Elite players’ perceptions of football playing surfaces: a mixed effects ordinal logistic regression model of players’ perceptions

Development and evaluation of a sensory panel for collecting reliable player perceptions of third-generation synthetic turf football surfaces

Perceptions of synthetic surfaces used in football can vary considerably between players, and obtaining reliable feedback is challenging. The aim of this study was to develop a suitable process and evaluate the merits of establishing a sensory panel to assess the subjective attributes of third generation synthetic turf surfaces (3G turf) used in football. Focus groups with 12 male and 13 female footballers were conducted on an outdoor 3G turf pitch to develop a common language to describe sensory feedback related to player–surface interactions. Post-session analysis revealed two main themes related to player–surface interactions: hardness and grip. These themes were broken down further into five sensory attributes (Movement Speed, Slip, Movement Confidence, Leg Shock and Give) which were investigated further in an indoor test area containing ten 3G turf surfaces with controlled surface properties. A panel consisting of 18 University footballers (11 male and 7 female) undertook a screening and training session to refine the language associated with the sensory attributes and become familiar with the testing protocol. During a final evaluation session, players were asked to discriminate between surfaces using the paired comparison method for each of the sensory attributes. Player consistency remained similar between the screening and evaluation sessions whilst the panel’s ability to discriminate between surfaces improved during the evaluation session. Sensory training can therefore be a useful approach to aid players in differentiating between surfaces and lead to a greater understanding of athlete perceptions of surface attributes.

An Intelligent Prediction for Sports Industry Scale Based on Time Series Algorithm and Deep Learning

Aiming at some existing issues in the sports industry, the existing model is optimized by deep learning and time series theory based on the relevant algorithm, and the scale of the sports industry is analyzed and predicted by the model. The results show the following: (1) Based on the single-step prediction of time series, MNTS structural algorithm can be used to describe and study the sports industry scale with the single factor, and the correlation fitting degree is high. (2) Curves of different evaluation methods can include parts linear stage and nonlinear stage according to the magnitude of change. (3) Seen from the optimization model in this paper, the proposed method can describe both global and local trends of data. (4) It can be seen from the prediction curve that the overall state of fluctuation indicates that time will have a great impact on the relevant scale of the sports industry. Compared with single-step prediction, the accuracy of multistep prediction is higher, and the multistep prediction model based on time series can well characterize and predict the scale of the sports industry. By using the relevant time algorithm, the sports industry scale can be predicted and analyzed so as to provide theoretical support for the formulation and implementation of relevant policies.

Perceived Links Between Playing Surfaces and Injury: a Worldwide Study of Elite Association Football Players

Background

Injuries in association football (soccer) are debilitating for players and can also be detrimental to the success of a team or club. The type or condition of a playing surface has been empirically linked to injuries, yet results are inconclusive. The overall purpose of this study was to analyse elite football players’ perceived links between playing surfaces and injury from a worldwide cohort of players. The results of this study can help to inform areas for future playing surface research aimed at trying to alleviate user concerns and meet user (i.e. the player) needs.

Methods

Quantitative data were collected from 1129 players across the globe to address the aim of this study.

Results

Ninety-one percent of players believed the type or condition of a surface could increase injury risk. Abrasive injuries, along with soreness and pain, were perceived to be greater on artificial turf. Surface type, surface properties and age were all potential risk factors identified by the players and linked to the playing surfaces.

Conclusions

The results identified three areas where future research should be focussed to help develop surfaces that alleviate user concerns and meet user (i.e. player) needs: (i) current reporting of soreness, pain or fatigue as injuries, (ii) contribution of surface properties to injury; and (iii) surface experience of players from different countries differentiates their views of injury risk.