The present study aimed to determine the physical-tactical profiles of elite football teams and individual players according to final league rankings. A total of 50 English Premier League matches (n = 100 match and 583 player observations) were analysed by coding the player's physical-tactical actions through synchronising tracking data and video. Final league rankings were categorised into Tiers: (A) 1st-5th ranking (n = 25), (B) 6th-10th ranking (n = 26), (C) 11th-15th ranking (n = 26), and (D) 16th-20th ranking (n = 23). One-way analyses of variance were used to compare match performances between different Tiers, and effect size (ES) was determined for the meaningfulness of the difference. Tier A teams covered 39-51% more high-intensity distance for 'Move to Receive/Exploit Space' (ES: 1.3-1.6, P < 0.01) and 'Run with Ball' (ES: 0.9-1.0, P < 0.05) than Tier C and D, and 23-94% more distance for 'Over/Underlap' (ES: 1.0, P < 0.01), 'Run in Behind/Penetrate' (ES: 0.7, P < 0.05), and 'Break into Box' (ES: 0.9, P < 0.05) compared to Tier C. Central and Wide Defensive Players in Tier A covered 65-551% more high-intensity 'Move to Receive/Exploit Space' distance compared to other Tiers (ES: 0.6-1.0, P < 0.01). Moreover, the additional options within the physical-tactical actions and zonal differences unveiled more meaningful insights into 'HOW' the top Tier teams physically and tactically perform. Thus, the amalgamated physical-tactical data help improve our understanding of a team's playing style relative to their competitive standard.
The present study aimed to contextualise physical metrics with tactical actions according to general and specialised tactical roles. A total of 244 English Premier League players were analysed by coding player's physical-tactical actions via the fusion of tracking data and video. Data were analysed across 5 general (Central Defensive Players = CDP, Wide Defensive Players = WDP, Central Midfield Players = CMP, Wide Offensive Players = WOP, Central Offensive Players = COP) and 11 specialised positions (Centre Backs = CB, Full-Backs = FB, Wing-Backs = WB, Box-to-Box Midfielders = B2BM, Central Defensive Midfielders = CDM, Central Attacking Midfielders = CAM, Wide Midfielders = WM, Wide Forwards = WF, Centre Forwards = CF). COP covered more distance at high-intensity (> 19.8 km · h-1) when performing actions such as 'Break into Box', Run in Behind/Penetrate', and 'Close Down/Press' than other positions (ES: 0.6-5.2, P < 0.01). WOP covered more high-intensity 'Run with Ball' distance (ES: 0.7-1.7, P < 0.01) whereas WDP performed more 'Over/Underlap' distance than other positions (ES: 0.9-1.4, P < 0.01). CDP and WDP covered more high-intensity 'Covering' distances than other positions (ES: 0.4-2.4, P < 0.01). Nonetheless, data demonstrated that implementing specialised positional analysis relative to a generalised approach is more sensitive in measuring physical-tactical performances of players with the latter over or underestimating the match demands of the players compared to the former. A contextualised analysis may assist coaches and practitioners when designing position or even player-specific training drills since the data provides unique physical-tactical trends across specialised roles.
The present study aimed to determine the physical-tactical trends of elite players/teams during peak 1-, 3- and 5-min periods of match-play. A total of 50 English Premier League matches (n = 583 player observations) were analysed by coding the players' physical-tactical activities through the synchronisation of tracking data and video. The contextualised data showed that during the peak periods (i.e., the most demanding passage of play), players/teams covered the largest distances for 'Recovery Run' (28-37%) out of possession and 'Support Play' (9-13%) in possession. In the following periods, players covered less high-intensity distance versus the average with a more pronounced decline in the next 1-min period than longer duration 3- and 5-min periods (48% vs ~25-30%, ES: 0.4-0.5, P < 0.01); team data showed similar trends with different relative patterns (31% vs 17-30%, ES: 0.5-0.8, P < 0.01). After peak periods, players/teams performed 20-53% less high-intensity distances for 'Covering' and 'Recovery Run' (ES: 0.2-0.7, P < 0.01) out of possession. However, players covered 28-91% less distance for 'Run with Ball' (ES: 0.1-0.5, P < 0.05) when in possession. Some physical-tactical actions exhibited inconsistency in different time durations of the next periods; however, these physical-tactical data were position-specific. This may signify that each position has certain physical-tactical actions to execute even after the peak periods, especially when they are tactically required to do so. As the data demonstrates unique physical-tactical trends of players/teams during the peak and next periods of play, this could help practitioners prescribe position- and player-specific drills, and better understand transient decrements in high-intensity running after intense passages of play.
This study aimed to: (1) develop an integrated approach to quantifying match physical-tactical performance and (2) comprehensively examine the validity and reliability of this novel approach. Both UEFA qualified coaches and performance analysts (n = 30) participated to verify the scientific robustness of this new method. The percentage of correct responses were used to verify the validity of the integrated approach and the minimum acceptable agreement was set at 80%. Two well-trained groups of observers analysed a randomly selected English Premier League match for inter- and intra-observer reliability using the kappa statistic. A high degree of validity was demonstrated as the mean percentage of correct responses by all participants, accounting for 91.8 ± 4.3% for all, 92.2 ± 4.7% for out-of-possession, and 91.6 ± 5.7% for in-possession physical-tactical variables. Inter- and intra-observer reliability were found to be strong (κ = 0.81) to almost perfect (κ = 0.94), respectively. Additional analyses demonstrated that there was a nearly perfect correlation between data derived from the novel filter used for the present study to capture high-intensity running and those obtained from the filter of the commercial data provider (r = 0.99; P < 0.01). The data demonstrates that the integrated approach is valid and reliable regarding the quantification of physical-tactical performances. Therefore, it is now possible to unveil unique high-intensity profiles of elite players related to key tactical actions. This may help coaches and practitioners better understand the physical-tactical performances of players, as well as effectively translate physical metrics into training.
There is growing interest in the use of the Internet for interacting with patients, both in terms of healthcare information provision and information gathering. In this article, we examine the issues in designing healthcare websites for elderly users. In particular, this article uses a year-long case study of the development of a web-based system for self-reporting of symptoms and quality of life with a view to examine the issues relating to website design for elderly users. The issues identified included the technical, social and medical aspects of website design for elderly users. The web-based system developed was based on the European Quality of Life 5-Dimensions health-status questionnaire, a commonly used tool for patient self-reporting of quality of life, and the more specific coronary revascularisation outcome questionnaire. Currently, self-reporting is generally administered in the form of paper-based questionnaires to be completed in the outpatient clinic or at home. There are a variety of issues relating to elderly users, which imply that websites for elderly patients may involve different design considerations to other types of websites.
Health Status , Internet , Quality of Life , Self Report , Aged , Aged, 80 and over , Attitude to Computers , Female , Humans , Male , User-Computer Interface
