by The University of Delaware IRB and they determined this project is exempt from IRB review according to federal regulations.
Speed
Players threw five maximal‐effort throws from a pitcher's mound to a catcher at the mound‐to‐home plate distance that they normally used in their local competitions. Speed was recorded in miles per hour for each pitch using a recently calibrated radar gun (JUGS Sports, Tualatin, OR).
Distance
A field was laid out using a tape measure to 300 feet from the ball release line. A starting point was designated at the beginning of the tape. The athletes were required to use three‐step footwork (i.e. crow's hop) to ball release point. Boys threw four times at maximal effort. Ball release and landing sites were monitored by the testing staff. The length of the throw was then measured to the nearest foot. The longest distance was used for analysis.
Weight and Height
Weight in kilograms and height in centimeters were recorded for each of the boys and converted to pounds and inches to be tested in the mathematical model.
Data Analysis
Maximum speed and maximum distance for each boy were used in the analysis. Predicted maximum throwing distance was calculated for each boy from his maximum pitch speed using a mathematical model previously developed from a sample of 853 baseball players (8 years, 46; 9 years, 144; 10 years, 175; 11 years, 187; 12 years, 116; 13 years, 105; 14 years, 80) who were studied at summer baseball camps in the early 90s in the Mid‐Atlantic States and validated on 114 additional players.22 (Figure 2) These data were plotted against the actual maximal distance for each subject. The relation between predicted and actual distances was examined using a Pearson Product Moment Correlation. The average actual and predicted distances were also compared using a t‐test to detect systematic over‐ or underprediction. A best‐fit model for the international data was also created de novo using multiple regression analysis and compared to the predictive capability of the model developed on the USA sample.
Figure 2.
The mathematical model to predict maximum throwing distance from age and pitch speed.
RESULTS
Mean maximum pitch speed of the international sample was between one and two standard deviations above the mean of the U.S.A. sample. Maximum throwing distance was approximately one standard deviation above the mean of the U.S.A. sample (Table 1). There were no significant differences between the heights and weights of the international sample and the USA sample at any age.
Table 1.
Maximum Speed and Distance by Age. SD in parentheses.
| Age (years) | Maximum Distance (ft) | Maximum Speed (mph) | |
|---|---|---|---|
| 8 | USA | 95.5 (13.7) | 40.3 (3.5) |
| International | 114.5 (18.3) | 44.8 (4.8) | |
| 9 | USA | 104.5 (17.6) | 42.8 (3.8) |
| International | 117.9 (29.5) | 46.2 (6.1) | |
| 10 | USA | 123.2 (17.3) | 46.1 (3.8) |
| International | 136.6 (20.5) | 51.8 (6.0) | |
| 11 | USA | 134.5 (19.6) | 48.2 (4.2) |
| International | 149.1 (24.6) | 54.6 (5.6) | |
| 12 | USA | 141.3 (25.1) | 50.4 (4.8) |
| International | 172.9 (31.0) | 59.1 (6.8) | |
| 13 | USA | 163.6 (23.6) | 54.3 (4.9) |
| International | 202.4 (37.9) | 66.3 (8.4) | |
| 14 | USA | 195.6 (29.2) | 60.4 (5.7) |
| International | 220.0 (39.8) | 69.1 (8.1) |
The Pearson correlation was .90 (p<0.001; Figure 3). For the four countries for which sufficient sample size across age groups was present, individual correlational analyses (DR, Japan, Puerto Rico and Venezuela) were performed, and the r2 values ranged from .85 to .92 (p<0.001; Figure 4). Means and standard deviations for pitch speed and maximum throwing distance by age for these four countries are summarized in Table 2.
Figure 3.
Graph of predicted distance from the original model versus actual maximum throwing distance for the entire sample.
Figure 4.
Scatterplot of predicted distances (ft) from the original US model versus actual maximum throwing distances (ft). All correlations (Pearson Product Moment) are significant (p<0.001).
Table 2.
Maximum Speed and Distance by Age and Country.
| Age | <10 | 11‐12 | 13‐14 | |||
|---|---|---|---|---|---|---|
| Max Distance (ft) | Max Speed (mph) | Max Distance (ft) | Max Speed (mph) | Max Distance (ft) | Max Speed (mph) | |
| Dominican Republic | 123.9 (21.0) | 46.8 (3.9) | 156.6 (18.9) | 55.7 (4.8) | 198.6 (36.7) | 64.8 (7.1) |
| Japan | 130.4 (14.6) | 49.4 (3.8) | 196.4 (22.4) | 63.7 (4.9) | 232.0 (25.6) | 69.1 (5.6) |
| Puerto Rico | 119.0 (17.5) | 46.2 (4.3) | 163.4 (23.3) | 56.5 (5.5) | 211.2 (38.3) | 69.2 (7.9) |
| Venezuela | 135.3 (21.2) | 51.4 (5.0) | 182.0 (34.8) | 59.0 (5.9) | 224.3 (26.4) | 69.2 (4.4) |
As an additional way to establish the quality of the original prediction model, the authors developed a new model for the current data for comparison. The best‐fitting new model using age, height, country, maximum velocity, and the square of maximum velocity produced predicted distances that correlated with actual maximum distances at r = .93 (p < .001). This small increase in prediction compared to the original model is to be expected. That the increase is so small suggests the original model is quite robust.
The new model did reveal one interesting additional finding; there were significant country differences by country in maximum distance that remained after controlling for country differences in age, height, and velocity. In the international sample, the Japanese and Venezuelan pitchers threw further even when accounting for age and height. For example, compared to the reference country (Dominican Republic) and after controlling for differences in age, height, and velocity (Figure 5), Japan and Venezuela had distances that were on average 14.68 and 10.02 feet greater (p < .001). Puerto Rico, on the other hand, did not differ significantly from the DR (3.07 greater, p = .18). These are unique country‐based difference because the differences in age, height, and velocity had been statistically controlled. Their origin is uncertain (e.g., differences in weather, field conditions, training and development).
Figure 5.
In this figure, the maximum velocity ranges from ‐2 SD below the country average to +2 SD above the country average. The lines are displaced horizontally because the countries varied in their averages and SDs. The vertical separation of the lines reflects the country differences that remain after adjusting for differences in age and height.
The final analysis examined the difference between the predicted and actual distances for the sample. This analysis addresses the absolute, rather than relative, accuracy of the mathematical model. For those subjects who had an available data for actual and predicted distances, the average difference between actual and predicted was an over‐estimate of 5.18 feet (maximal actual throwing distance = 170.86 feet; average predicted distance = 176.03 feet). This difference is statistically significant, p<0.001, but represents only 3% of the actual average distance. Importantly, the difference between actual and predicted distances did not vary meaningfully by country or age group. For the four countries for which sufficient overall sample sizes and age group samples sizes were available to perform meaningful analyses (DR, Japan, Puerto Rico and Venezuela) the predicted distance overestimated actual distance by 6.93 feet for the Dominican Republic sample, by 4.05 feet for the Puerto Rican sample, and by .88 feet for the Venezuelan sample. The predicted distance underestimated the actual distance for the Japanese sample by 3.95 feet. For the age groups, predicted distances were overestimates for ages 8 (1.99 feet), 10 (4.48 feet), 11 (6.80 feet), 12 (3.55 feet), and 13 (4.58 feet) but underestimates for ages 9 (3.66 feet) and 14 (3.06 feet).
DISCUSSION
The mathematical model originally developed by the authors on a large sample of youth baseball players from the Middle Atlantic region of the USA was generalizable to an international sample of youth players. Both relative (regression) and absolute prediction was strong. The classification system has construct and face validity as well; the International sample as a whole had more baseball experience and had a higher proportion of pitchers in the samples. As expected, they pitched with higher speeds and threw further than the USA sample, but there was no systematic over or underprediction, absolute differences were small and the performance of each sample was related to the level of play of the subjects in each country. The classification can be used to identify at risk players or to allow for comparison of results by aptitude across studies.
The DR was the largest group (126) and the athletes were tested at various baseball clubs on the island. They were mostly pitchers with varying years of pitching experience. 28.5% (36/126) were two or more standard deviation above the mean of the USA sample and 8% (10/126) 3 or more standard deviations above. In the Dominican Republic, because of the popularity of baseball, the authors expected the sample to be skewed toward more talented pitchers since 89 of 856 major league players on opening day rosters in 2013 were from the DR. The Dominican Republic sample, however, was more like the USA sample with a large range of abilities from 2 standard deviations below to 3 standard deviations above the USA mean. The correlation was .86 and the predicted versus actual distance was excellent (less than 1 foot) across all levels from −2 to +3 standard deviations in all ages from 8‐14. The DR was therefore used as the reference country for comparison of country differences in throwing as a surrogate for the USA.
The Japanese athletes were predominantly pitchers from baseball clubs in the Tokyo region and ranged in age from 9‐14. They were on average 2 standard deviations above the mean for the USA athletes (range 1‐5). The correlation coefficient was .85, again reflecting the strength of the classification system and the predicted distance only under‐estimated by 3.95 feet, less than 2% of the actual average distance. As the Japanese players were all pitchers and experienced players, the authors expected them to throw faster and farther and they did. The Japanese research scientists are adding to youth baseball literature contributing their expertise and demonstrating that cultural differences do exist.24 In a 2010 study by Harada et al, 79/296 (27%) of players from youth baseball teams in Yamagata, Japan, threw every day. Pitchers in this study were 4.5 times more likely to have elbow injuries than non‐pitchers and 42/63 (67%) of pitchers had elbow injuries. Twenty‐six of these 12 year olds had four or more years of throwing experience with 15/26 (58%) having elbow injuries. In Japan only those leagues associated with Little League™ are forced to adhere to game pitch counts and mandatory rest days. Only a small minority of players in Japan, however, belong to Little League™. For most Japanese youth leagues, there are no practice or game restrictions. Unfortunately, neither speed nor maximum throwing distances were published for any of the cited studies of injury in Japanese youth baseball, therefore the level of thrower cannot be determined.
Venezuela provided 76 players for the study from baseball clubs around the country. While the majority had more than two years of pitching experience, all positions were represented. There were 57% Level 2 or greater and the 29% Level 3 or greater. There were 33% (25/76) at Level 1 or lower. While this group mirrored the U.S.A. Mid‐Atlantic sample of all positions, all ages and abilities, they were more experienced and better players and therefore the correlation of .91 was higher than expected. Venezuela had 63 major leaguers opening day 2013 (856 major leaguers). The Venezuelan Maracaibo team won the Little League™ World Series in 2000. While political considerations led of MLB to largely abandon Venezuela for investment in academies, new rules to limit steroid use and age fraud in the Dominican Republic have revitalized the youth baseball recruitment in Venezuela, where these rules (drug tests and age verification) have not yet been implemented.16,25 Venezuelan youth, like the Dominicans, are at great risk for injury as a consequence of the largely unregulated academy systems in their countries. No systematic study in the peer‐reviewed literature has examined risk and injuries in these countries. A simple classification system like the one presented here may allow for some risk assessment and testing of simple interventions in the Dominican Republic and Venezuela.
Puerto Rico provided 116 athletes that ranged from −1 to 5 standard deviations from the USA mean. Their correlation coefficient was the highest at .92, very similar to the Venezuelan's .91. Twenty‐seven percent were Level 2 and only 12% Level 3 to 5. The mathematical model overestimated the Puerto Rican sample by just 6.4 feet. There were 13 Puerto Rican players on Major League rosters on opening day 2013. Puerto Rico was also one of the eight international teams in the 2010 Little League™ World Series. The Mexican data were collected at their 2007 national tournament. All subjects were pitchers. 78 pitchers ages 10‐14 threw for speed but only 32 threw for maximum distance, because they were in competition at the time and thus the sample range and size was too small to be analyzed in our by country analysis. Nevertheless, discussion of the sample contributes to the face validity of the international classification. This was the only country where representative pitchers selected for their respective regional teams (in a country that in 1997 won the Little League™ World Series and in 2010 were semifinalists) comprised our sample. The authors expected, therefore, that they would pitch faster and throw further than our USA sample and they did. Ninety percent of the Mexican pitchers in the current sample were Level 2 or greater with 57% Level 3 or greater. Only 10% of the pitchers sampled from their national youth tournament were within one standard deviation of the U.S.A. youth player mean.
The international sample was very diverse. The between country differences in maximal throwing distance underscore this diversity and breadth. Beyond the contribution of age and height, there were significant country differences. Once the fact that there are country differences in age and height were controlled, there were still differences in the maximum throwing distances present in boys from different countries. The players from Japan and Venezuela were the best players in the subsample; the Japanese players were mostly pitchers. The country difference in distance may reflect ability, more practice, or the type and quality of instruction that is given to children in different countries, but at this time, the actual reason for the differences remains unknown. The original model had an excellent ability to predict the distances. The difference between the original model and the best fit model of r=.03 is trivial compared to the importance of being able to use the same classification around the baseball playing world.
The mathematical model to predict distance from age and speed is a complicated multiple regression equation using polynomial fittings and includes interactions (Figure 2). Its construction was necessary when standard physical equations systematically overpredicted distance by approximately 80% because youth baseball players are not cannons. If they had an excellent 3‐step crow hop, were able to transfer all the power that they generated from the lower extremities 100% through their core, and released the ball at their maximum height on a 45‐degree trajectory, the predicted and actual distances from the standard physical equations would be much closer. The mathematical model is based on data from all players, not solely pitchers, from the Mid‐Atlantic region of U.S.A. It assumes that any new sample would have similar age, speed, and distance characteristics, an assumption that was verified in our international sample. In the 1996 study, height and weight data did not add to the accuracy of the predicted distance. This can be explained by age and speed capturing most differences. Older athletes who throw with the same speed as a younger pitcher will throw nearly 8 feet further, 9 year olds versus 11 year olds, perhaps as a result of improvement in biomechanics and coordination.26
In a world interested in reducing youth baseball injuries, education and protection are two key concepts in injury prevention.22,27 Young athletes who can throw further and faster than age norms have increased elbow forces and moments and therefore place their growth plates and ligaments at greater risk of injury.23,28 Pitchers with better mechanics threw faster but have increased medial elbow stresses.10 Risk factors for injury have been identified. While those who have complained of shoulder pain versus elbow pain may have some differences, pitching with fatigue often associated with increased pitch counts, both in a single game and cumulative for the year, and poor self perceived performance parameters are common to both.6 The Japanese have also identified risk factors for elbow injuries among youth baseball players. In their study of 294 baseball players, ages 9‐12, 60 had elbow injuries.5 While both Lynam et al7,8 and Harada et al5 studied youth baseball players who participated in organized teams, comparison of these studies without a classification system is difficult. When studying pitching biomechanics in youth baseball pitchers and looking at pitching faults as risk for injury, the lack of speed or distance data to classify the talent level limits the ability to aggregate the results of studies and assess their generalizability. Either pitch speed or distance can be used and allow us to compare and contrast across studies of risk and/or biomechanics.29
How fast or how far is elite? Few would argue that the pitchers in the Little League™ World Series are elite, but where does elite begin? Of the 20 pitchers from the countries studied (USA, Japan, Puerto Rico and Mexico) who appeared in the televised final games of the 2010 Little League™ World Series in Williamsport, Pennsylvania, all were Level 4 or 5 (mostly 12 year olds with pitch speeds of 70 mph or above). In developing our classification of elite and at risk players, the authors extrapolated from the literature that suggested higher risk of injury in high school age players who have pitch speeds in above 80 mph, which in 14 year olds is Level 3. Therefore the authors propose that Level 3 be the threshold for classifying a pitcher as elite and potentially at risk. A 12 y. o. who is Level 3 would throw 65 mph or 216 feet, well below the 2010 Little League™ World Series pitchers’ mean pitch speed of 72.5 mph. The categorization of the 2010 Little League™ World Series pitchers as at risk has face validity. Only three pitchers have pitched in both the Little League™ World Series and in the major leagues. It is reasonable to assume that injury may have played some role in this.
In some youth baseball pitching studies with repor‐ted velocity, their athletes, described as all‐stars or members of travel teams, are near the mean for our USA or Level 1.29‐31 However, all‐star and travel team pitchers are not necessarily elite. Nakamizo et al in their study of glenohumeral anterior rotation in youth pitchers did record pitch speed in the 25 10‐12 year old pitchers in their study.30 Sabick et al studied 14 youth pitchers with a mean age of 12 and speed of 21.6 m/s (48.2 mph), Level 0 or at the mean of the 1996 USA sample.32 Based on the authors’ developed classification system, these pitchers are at or slower than 1 standard deviation above the USA mean (23.7 m/s; 53 mph). So, these studies did not examine elite pitchers, nor did they study at risk pitchers, according to the authors of the current study. Fleisig et al studied a range of ages (10‐15) and abilities (Level 0‐4) with a mean speed of 28 m/s (63 mph).28,33 The classification system developed by the authors would allow for comparison across all of these studies and ultimately aggregate analysis for methods such as meta‐analysis that can lead to more powerful prediction of injury risk.
Maximum long‐toss can be used as a surrogate for speed. Long toss distance can be recorded each year following preseason and before the first game as a team or league competition single event, or as part of a performance battery of running, hitting and throwing and can be used for classification as well as a long toss target in rehabilitation, training and conditioning programs. While the radar gun may be helpful in comparing fast ball speed with the speeds of other pitches, to compare the pitchers average speed at different times in the game and different times in the year from preseason to playoffs and may be used in tryouts for All‐Star teams, the radar gun's place in youth baseball is difficult to define. Little League™ baseball frowns upon the use of a radar gun, as do most other youth baseball organizations because of the potential for abuse of the quest for speed at any cost Here we have demonstrated that a radar gun is not necessary to classify players as elite or at risk.
Limitations of the study include unequal sample sizes and ranges of ages across the countries studied. Collecting data internationally is culturally challenging. A planned trip to Cuba to study youth baseball players already has been postponed three times due to bureaucratic issues. The problems with the Mexican pitchers not throwing long toss during the tournament was detailed above. Nevertheless, the result demonstrates a robust and generalizable model.
CONCLUSIONS
The authors tested a mathematical model published in 1996 on a sample of US baseball players on a varied sample of international youth players. The strong correlation between actual and predicted distance demonstrates that the model is robust and generalizes to the entire international sample. These data suggest that the classification system is valid and can be used prospectively and retrospectively to categorize pitchers in order to allow for studies of youth baseball injury epidemiologically and to allow for classification for biomechanical or interventional studies in youth baseball internationally. The implementation of this system can ultimately allow for important questions about risk and performance to be answered across in the U.S.A. and around the world.
Acknowledgements:
The staff of the American Baseball Foundation, graduate student Kevin McGinnis, Coach Bill Thurston and research assistant Ben Joseph.
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