Hidden dynamics of soccer leagues: the predictive ‘power’ of partial standings

Objectives Soccer leagues reflect the partial standings of the teams involved after each round of competition. However, the ability of partial league standings to predict end-of-season position has largely been ignored. Here we analyze historical partial standings from English soccer to understand the mathematics underpinning league performance and evaluate the predictive ‘power’ of partial standings. Methods Match data (1995-2017) from the four senior English leagues was analyzed, together with random match scores generated for hypothetical leagues of equivalent size. For each season the partial standings were computed and Kendall’s normalized tau-distance and Spearman r-values determined. Best-fit power-law and logarithmic functions were applied to the respective tau-distance and Spearman curves, with the ‘goodness-of-fit’ assessed using the R2 value. The predictive ability of the partial standings was evaluated by computing the transition probabilities between the standings at rounds 10, 20 and 30 and the final end-of-season standings for the 22 seasons. The impact of reordering match fixtures was also evaluated. Results All four English leagues behaved similarly, irrespective of the teams involved, with the tau-distance conforming closely to a power law (R2>0.80) and the Spearman r-value obeying a logarithmic function (R2>0.87). The randomized leagues also conformed to a power-law, but had a different shape. In the English leagues, team position relative to end-of-season standing became ‘fixed’ much earlier in the season than was the case with the randomized leagues. In the Premier League, 76.9% of the variance in the final standings was explained by round-10, 87.0% by round-20, and 93.9% by round-30. Reordering of match fixtures appeared to alter the shape of the tau-distance curves. Conclusions All soccer leagues appear to conform to mathematical laws, which constrain the league standings as the season progresses. This means that partial standings can be used to predict end-of-season league position with reasonable accuracy

“You’ll Never Tweet Alone”: Managing Sports Brands through Social Media

The emergence of social media has had a profound impact on the way companies communicate and connect with their customers. Indeed, brands across different industries have started utilizing social media as part of their marketing strategies. However, the way in which businesses using social media for branding purposes has been limited. This study aims to address the above-mentioned gap by drawing on the professional sports industry. Employing a case study approach, the study analyzes the use of Twitter by a professional football organization in order to examine brand attributes (both product-related and non-product-related) and their relation to Twitter’s key engagement features (Reply, Retweet, Favorite). The results extend the current knowledge base of social media brand management in the sports industry, while offering significant insights for practitioners as far as the interaction of online followers to the communicated brand attributes is concerned