By Vern Gambetta
Vern Gambetta, MA, is the President of Gambetta Sports Training Systems in Sarasota, Fla., and the former Director of Conditioning for the Chicago White Sox. He is a frequent contributor to Training & Conditioning and can be reached at www.gambetta.com.
Training & Conditioning, 13.4, May/June 2003, http://www.momentummedia.com/articles/tc/tc1304/getgame.htm
Function and specificity are familiar concepts in strength training and rehabilitation. Few strength and conditioning coaches debate the benefit of preparing athletes for the specific requirements of their sport.
But how do we figure out what sport-specific movements happen most in a game, or for a particular player? In reality, much of our programs are based on assumptions. And sometimes those assumptions are not totally correct.
For example, we assume that basketball players need to train for maximal-height jumps. But have you ever examined if all the players on the floor actually perform more than a couple of maximal-height jumps per game? Another example: do you know how much time a soccer player spends running forwards vs. backwards vs. sideways during a game?
In truth, most of us do not know the answers to the above questions. But I have come to realize that finding out the answers is critical for making our strength and conditioning programs more sport-specific and functional. If we truly want to elevate the specificity of our training programs, we need to spend time doing objective sport analysis.
Objective sport analysis requires a coach to examine the competitions of the athlete he or she works with in a new and unbiased way. Not just by relying on our powers of observation and memory, but by closely examining exactly what our athletes do during a competition.
How do we do this? Perhaps the most efficient tool is video analysis. Many sophisticated analysis programs are now available that provide strength coaches with previously unfathomable detail of athletes’ actions and movements. The problem is that they can be quite expensive.
However, any coach can sufficiently analyze an athlete with just two tools: a video camera (preferably digital) and a stopwatch. If analyzed correctly, the information that can be gleaned from a basic game tape will significantly upgrade your training programs. After all, if you’re training athletes for the game, what better source of information is there than the game itself?
One of the benefits of using game analysis is its ability to help you break down barriers, debunk myths, and think outside the box. Many myths about sport performance and rehabilitation are passed on as facts from generation to generation. These myths are based on assumptions, experiences from having played the game and tradition rather than on factual data. Game analysis can help you answer a question you should ask yourself about all your programs from time to time: Am I doing this because this is the way it has always been done, or does this really reflect what is happening in the game?
Although it may go against your instincts, the first thing to remember in analyzing ballgames is to NOT watch the ball. Game analysis, as you will learn for yourself, has shown that any player has very little contact with the ball in sports like soccer, lacrosse, and even basketball. So, what they do without the ball and away from the ball is as important, possibly even more important, than what they do with the ball. This means you should closely watch what the player does without the ball—how they move, how they end up in possession of the ball.
Also beware of “highlight-play syndrome.” The highlights we see on television are just that, highlights. They happen infrequently, sometimes once a season, sometimes once a career. It can be easy to fall into the trap of letting highlights influence the training programs, so it’s important to scratch them from your thoughts.
Instead, base your program on what the game analysis shows happens consistently in the game. For example, in an analysis of professional basketball, I found that the number of maximal jumps—which I define as a hand above the rim—was small. In one game I analyzed, the NBA’s second leading rebounder only made three maximal jumps. That runs counter to what we’re led to believe by the highlight videos.
Game analysis will also help clear up misconceptions that arise when information is taken out of context. A classic example is the misinterpretation of the distances players run during a game. A soccer player will “run” in excess of six miles during a normal game. A basketball player will “run” up to two and one half miles in the course of a game. The thinking, then, is that we must do distance running to train them.
But that logic is actually wrong. By looking more closely at the game, you can break down a player’s movements into sprints, runs, jogs, and shuffles and get a much more accurate idea of how to condition for the sport.
Also beware of applying the trickle-down effect to sports analysis. There is a natural tendency to look at the way sport is performed at the highest level and then adapt it to lower developmental levels. But the level of play—as well as the length of quarters, time clock, and rules—can significantly change a game, and in turn, affect the conditioning emphasis. There is a big difference between basketball played under international rules and NBA rules. There is a big difference between football played at the college level and the high school level.
Sport analysis falls into three broad categories: notational analysis, race analysis, and biomechanical analysis. Notational analysis quantifies key performance elements and analyzes movement paths and patterns. Race analysis is the quantification and breakdown of the phases of a linear event, such as track and swimming. Biomechanical analysis consists of detailed quantitative measurements that use very expensive computer software. Therefore, we will focus on notational and race analysis.
Notational analysis is accurate information that is gathered systematically. It usually involves a system that allows the observer to record: location of the action, the performer(s) involved in the action, the action, and the time the action took place.
Here is some information you may want to compile in your notational analysis:
• Distance run in various intensity zones: This will tell how the player distributes his or her efforts.
• Positioning and pattern of movement: This helps determine the types and amounts of an athlete’s movements during competition.
• Situational analysis: Looks at movement patterns during certain pre-set situations, such as an out-of-bounds play in basketball, corner kick in soccer, and first versus second serve in tennis.
• Time of action versus time of play: The greater the time of actual action, the more intense the play. This has implications for the conditioning portions of our programs.
• Fatigue index: Distance covered in the first half of a game versus the second half. This is a good indicator of conditioning when tactical considerations can be factored out (such as a basketball team switching to a full-court press in the second half).
• Frequency of intense effort: How often does the player have to go “all out?”
Race analysis is more straightforward since there are no defenders for players to overcome and fewer strategic adjustments. Here are some specific areas you may want to focus on in your race analysis:
• Reaction time: This tells the coach how well the athlete reacts to the starting stimulus. If this is deficient, drills can be designed to improve it.
• Split times or segment times: This provides information about race distribution. Does the athlete need to work on generating more power early in a race or sustaining power for a longer period?
• Instantaneous velocity: This shows the actual velocity at that particular time as measured by a laser, and it can give further information on race distribution.
• Acceleration: Look at rate of acceleration and how long it takes to get to top speed.
• Maximum velocity: How fast can the athlete go at top speed?
• Number of strides in track or strokes in swimming: Top performers all fall within certain ranges. If there is a deficiency, this can be addressed in training.
• Hurdle rhythmic units: This is a comparison of the times from hurdle to hurdle, and can help to identify any deficiency.
The actual means of gathering the data varies. The key is to devise a consistent recording method that gives you the information you need. One method I like to use is isolating a video camera on a position or player for the duration of the match or game.
Time/motion analysis is also very helpful. All this entails is a stopwatch and a well-constructed chart to mark down what a player does and for how long. This can assess work-to-rest ratio and chart movement categories during the course of a game.
The next step is to interpret the data based on what you have decided are the key performance factors. As an example, let’s look at some data from basketball. The following is a composite of game analysis from the Australian National Basketball League (published in The Journal of Sports Sciences, #13, 1995), which plays the game in four 12-minute quarters, with a 15-minute halftime and two minutes between quarters:
• Walking or Standing: 4 min.
• Jogging forwards or backwards: 4 min.
• Running forwards or backwards: 4 min.
• Sprinting: 3 min.
• Shuffling at low to medium intensity: 9 min.
• Shuffling at high intensity: 2 min.
• Jumping: 41 sec.
• While the ball was in play there was a change in movement category every two seconds resulting in 1,000 different movements during a game.
• Strenuous exertion: 28 percent of court time.
• Intense activity: 13 to 14 sec. at a time.
• High intensity efforts per game: 105.
• An intense effort occurred every 21 seconds.
• Side to side movements: 31 percent of court time, two-thirds of this was intense.
• Individual shuffle movements: 1 to 4 sec. in duration.
• Sprints: 1 to 5 sec. in duration.
Here is some data on NBA basketball (from the August, 1994, Journal of Applied Biomechanics, Vol. 10, No. 3) breaking down jumping patterns:
• 30 percent of all jumps were “low,” which consisted of an unchallenged shot or rebound.
• 45 percent of jumps were “medium,” which included most rebounds, defending jump shots, and jump shots.
• 25 percent of jumps were “maximal or near maximal” as would occur when taking a high-intensity jump shot or dunk.
The average number of jumps per game was 70, distributed as follows:
• Guards: 55
• Centers: 83
• Forwards: 72
Applying the Analysis
The final step is the application of the data to the design and implementation of training. The idea is to use your findings to adjust volumes and intensities to better reflect game demands during training.
Going forward with our basketball examples, how can we take the data that was gathered in games and design a more effective training program? First, we see that the game is a series of intermittent high-intensity multi-directional movements. That will direct us immediately to high intensity activities that involve varied movement patterns with variable work-to-rest ratios. The work will vary depending on the time of the training year and the fitness of the player.
Another implication that we can derive from this analysis of basketball is that power is at a premium. Thus, jumps of varied intensities and quick movements should be emphasized.
One question I’ve been asked about game analysis is this: should all training be limited to the specific movements of the game? The answer is no. There is a definite role for general preparation activities of strength and fitness, especially among less-experienced athletes.
However, what we do need to beware of is building an endurance base for our athletes through long, slow, steady aerobic work. Endurance for basketball, or for that matter other transition game sports, should be trained with general and game movement imitation drills in an intermittent pattern. A well-planned program can develop the endurance base necessary to enhance the aerobic fitness component without compromising explosive power.
If you have the time to take game analysis to another level, the next step is making it player specific. The methods are the same but this entails focusing your analysis and interpretations on just one player’s movements.
For example, you may find that your basketball center does a lot less shuffling movements than the other position players do, but does a lot more pivoting. You may want to tweak his strength and conditioning program with that in mind. This kind of individual analysis can be very motivational for the athlete because the program can be personalized to each individual’s style and pattern of play.
Another example might entail analyzing your middle linebacker in football. After recording his movements during several games, you might find that the number of times he makes a first hit on a ball carrier is far fewer than the number of times he is the second or third player to assist on a tackle. This may mean his agility—getting to the play—is more important than having the power to make a solo tackle, and his training can be adjusted accordingly.
Whether you have the time and resources to do just one game analysis for each team you work with each season or can individualize the analysis for each athlete, the process will bring more objectivity to training, and help eliminate bias. Using game analysis concepts will enable you to significantly improve your training program by being more exact and specific in the prescription of work.
Testing the Tests
Analyzing sports objectively may also help us change some long-standing tests that are truly misleading. For example, the Cooper test is one that is commonly used but not very useful. It entails running one and one half miles in a target time. It is a continuous run test, and athletes are successful if they can run it at consistent steady pace. The problem is, few sports are played at a consistent, steady pace. (Think of a soccer player’s movements during a game.) This leads to the players training for the test, rather training for the game.
Another example is the continued infatuation with the 60-yard dash as a speed test for baseball. The distance is based on the fact that the distance between bases is 30 yards and the sum of the distance from first to third base is 60 yards. This test is timed as a straight sprint.
However, to run from first to third, even when the player is very efficient at the turn at second base, is at least 64 yards. In addition, this is not a straight-ahead run. The athlete’s ability to run that distance in a semi-circle pattern is a huge factor in his success. In most game situations, the longest distance a baseball player runs in a straight line is 30 yards.
It would be better to use a 30-yard sprint and a timed sprint on the base path from first to third as performance tests. That will provide a better speed profile in relation to the demands of the game.
The 40-yard test in football is another example of a little bit of factual information being distorted. Except for receivers and some special teams players, the only time most players run 40 yards in a straight line is when they run on and off the field. Yet an inordinate emphasis is still placed on this test.
A thorough game analysis can help you develop tests that accurately assess what your athletes will face in game conditions. Just as important, it will also supply you with concrete evidence to show sport coaches an alternative to the tests they grew up with.