The limitations of using statistical tools to evaluate athletes are pretty big. Here’s a breakdown:
Data Quality: If the data collected is wrong, it can lead to bad results. This can mess up how athletes train.
Context Ignorance: Statistics often ignore important things like the weather or an athlete's mood, which can really change how well they perform.
Over-Simplification: It’s easy to turn a complex skill into just numbers. But this means we miss out on important details about how an athlete performs.
Temporal Variability: Athletes can perform differently based on how tired they are or if they are hurt. This makes simple statistics not very useful.
To fix these problems, we can:
The limitations of using statistical tools to evaluate athletes are pretty big. Here’s a breakdown:
Data Quality: If the data collected is wrong, it can lead to bad results. This can mess up how athletes train.
Context Ignorance: Statistics often ignore important things like the weather or an athlete's mood, which can really change how well they perform.
Over-Simplification: It’s easy to turn a complex skill into just numbers. But this means we miss out on important details about how an athlete performs.
Temporal Variability: Athletes can perform differently based on how tired they are or if they are hurt. This makes simple statistics not very useful.
To fix these problems, we can: