The present paper investigates factors contributing to the home advantage, by using the exceptional opportunity to study professional football matches played in the absence of spectators due to the COVID-19 pandemic in 2020. More than 40,000 matches before and during the pandemic, including more than 1,000 professional matches without spectators across the main European football leagues, have been analyzed. Results support the notion of a crowd-induced referee bias as the increased sanctioning of away teams disappears in the absence of spectators with regard to fouls (p < .001), yellow cards (p < .001), and red cards (p < .05). Moreover, the match dominance of home teams decreases significantly as indicated by shots (p < .001) and shots on target (p < .01). In terms of the home advantage itself, surprisingly, only a non-significant decrease is found. While the present paper supports prior research with regard to a crowd-induced referee bias, spectators thus do not seem to be the main driving factor of the home advantage. Results from amateur football, being naturally played in absence of a crowd, provide further evidence that the home advantage is predominantly caused by factors not directly or indirectly attributable to a noteworthy number of spectators.

Performance analysis in football predominantly focuses on systematic contributions to success, thus neglecting the role of randomness. The present paper pursues a direct approach to quantify and analyse randomness in football by identifying random influences in the goal scoring process. The dataset includes all matches from the seasons 12/13 to 18/19 of the English Premier League, adding up to a total of 7,263 goals, that were checked for the occurrence of six variables of random influence. Additionally, the influence of nine situational variables was investigated. Results show that randomness was present for almost 50% of all goals. Moreover, it was demonstrated that the proportion of random goals decreased over the seven seasons (p < .001), is more pronounced for weaker teams (p < .05) as well as if the current scoreline is a draw (p < .05) and depends on the match situation (open play, freekick, corner, penalty). An improved understanding of randomness in football has important implications for both researchers and practitioners. Performance analysts should acknowledge randomness as a crucial factor to distinguish clearly between performance and success. Coaches could even consider the conscious creation of uncontrollable situations as a possible tactic to provoke random influences on goal scoring.

In the scientific community a large literature on sports forecasting exists, covering a wide range of different sports, methods and research questions. At the same time a lack of general literature such as reviews or meta‐analyses on aspects of sports forecasting can be attested, partly attributable to characteristics of forecasting in sports that make it difficult to present through systematic approaches. The present study contributes to filling this gap by providing a narrative review about forecasting related to the outcomes of sports events. An overview about relevant topics in forecasting the outcomes of sports events is presented, a basic methodology is discussed and a categorization of methods is introduced. Having a specific focus on forecasting from ratings, we shed light on the difference between systematic and unsystematic effects influencing the outcomes of sports events. Finally an outlook on the expected impact of the increasing amount and complexity of available data on future sports forecasting research is presented. The present review can serve as a valuable starting point for researchers aiming at the investigation of sports‐related forecasts, both helping to find appropriate methods and classify their work in the context of the state of research.

Sentiment analysis refers to the algorithmic extraction of subjective information from textual data and—driven by the increasing amount of online communication—has become one of the fastest growing research areas in computer science with applications in several domains. Although sports events such as football matches are accompanied by a huge public interest and large amount of related online communication, social media analysis in general and sentiment analysis in particular are almost unused tools in sports science so far. The present study tests the feasibility of lexicon-based tools of sentiment analysis with regard to football-related textual data on the microblogging platform Twitter. The sentiment of a total of 10,000 tweets with reference to ten top-level football matches was analyzed both manually by human annotators and algorithmically by means of publicly available sentiment analysis tools. Results show that the general sentiment of realistic sets (1000 tweets with a proportion of 60% having the same polarity) can be classified correctly with more than 95% accuracy. The present paper demonstrates that sentiment analysis can be an effective and useful tool for sports-related content and is intended to stimulate the increased use of and discussion on sentiment analysis in sports science.

Betting odds are frequently found to outperform mathematical models in sports related forecasting tasks, however the factors contributing to betting odds are not fully traceable and in contrast to rating-based forecasts no straightforward measure of team-specific quality is deducible from the betting odds. The present study investigates the approach of combining the methods of mathematical models and the information included in betting odds. A soccer forecasting model based on the well-known ELO rating system and taking advantage of betting odds as a source of information is presented. Data from almost 15.000 soccer matches (seasons 2007/2008 until 2016/2017) are used, including both domestic matches (English Premier League, German Bundesliga, Spanish Primera Division and Italian Serie A) and international matches (UEFA Champions League, UEFA Europe League). The novel betting odds based ELO model is shown to outperform classic ELO models, thus demonstrating that betting odds prior to a match contain more relevant information than the result of the match itself. It is shown how the novel model can help to gain valuable insights into the quality of soccer teams and its development over time, thus having a practical benefit in performance analysis. Moreover, it is argued that network based approaches might help in further improving rating and forecasting methods.

This study aimed to assess the technical match performance of top-class football players in a long-term perspective. Technical performance profiles of players according to five playing positions (central defender, full back, wide midfielder, central midfielder, forward) and five situational variables (competition stage, match location, quality of team, quality of opponent, match outcome) were established. Technical match data of players in the UEFA Champions League from season 2009–2010 to 2016–2017 were analyzed. The true effects of positional and situational variables on players’ technical performance were evaluated by the non-clinical magnitude-based inference. Results showed that the effect of competition stage on player’s performance was negligible. Quality of team, quality of opponent and match outcome revealed the strongest effects on player’s performance (ES: −0.42 ± 0.10–0.59 ± 0.10) while the effect of match location was relatively lower (ES: −0.32 ± 0.10–0.23 ± 0.07). The number of variables that showed statistical differences under five competing contexts for wide midfielders and forwards were higher than those of central defenders, full backs, and central midfielders. Differences of players’ match performance could mainly be identified in variables related to goal scoring, passing, and organizing, these findings may provide important insights for coaches and analysts during the match preparation and training session.