Discussions

Recommendations from the study:

It is clearly evident from the ML analysis, that Synthetic surface leads to (I) a Higher injury rate (II) Higher Severity of injury based on the number of days absent (III) injuries across the artificial turf (IV) more injuries to lower limbs and foot.
There is no difference in maximum speed & distance that players achieve between Artificial & Natural turf.

These findings perfectly match to study published by the American Journal of Sports Medicine, 2019,[8] “ Higher Rates of Lower Extremity Injury on Synthetic Turf Compared with Natural Turf Among National Football League Athletes Published in the American Journal of Sports Medicine, 2019” Since ML can handle a large amounts of Data, one of the possible recommendations to NFL is to further improve & increase parameters sensitivity. NFL can also add variables like footwear, padding and even putting accelerometer/wearable device to measure the physical parameters of athletes. The application of ML opens up the door to infinite opportunities for sports science.
In a second phase study, we can now collect more data around lower-limb injuries, as seen more on artificial turf. This can further improve assessment and accordingly one can work on the prevention of injuries by means of various proactive measurements. This opens the further study of injury prevention and sports training modules in conjunction with ML technique & applications.

Few Limitations:

As shown above, we have used the python algorithm of ML to analyze the data. Here accuracy of data is critical and also the more the volume of data, the better is prediction. We have shown in the above case, results can slightly vary based on sample size & accuracy too. This is an important factor to consider in deploying the ML in sports or any other domain too.