Performance monitoring for football players
Having worked as a consultant in sports project for several months, I found many interesting things that cannot be recognized if I still learn in school or labs. Therefore, in this blog, I want to share some experiences about the differences between research and practice. I hope this experience can help someone who has joined or will join the sports project.
When I work in labs, we always want to reflect some sports performance through accurate index and values as precise as possible. Thus, many biochemical or physiological index could be found to reflect a certain sports performance.
For example, we can use CK, urine protein, morning pulse, and others to evaluate a player’s fatigue. And then, we would compare those indexes and prove someone is better than others to evaluate a certain performance. After then, the chosen one could be taken as the Golden Standard and any other index discovered in the future should be compared with the golden standards to prove the new one is reasonable.
As shown in figure 1, dots with different colors represent different indexes. Then, we try to build the liner relationship between those indexes and performance. Finally, the best one (blue one) will be picked out. (the higher the r’s value, the higher the correlation is)
In a nutshell, our target is discovering the precisest index, no matter how difficult the measurement looks like. In one word, accuracy is the top priority.
While working with football teams and discussing with fitness coaches, I found most teams choose indexes that are easy to get. Comparing with lab research, they need to find a balance between accuracy and measurability. When the easy-index cannot reflect the performance directly, coaches usually trend to make the correlation between the easy-index and performance more relevant through doing many complex calculations with the easy-index, rather than find another index which maybe a little hard to measured.
Take fatigue as an example too. Coaches prefer to use some formula with running distance, HR or RPE to reflect players’ fatigue. Such as A/C ratio, training monotony, HRV. Those calculated indexes maybe not as precise as CK, but the HR, distance and RPE are much easy to acquire.
This layout reflects players’ fatigue by comparing the running distance between the latest week (acute) and previous weeks (chronic). And the dots represent players’ fatigue status. A too high value indicates that players may be in fatigue states. It is much easier to understand than figure 1 for coaches.
At first, I didn’t understand this choice. Because some indexes used by coaches are too “simple” and subjective, especially RPE. But the coaches give me an Irrefutable explanation. The main purpose of a team is training and playing matches. So, most of the monitoring cannot disturb training or matches. It means those indexes used for monitoring players must be easy to get and feedback the result rapidly. Therefore, some biochemical indexes especially invasive ones, such as CK and blood lactic, cannot satisfy the team request.
Moreover, there is another reason make this choice reasonable. Even if there is an insignificant deviation in those indexes (including the calculated one). when monitoring the same samples, if these deviations keep consistent, we can also judge players’ status or performance. After all, in teams, our purpose is merely comparing a player with others or his former states.
As shown in figure 3, the line represents the precious index (golden standard), and the dots represent the value got by complex calculations with a “simple” index. We can judge the performances through the height of the dot, although every dot is lower than the line.
In a word, coaches would like to sacrifice the accuracy for convenience and timeliness. It looks like coaches are very willing to use only one index if this one can reflect all the sports performances through complex calculations.
To sum up, I construct a table below to explain the difference in monitoring the players’ performance.
|type of index||direct one
tend to inner load
biochemical or physiology
|usually need some calculating
tend to external load
|purpose||find and prove golden standard||player compare|
even involves molecular
|make full use of simple index through tedious calculation|
And of course, it’s not definite. More and more labs are devoted to the research of basic data (“simple index”) and vice versa. This blog is just for reference. When the customer is a football or other sports team, perhaps, our innovation should focus on finding or developing some calculation formulas which can reflect or forecast something with a “simple” index (such as distance, HR, RPE, vertical height, and so on).