Metric tools usage is not just a matter of measuring but also knowing the meanings of basic statistics.
Good management of sport surfaces and its improvement, need to characterize their different values and statistic parameters which define them. From Tiloom we make sure our tools & metrics give the best quality ratings at:
- Statistical sensitivity, the smallest change the tool is able to detect, also named resolution.
- Repeatability, shows the maximum precision. A good tool must be able to repeat the same readings under the same conditions.
- Accuracy, capacity of the tools to show the true value of the measurement.
Tiloom support guaranties your metrics over time, we keep recalibrating our client’s tools
Once you have choosen your ideal metric tool, then you need to start measuring, colleting a large data base which needs to be set properly in order to visualize it clearly as with our agronomic big data analysis.
Usually our statistic variables will be cuantitative (numeric) like for example, the optimal reading of 43 Nm (with our @Deltec Light weight traction unit) on a football pitch or less frecuent we might use other variables like stability, which then we will refer them as a cualitative parameters.
Renovated low compacted pitches tend to have less stability than those more compacted old ones
It is really important to characterize our greens and football pitches and to do so we will take into account some important statistical parametres like :
- Arithmethic mean, being a clear example the pluviometry during a irrigation cicle.
- Median statistical parameter, the value located in the middle of a data base
- Statistical standard deviation, used in order to know how scattered we have our data from the arithmetic mean. The lower it is, more homogeneus our pitches or greens will be.
Low statistical standard deviation is synonymous of better pitches qualities and more uniformity. Our apps like Pogo turf pro & Field Tester will let you know to analysis all your data in a easy and confortable way to get the best out of your facilities.
Predictive analysis. Linear regression allows us to obtain relationships among two different measurements in a XY graphics, letting us know the behaviour of a variable from the other. It will not always be possible to do so though, the strongest relations will be given by its R2 index (0-1 range), being acceptable from 0,8.
Here is an interesting example of a predictive analysis. In the graph below, the amount of potassium in 5 saturated paste soil analyses is plotted and compared with 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, knowing our field is easier than ever.
If you want to know more about your sport facilities, please get in touch with us at firstname.lastname@example.org to quote you the best tools for your own assessment or call us to help you out with the measurements.