Philp Fraser, Al-Shallawi Ahmad, Kyriacou Theocharis, Blana Dimitra, Pandyan Anand
School of Health and Rehabilitation, Keele University, Keele, Staffordhire, UK.
Institute of Science and Technology in Medicine, Keele University, Keele, Staffordshire, UK.
BMJ Open Sport Exerc Med. 2020 Jan 14;6(1):e000634. doi: 10.1136/bmjsem-2019-000634. eCollection 2020.
This objective of this study was to evaluate whether combining existing methods of elastic net for zero-inflated Poisson and zero-inflated Poisson regression methods could improve real-life applicability of injury prediction models in football.
Predictor selection and model development was conducted on a pre-existing dataset of 24 male participants from a single English football team's 2015/2016 season.
The elastic net for zero-inflated Poisson penalty method was successful in shrinking the total number of predictors in the presence of high levels of multicollinearity. It was additionally identified that easily measurable data, that is, mass and body fat content, training type, duration and surface, fitness levels, normalised period of 'no-play' and time in competition could contribute to the probability of acquiring a time-loss injury. Furthermore, prolonged series of match-play and increased in-season injury reduced the probability of not sustaining an injury.
For predictor selection, the elastic net for zero-inflated Poisson penalised method in combination with the use of ZIP regression modelling for predicting time-loss injuries have been identified appropriate methods for improving real-life applicability of injury prediction models. These methods are more appropriate for datasets subject to multicollinearity, smaller sample sizes and zero-inflation known to affect the performance of traditional statistical methods. Further validation work is now required.
本研究的目的是评估将现有的零膨胀泊松弹性网方法与零膨胀泊松回归方法相结合，是否能提高足球运动中损伤预测模型的实际适用性。
在一个来自英国某单一足球队2015/2016赛季的包含24名男性参与者的已有数据集中进行预测变量选择和模型开发。
零膨胀泊松惩罚法的弹性网在存在高度多重共线性的情况下成功减少了预测变量的总数。另外还发现，易于测量的数据，即体重、体脂含量、训练类型、时长和场地、体能水平、“无比赛”的标准化时长以及比赛时间，可能会增加发生误工损伤的概率。此外，连续多场比赛以及赛季中受伤次数增加会降低不受伤的概率。
对于预测变量选择，已确定零膨胀泊松惩罚法的弹性网与使用零膨胀泊松回归模型来预测误工损伤相结合，是提高损伤预测模型实际适用性的合适方法。这些方法更适用于存在多重共线性、样本量较小以及已知会影响传统统计方法性能的零膨胀问题的数据集。现在需要进一步的验证工作。
