Measurement. Introduction to measurement

In this entry, the use of instrumentation and the interpretation of the data obtained in relation to agronomic, biomechanical and playability parameters of natural turf sports surfaces will be discussed.

For the correct management and continuous improvement of these surfaces, it is essential to know and measure the various parameters that characterise them, as it is not possible to improve what is not measured. In addition, specific tools are required for the analysis of each of the parameters to be studied.

The choice of suitable instrumentation is essential to obtain accurate and reliable information on the parameters being measured. The quality of measuring instruments is assessed through several essential attributes:

• Sensitivity: This attribute, also known as resolution, indicates the smallest variation the instrument can detect. It can occur in digital or analogue readings. A high sensitivity allows subtle changes in the measured parameter to be detected.
• Repeatability: Repeatability refers to the ability of the instrument to produce consistent and accurate readings when repeated measurements are made under the same conditions. Repeatability is crucial to ensure that the data obtained are reliable and can be used with confidence.
• Accuracy: This attribute measures the ability of the instrument to provide the true value of the measurement. Accuracy is the final determinant of the quality of an instrument, as it ensures that measurements accurately reflect the reality of the parameter being evaluated.

Data Readout

Once the appropriate tool has been selected and the necessary measurements have been made, a set of data is obtained and properly analysed. Generally, the statistical variables under study will be quantitative, such as the results of a football field compaction test. It is crucial to characterise the sample using the most important statistics, which include:

Media:

The average is obtained by dividing the sum of a set of data by the number of data. For example, the average hardness of a football pitch is calculated by adding all hardness measurements and dividing by the total number of measurements.

Median:

In a data set, the median is the value that lies at the midpoint when ordering the data from smallest to largest. This means that half of the values are smaller than the median and half are larger.

Standard deviation:

This measure quantifies the variation or dispersion of a set of numerical data. A low standard deviation indicates that most of the data are clustered close to the mean, while a high standard deviation suggests that the data are more dispersed. In the context of field quality, low standard deviations are desirable as they indicate a uniform and consistent surface.

Linear Regression:

Linear regression is used to identify and quantify the relationship between two variables. By plotting measurements on an X-Y graph, linear regression allows the behaviour of one variable to be predicted based on the other. For example, it can be used to predict how changes in the soil compaction will affect the playability of the field.

Linear Regression

When performing a linear regression, the R² value is obtained, which varies from 0 to 1. A value of 0 indicates no relationship, while a value of 1 indicates a perfect predictive relationship. An acceptable relationship is considered to be 0.8 and above.

There are clear examples of such relationships, such as the high inverse correlations between biomechanical parameters such as shock absorption and energy restitution, where the higher the one, the lower the other.

Climatological Considerations

In tournament measurements, it is essential to measure weather parameters such as wind speed, temperature and relative humidity. These factors can affect the accuracy of the measurements. For example, high wind speed values can prevent a correct measurement, which makes it necessary to record these conditions or to eliminate disturbing circumstances, such as the use of tunnels for green speed measurement.

Heat Maps

A heat map is a data visualisation technique that represents different magnitudes using colours or tones over a specific area, facilitating the interpretation of the data. Geographic Information Systems (GIS) are key tools for the creation of these maps, as they interpolate the results and allow for detailed visualisation. There are GIS available for free use, such as GvSIG from the Community of Valencia and QGIS.

Below are two examples of how data can be represented using heat maps:

Rainfall test (mm): A heat map can show the distribution of precipitation over a sports field. Areas with more rainfall are represented with more intense colours, while areas with less rainfall are shown with softer tones. This allows areas with drainage problems or excessive moisture to be identified.

Ground Hardness (gravities): Using a heat map, the hardness of the soil in different parts of the field can be visualised. Areas with high compaction will appear in darker colours, indicating areas that may require aerification. On the other hand, areas with lower hardness will be shown in lighter shades, indicating looser soils.

Heat maps provide an intuitive and clear view of the collected data, which facilitates decision making in the management of sports surfaces. By visualising the different magnitudes on a single map, it is possible to identify patterns and problem areas quickly and efficiently.