Metrics on sports surfaces

The use of instrumentation implies not only using good tools but also being familiar with certain statistical concepts.

For the correct management of sports surfaces and their continuous improvement, it is necessary to know the different values and statistical parameters that characterise them.

At Tiloom we make sure that our measurements and instrumentation present the best ratings in quality attributes, such as :

• SensitivityThe smallest variation that the measuring instrument can detect. If the reading is digital or against a scale, the sensitivity is called resolution.
• RepeatabilityDetermines the highest possible accuracy that can be achieved. An instrument must be capable of repeating readings with the same accuracy as far as can be read.
• AccuracyThe ability of the instrument to give the true value of the measurement (quality). The instrumentation offered by Tiloom guarantees your measurements over time, we recalibrate the equipment during its lifetime.

Once the right tool is chosen and we start to make the relevant measurements, we will obtain a set of data that we have to sort and visualise clearly through our agronomic analysis. Generally our statistical variable under study will be quantitative (numerical), e.g. a result of 43 Nm in a tensile strength test. traction (using Deltec lightweight tensile strength equipment) of a football pitch or in some cases we will also use qualitative variables for example in cases where the stability of the pitch is assessed.

Campos recently The inclusion of certain fine-textured %s in mixes to compensate for these characteristics is often less stable.

It is very important to characterise our courses and greens, and for this purpose we will make use of the most important statistics, such as the following:

• The average and as an interesting example the rainfall half collected when irrigating the field;
• The medianwhich is in the middle of the data values;
• The standard deviationto quantify the variation or dispersion of a set of numerical data. A low standard deviation indicates that most of the data in a sample tends to be clustered close to its mean (also called the expected value), while a high standard deviation indicates that the data is spread over a wider range of values.

A low standard deviation is synonymous with higher field quality, there is more uniformity over the whole sport surface.with our application Pogo turf pro & Field Tester you will get all the most interesting analysis that you can carry out in a simple and quick way.

The collection and recording of data makes it possible to study the data in order to obtain information, i.e. to carry out a predictive analysis, to know the future through what has been observed in the past.

Predictive analytics. Linear regression also allows us to obtain relationships between measures by comparing them on an X-Y plot. It allows us to predict the behaviour of one variable from another. It is not always possible to predict the variables, when performing a linear regression we obtain the R2 value. This value ranges from 0 to 1, being 0 when there is no relationship and 1 when the predictive relationship is maximal. An acceptable relationship is considered to be 0.8 and above.

Here is an interesting example of a predictive analysis. The following graph shows the amount of potassium in 5 saturated paste soil analyses and compares it with the salinity. The linear regression obtained gives an r2 of 0.77. This result is far from perfect if it approximates the relationship between salinity readings and potassium in soil.

Examples like this can be found by studying other variables. With this powerful information, getting to know our field is easier than ever. Contact us through info@tiloom.com to study your case and learn from the field to make the most of it.