Predicting Wins, Losses and Attributes’ Sensitivities in the Soccer World Cup 2018 Using Neural Network Analysis

Abstract

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.

Machine Learning-Based Identification of the Strongest Predictive Variables of Winning and Losing in Belgian Professional Soccer

“…In our study however, the aim was to identify the strongest predictive variables, so instead of a division between historic and future games, a cross-validation approach was used to evaluate model performance. The prediction accuracy was considerably higher compared to a similar study conducted by [28], who reported classification accuracies of 72.7% and 83.3% when predicting respectively losing and winning in professional soccer using artificial neural networks. A classification accuracy of amply 89% can be considered as high, as it has been shown that chance plays a major role in goal-scoring [29].…”

A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

“…These systems are able to evaluate the activities of players successfully [79] such as the distance covered by players, shot detection [80,81], the number of sprints, player's position, and their movements [82,83], the player's relative position concerning other players, possession [84] of the soccer ball and motion/gesture recognition of the referee [85], predicting player trajectories for shot situations [86]. The generated data can be used to evaluate individual player performance, occlusion handling [21] by the detecting position of the player [87], action recognition [88], predicting and classifying the passes [89][90][91], key event extraction [92][93][94][95][96][97][98][99][100][101], tactical performance of the team [102][103][104][105][106], and analyzing the team's tactics based on the team formation [107][108][109], along with generating highlights [110][111][112][113]. Table 3 summarizes various proposed methodologies to resolve various challenging tasks in soccer with their limitations.…”

Tactics analysis and evaluation of women football team based on convolutional neural network

“…Hassan et al applied CNN to predict the winning or losing and attribute sensitivity of the 2018 World Cup. They thought that sports scientists could use a new neural network model based on radial function to adjust training, tactics and confrontation analysis to improve their performance 16 .…”