论文标题
分析板球比赛结果预测的长期短期记忆模型
Analysing Long Short Term Memory Models for Cricket Match Outcome Prediction
论文作者
论文摘要
随着技术的发展，在高级传感器的帮助下，在运动中收集了大量数据。体育分析是对这些数据的研究，为团队及其球员提供了建设性的优势。国际板球比赛在全球范围内都很受欢迎。最近，已经使用了各种机器学习技术来分析板球比赛数据，并将匹配结果视为胜利或输球。通常，这些模型利用了整个比赛水平统计数据，例如团队，场地，平均运行率，获胜保证金等，以预测比赛开始之前的比赛结果。但是，很少有作品可以根据逐球级别的统计数据提供见解。在这里，我们提出了一个新型的复发性神经网络模型，鉴于逐球统计，可以定期预测比赛的胜利概率。长期的短期内存（LSTM）模型作为输入球的明智功能以及训练数据集可用的匹配级别详细信息。它的预测是在比赛期间随时随地赢得比赛。这种洞察力将有助于团队预测他们在每个球后赢得比赛的可能性，并帮助他们确定他们在游戏策略中应进行的关键游戏内变化。
As the technology advances, an ample amount of data is collected in sports with the help of advanced sensors. Sports Analytics is the study of this data to provide a constructive advantage to the team and its players. The game of international cricket is popular all across the globe. Recently, various machine learning techniques have been used to analyse the cricket match data and predict the match outcome as win or lose. Generally these models make use of the overall match level statistics such as teams, venue, average run rate, win margin, etc to predict the match results before the beginning of the match. However, very few works provide insights based on the ball-by-ball level statistics. Here we propose a novel Recurrent Neural Network model which can predict the win probability of a match at regular intervals given the ball-by-ball statistics. The Long Short Term Memory (LSTM) Model takes as input the ball wise features as well as the match level details available from the training dataset. It gives a prediction of winning the match at any time stamp during the match. This level of insight will help the team to predict the probability of them winning the match after every ball and help them determine the critical in-game changes they should make in their game strategies.
