Principal component analysis identifies different representative match load profiles in international women’s field hockey based on playing positions. [El análisis de componentes principales identifica diferentes perfiles de rendimiento en función de las posiciones en partidos internacionales de hockey hierba femenino].

Abstract

The aim of this study was to assess the principal components (PC) of women’s field hockey players´ TL distinguishing by playing positions (i.e., back, midfielder, forward). Data were collected from sixteen players belonging to the Spanish National women’s field hockey team during 13 official matches from the European Championship, World Series, and Pre-Olympic tournament. The Principal Component Analysis (PCA) grouped a total of 16 variables in five to six PC, explaining between 68.6 and 80% of the total variance. Different variables formed the PC that explain the player’s performance in different field positions. There were differences by positions in the distance covered at 21 to 24 km·h-1 (midfielders>forwards), decelerations from 5 to 4 m·s-2 (midfielders>forwards), and in maximum accelerations (midfielders>backs). Overall, strength and conditioning coaches should combine exercises which induce a high degree of aerobic endurance and power. However, some specification should be made by playing position: (1) defenders should perform training sessions with at least the same amount of volume as in the matches; (2) forwards should perform training efforts that ensure high repeated sprint ability; and (3) midfielders should perform a high training volume to develop high-intensity aerobic endurance, in combination with short-term efforts.

Reducing Big Data to Principal Components for Position-Specific Futsal Training

“…In team sports particularly, PCA has been used to reduce datasets and highlight what variables explain, especially for players’ behavior as related to playing positions. For example, Morencos et al, (2021) analyzed 16 elite hockey players during 13 official matches using 250 registered variables, reducing data descriptions to 16 variables with six principal components. These results showed differentiated variables of importance to match performance for defenders (i.e., distances at different intensities, sprint, PL, impacts, speed, acceleration, maximum decelerations, heart rate, and impacts), forwards (i.e., heart rate, accelerations and the distance at different intensities, decelerations, sprint, TL, maximum speed, and impacts), and midfielders (i.e., distances at different intensities, accelerations, speed, decelerations, heart rate, impacts, and sprints).…”