Optimization of coaching using six sigma.

Author:Colibaba-Evulet, Dumitru


"Six sigma", as applied by large corporations like Motorola, Honeywell and other, tries to create a closed-loop approach to minimization or elimination of all manufacturing defects generated by existing industrial processes (Pande et al., 2000). In addition, the method can be equally applied to new processes through implementation in the design phase, thus avoiding bad decisions and errors committed in the past. Similarly, one could conceive that six sigma can be applied when preparing and coaching an athlete or a team of athletes before and during their competition season. Historical data gathered during the preceding competitions and seasons become extremely important-the improvement of an athlete becoming a statistical problem to be solved by the coaching team, unbiased by the traditional approaches of physical training used in the past.

"Six sigma" is a methodology relying strongly on the preceding years' performance (Breyfogle, 2003). The data (performance, records and failures) recorded during the preseason and the competitions are analyzed statistically. The analysis becomes unbiased by being transformed from a traditional coaching method to a statistical analysis of the failures, the importance of the factors involved and the down selection to the few vital elements that effect the athlete's or team's performance. The essential actions to achieve the strategic goals of an athlete can be applied with precise, surgical accuracy using the statistical approach of this method.


The materials used in this study are data collected from the performance records of a basketball team with sufficient statistical significance. The data was collected under the supervision of one of the authors while he was coaching the team. The statistical analyses performed on this data were used to draw conclusions of the development plan identified for the team and for the athletes (individual and team level). The statistical tools involved in this study were Minitab (Ryan et al., 2005), analytical approaches (Breyfogle, 2003) and computational tools (Levine et al., 2000; StatSoft, Inc., 2000). Because introduction of the six sigma methodology to sports needs to start with the building blocks for analyses, an a short description is listed here but further reading is advised in (Pande et al., 2000, Breyfogle, 2003).


The method has been used extensively in the last decades to improve the performance of final products by improving the quality of the study done by a business and understanding of the process variables that affect the final product (Pande et al., 2000). In the end, the method inherently improves upon the productivity and eliminates the source of the defect generating process.

The assumption is that similarly to the business, the method can be equally applied to athletic activities with immediate benefits of better performance and reduction of performance variation (i.e., improving accuracy and improving repeatability by reducing variance) if identification and elimination of causes of failures or "defects".

The goal of this study is to introduce, explain and extrapolate the six sigma methodology to the high performance sports research area by working on two examples.

The approach and application of six sigma (example 1: the 100 m flat runner): A 100 m flat runner may have mixed results during the season. Let us assume that the average time our athlete has per season is 10 sec, but that he targets to achieve or exceed constantly the Olympic record (Fig. 1). The coach has really 2 options:

* To prepare the athlete as before, based on his experience as a coach and the lesson learned in the past (the traditional approach)

* To use the six sigma method and determine, based on data collected in the past, the statistically proven right strategy and actions to be taken for achieving the goal by reducing the variance: This option can provide a consistent metric, set realistic goals and motivate the athlete

Figure 1 shows that the athlete needs to be able to "move" his average performance towards the goal (Olympic record time) while reducing his standard deviation (sigma) so that the size of the distribution (Fig. 2) narrows and results become more accurate and less spread. This is achieved only if "defective" o r unacceptable...

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