Predicting the results of soccer competitions and the contributions of match attributes, in particular, has gained popularity in recent years. Big data processing obtained from different sensors, cameras and analysis systems needs modern tools that can provide a deep understanding of the relationship between this huge amount of data produced by sensors and cameras, both linear and non-linear data. Using data mining tools does not appear sufficient to provide a deep understanding of the relationship between the match attributes and results and how to predict or optimize the results based upon performance variables. This study aimed to suggest a different approach to predict wins, losses and attributes’ sensitivities which enables the prediction of match results based on the most sensitive attributes that affect it as a second step. A radial basis function neural network model has successfully weighted the effectiveness of all match attributes and classified the team results into the target groups as a win or loss. The neural network model’s output demonstrated a correct percentage of win and loss of 83.3% and 72.7% respectively, with a low Root Mean Square training error of 2.9% and testing error of 0.37%. Out of 75 match attributes, 19 were identified as powerful predictors of success. The most powerful respectively were: the Total Team Medium Pass Attempted (MBA) 100%; the Distance Covered Team Average in zone 3 (15–20 km/h; Zone3_TA) 99%; the Team Average ball delivery into the attacking third of the field (TA_DAT) 80.9%; the Total Team Covered Distance without Ball Possession (Not in_Poss_TT) 76.8%; and the Average Distance Covered by Team (Game TA) 75.1%. Therefore, the novel radial based function neural network model can be employed by sports scientists to adapt training, tactics and opposition analysis to improve performance.

While tactical performance in competition has been analysed extensively, the assessment of training processes of tactical behaviour has rather been neglected in the literature. Therefore, the purpose of this study is to provide a methodology to assess the acquisition and implementation of offensive tactical behaviour in team handball. The use of game analysis software combined with an artificial neural network (ANN) software enabled identifying tactical target patterns from high level junior players based on their positions during offensive actions. These patterns were then trained by an amateur junior handball team (n = 14, 17 (0.5) years)). Following 6 weeks of tactical training an exhibition game was performed where the players were advised to use the target patterns as often as possible. Subsequently, the position data of the game was analysed with an ANN. The test revealed that 58% of the played patterns could be related to the trained target patterns. The similarity between executed patterns and target patterns was assessed by calculating the mean distance between key positions of the players in the game and the target pattern which was 0.49 (0.20) m. In summary, the presented method appears to be a valid instrument to assess tactical training.

One key to success in sport games is to anticipate individual and team tactical behaviour. The earlier and the more accurate players can identify the opponents’ strategy, the higher is the probability of success. To date it is not known if artificial intelligence is able to perform such anticipations in sports games. Therefore, the aim of the present study is to predict the shooting in team handball by means of artificial neural networks (ANN) in two approaches. In approach 1 only the shooting position was predicted and in approach 2 the last pass preceding the shot and the shot position itself was predicted from position data from preceding actions. Position data from 723 actions sequences of the EHF U18 Men European handball championship were annotated. A hybrid prediction system radial basis function network was used for prediction. Seventy per cent of the data-set were used for the network training, 15% for cross-validation and 15% for testing. The ANN predicted the real positions with an accuracy of 1.20 ± 0.46 and 1.42 ± 0.77 m for approach 1 and 2, respectively. Results demonstrate that ANNs are capable to predict position data in the sports game team handball with meaningful accuracy.

The purpose of this study was to examine the changes in co-activation around the knee joint during different walking speeds in healthy females using the co-activation index. Ten healthy females (age: 21.20 ± 7.21 years, height: 164.00 ± 4.00 cm, mass: 60.60 ± 4.99 kg) participated in this study and performed three walking speeds (slow, normal, and fast). A Qualisys 11-camera motion analysis system sampling at a frequency of 200 Hz was synchronized with a Trigno EMG Wireless system operating at a 2000 Hz sampling frequency. A significant decrease in the co-activation index of thigh muscles was observed between the slow and fast, and between the normal and fast, walking speeds during all walking phases. A non-significant difference was observed between the slow and normal walking speeds during most walking phases, except the second double support phase, during which the difference was significant. A negative relationship was found between walking speed and the co-activation index of thigh muscles in all speeds during walking phases: first double support (r = −0.3386, p < 0.001), single support (r = −0.2144, p < 0.01), second double support (r = −0.4949, p < 0.001), and Swing (r = −0.1639, p < 0.05). In conclusion, the results indicated high variability of thigh muscle co-activation in healthy females during the different walking speeds, and a decrease in the co-activation of the thigh muscles with the increase of speed.

The purpose of this pilot study was to evaluate the relationship between the kinematic variables of the right hand and left leg with ball velocity during jump-throwing phases in handball for better-informed training. We investigated ball velocity and the key kinematic variables of jump throwing during different throwing phases in three strides. Ten right-handed male handball professional players who had competed in the Egyptian Handball Super League participated in this study. Jump throwing performance was divided into three phases (cocking, acceleration and follow-through), which included eight events during the throwing. Five trials were captured for each player, and a 3D analysis was performed on the best trial. Results indicated that the velocity of the throwing hand was the most important variable during jump throwing, which was correlated with ball velocity during the three phases of performance in four events: Initial contact (IC) (r = 0.66*), initial flight (IF) (r = 63*), maximum height of the throwing hand (Max-HH) (r = 0.78*) and ground contact (GC) (r = 0.83*). In addition, the initial flight was the most important event in which players need to be using the best angles during performance, particularly the shoulder angle.