In this paper, we carefully investigate how we can use multiple different
Natural Language Processing techniques and methods in order to automatically
recognize the main actions in sports events. We aim to extract insights by
analyzing live sport commentaries from different sources and by classifying
these major actions into different categories. We also study if sentiment
analysis could help detect these main actions.

我们研究了如何使用多种自然语言处理技术和方法自动识别体育赛事中的主要动作，并通过分析来自不同来源的现场体育评论以及将这些主要动作分类来提取洞察。我们还研究了情感分析是否能够帮助检测这些主要动作。

分析体育评论以自动识别事件并提取观点

Analyzing sports commentary in order to automatically recognize events  and extract insights

Predicting the outcome of sports events is a hard task. We quantify this
difficulty with a coefficient that measures the distance between the observed
final results of sports leagues and idealized perfectly balanced competitions
in terms of skill. This indicates the relative presence of luck and skill. We
collected and analyzed all games from 198 sports leagues comprising 1503
seasons from 84 countries of 4 different sports: basketball, soccer, volleyball
and handball. We measured the competitiveness by countries and sports. We also
identify in each season which teams, if removed from its league, result in a
completely random tournament. Surprisingly, not many of them are needed. As
another contribution of this paper, we propose a probabilistic graphical model
to learn about the teams' skills and to decompose the relative weights of luck
and skill in each game. We break down the skill component into factors
associated with the teams' characteristics. The model also allows to estimate
as 0.36 the probability that an underdog team wins in the NBA league, with a
home advantage adding 0.09 to this probability. As shown in the first part of
the paper, luck is substantially present even in the most competitive
championships, which partially explains why sophisticated and complex
feature-based models hardly beat simple models in the task of forecasting
sports' outcomes.

本文提出了一个通过计算比赛结果与完美平衡比赛结果之间距离的系数来衡量体育赛事预测的难度，并通过收集和分析 198 个体育联赛中的所有比赛来衡量竞争力与各国和各项运动的竞争力。文章还提出了概率图模型来学习团队技能，并将技能分解为与团队特征相关的因素，并估计大约 36％的概率，预测出在 NBA 联赛中，劣势团队获胜，这部分解释了为什么复杂的特征模型很难战胜简单的模型。