Preparing for Pre-Season Testing and Analysis

As the pre-season kicks off, evaluating player performance is crucial for setting the right training. In our experiences as sports scientists, leveraging statistical methods such as percentiles, z-scores, averages, and standard deviation has been invaluable. Here's some of our go-to methods to provide deeper insights into player performance.

1. Averages and Standard Deviation

Averages (mean) provide a central value around which player performance is evaluated. Standard deviation measures the variation or dispersion of the data points. Together, they give a snapshot of typical performance and the range within which most performances fall.

1. Example of how we use it: Calculating the average score of a wellbeing measure on a given day and the standard deviation to understand when they are off their normal score.

2. Percentiles

Percentiles rank players relative to their peers. For instance, a player in the 90th percentile for sprint speed is faster than 90% of their teammates.

1. Example of how we use it: Using percentiles to compare a player's sprint performance against historical data in the academy, to provide comparison to how good or bad the performance actually was.

3. Z-Scores

Z-scores indicate how many standard deviations a data point is from the mean. They help in identifying outliers and assessing the relative performance of players.

1. Example of how we use it: Calculating the z-score of a player's jump height to see how it compares to the team average, highlighting exceptionally strong or weak performers.

4. Moving Averages

Moving averages smooth out short-term fluctuations and highlight longer-term trends in performance data.

1. Example of how we use it: Tracking the moving average of a player's daily training load to ensure balanced and progressive conditioning.

6. Performance Index Scores

Combining various metrics into a single performance index score can simplify the comparison of overall player performance.

1. Example of how we use it: Creating a composite score from speed, agility, and endurance metrics to rank players comprehensively.

Insight Generation:

1. Identify players who may need personalised training programs on certain skills.

2. Recognise strengths, weaknesses, asymmetries, discrepancies, etc.

3. Adjust training loads to prevent overtraining and reduce injury risks.

Conclusion

Incorporating these statistical methods into pre-season analysis have allowed us to provide a comprehensive understanding of player performance. By leveraging statistical methods like those mentioned above coaches and sports scientists can make data-driven decisions to optimise training and prepare the team for a successful season.