Institute for Sport, Physical Activity and Leisure, School of Sport, Leeds Beckett University, Leeds, West Yorkshire, England, United Kingdom.
Yorkshire Carnegie Rugby Union club, Leeds, England, United Kingdom.
PLoS One. 2019 Dec 18;14(12):e0225696. doi: 10.1371/journal.pone.0225696. eCollection 2019.
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.
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.
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.
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.
足球联赛反映了每轮比赛后各支参赛球队的部分排名情况。然而,部分联赛排名预测赛季末排名的能力在很大程度上被忽视了。在这里,我们分析英国足球的历史部分排名,以了解联赛表现背后的数学原理,并评估部分排名的预测能力。
分析了来自英国四个高级联赛的比赛数据(1995-2017 年),并为具有同等规模的假设联赛生成了随机比赛比分。为每个赛季计算了部分排名,并确定了肯德尔标准化 tau 距离和斯皮尔曼 r 值。对各自的 tau 距离和斯皮尔曼曲线应用最佳拟合幂律和对数函数,使用 R2 值评估拟合优度。通过计算 22 个赛季的第 10、20 和 30 轮的排名与最终赛季末排名之间的排名转换概率,评估了部分排名的预测能力。还评估了重新排列比赛赛程的影响。
所有四个英国联赛表现相似,无论涉及的球队如何,tau 距离都非常符合幂律(R2>0.80),而斯皮尔曼 r 值则遵循对数函数(R2>0.87)。随机联赛也符合幂律,但形状不同。在英国联赛中,球队相对于赛季末排名的位置在赛季早期就变得“固定”,而在随机联赛中则不是这样。在英超联赛中,最终排名的 76.9%的方差可以由第 10 轮解释,87.0%可以由第 20 轮解释,93.9%可以由第 30 轮解释。比赛赛程的重新排序似乎改变了 tau 距离曲线的形状。
所有足球联赛似乎都符合数学规律,这些规律限制了联赛排名随着赛季的进行而变化。这意味着可以使用部分排名来预测赛季末的联赛排名,具有相当的准确性。