Guest editorial: special issue on machine learning for soccer

The effects of scheduling network models in predictive processes in sports

In many sports disciplines, the schedule of the competitions is undeniably an inherent yet crucial component. The present study modeled sports competitions schedules as networks and investigated the influence of network properties on the accuracy of predictive ratings and forecasting models in sports. Artificial networks were generated representing competition schedules with varying density, degree distribution and modularity and embedded in a full rating and forecasting process using ELO ratings and an ordered logistic regression model. Results showed that network properties should be considered when tuning predictive ratings and revealed several aspects for improvement. High density does not increase rating accuracy, so improved rating approaches should increasingly use indirect comparisons to profit from transitivity in dense networks. In networks with a high disparity in their degree distribution, inaccuracies are mainly driven by nodes with a low degree, which could be improved by relaxing the rating adjustment functions. Moreover, in terms of modularity, low connectivity between groups (i.e., leagues or divisions) challenges correctly assessing a single group’s overall rating. The present study aims to stimulate discussion on network properties as a neglected facet of sports forecasting and artificial data to improve predictive ratings.

A big data analysis of Twitter data during premier league matches: do tweets contain information valuable for in-play forecasting of goals in football?

Data-related analysis in football increasingly benefits from Big Data approaches and machine learning methods. One relevant application of data analysis in football is forecasting, which relies on understanding and accurately modelling the process of a match. The present paper tackles two neglected facets of forecasting in football: Forecasts on the total number of goals and in-play forecasting (forecasts based on within-match information). Sentiment analysis techniques were used to extract the information reflected in almost two million tweets from more than 400 Premier League matches. By means of wordclouds and timely analysis of several tweet-based features, the Twitter communication over the full course of matches and shortly before and after goals was visualized and systematically analysed. Moreover, several forecasting models including a random forest model have been used to obtain in-play forecasts. Results suggest that in-play forecasting of goals is highly challenging, and in-play information does not improve forecasting accuracy. An additional analysis of goals from more than 30,000 matches from the main European football leagues supports the notion that the predictive value of in-play information is highly limited compared to pre-game information. This is a relevant result for coaches, match analysts and broadcasters who should not overestimate the value of in-play information. The present study also sheds light on how the perception and behaviour of Twitter users change over the course of a football match. A main result is that the sentiment of Twitter users decreases when the match progresses, which might be caused by an unjustified high expectation of football fans before the match.

Uniform Manifold Approximation and Projection Analysis of Soccer Players

In professional soccer, the choices made in forming a team lineup are crucial for achieving good results. Players are characterized by different skills and their relevance depends on the position that they occupy on the pitch. Experts can recognize similarities between players and their styles, but the procedures adopted are often subjective and prone to misclassification. The automatic recognition of players’ styles based on their diversity of skills can help coaches and technical directors to prepare a team for a competition, to substitute injured players during a season, or to hire players to fill gaps created by teammates that leave. The paper adopts dimensionality reduction, clustering and computer visualization tools to compare soccer players based on a set of attributes. The players are characterized by numerical vectors embedding their particular skills and these objects are then compared by means of suitable distances. The intermediate data is processed to generate meaningful representations of the original dataset according to the (dis)similarities between the objects. The results show that the adoption of dimensionality reduction, clustering and visualization tools for processing complex datasets is a key modeling option with current computational resources.

Discovering associations between players' performance indicators and matches' results in the European Soccer Leagues

The application of data mining techniques and statistical analysis to the sports field has received increasing attention in the last decade. One of the most famous sports in the world is soccer, and the present work deals with it, using data from the 2009/2010 season to the 2015/2016 season from nine European leagues extracted from the Kaggle European Soccer database. Overall performance indicators of the four roles in a soccer team (forward, midfielder, defender and goalkeeper) for home and away teams are used to investigate the relationships between them and the results of matches, and to predict the wins of the home team. The model used to answer both these demands is the Bayesian Network. This study shows that this model can be very useful for mining the relations between players' performance indicators and for improving knowledge of the game strategies applied by coaches in different leagues. Moreover, it is shown that the ability to predict match results of the proposed Bayesian Network is roughly the same as that of the Naive Bayes model.

Fractional Dynamics in Soccer Leagues

This paper addresses the dynamics of four European soccer teams over the season 2018–2019. The modeling perspective adopts the concepts of fractional calculus and power law. The proposed model embeds implicitly details such as the behavior of players and coaches, strategical and tactical maneuvers during the matches, errors of referees and a multitude of other effects. The scale of observation focuses the teams’ behavior at each round. Two approaches are considered, namely the evaluation of the team progress along the league by a variety of heuristic models fitting real-world data, and the analysis of statistical information by means of entropy. The best models are also adopted for predicting the future results and their performance compared with the real outcome. The computational and mathematical modeling lead to results that are analyzed and interpreted in the light of fractional dynamics. The emergence of patterns both with the heuristic modeling and the entropy analysis highlight similarities in different national leagues and point towards some underlying complex dynamics.