Overcoming the problem of multicollinearity in sports performance data: A novel application of partial least squares correlation analysis
Overcoming the problem of multicollinearity in sports performance data: A novel application of partial least squares correlation analysis
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DOI：
10.1371/journal.pone.0211776
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发表时间：
2019-02
期刊：
影响因子：
3.7
通讯作者：
D. Weaving;B. Jones;Matt Ireton;S. Whitehead;K. Till;C. Beggs
中科院分区：
文献类型：
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作者：
D. Weaving;B. Jones;Matt Ireton;S. Whitehead;K. Till;C. Beggs
专业体育组织投入大量资源收集和分析数据，以更好地了解影响表现的因素。全球定位系统（GPS）等非侵入性技术的最新进展意味着教练和体育科学家现在可以随时获得大量数据。然而，分析这些数据可能具有挑战性，特别是当样本量很小并且数据集包含多个高度相关的变量时，这在体育背景下经常发生。特别是多重共线性，如果处理不当，可能会产生问题，并可能导致错误的结论。在本文中，我们提出了一种新的“留一个变量”（LOVO）偏最小二乘相关分析（PLSCA）的方法，旨在克服多重共线性的问题，并显示如何可以用来识别训练负荷（TL）变量，影响最“结束健身”在年轻的橄榄球联盟球员。方法通过GPS、微机电系统（MEMS）和球员的训练过程感知用力评分（sRPE）对16名男性职业青少年橄榄球联盟运动员（17.7 ± 0.9岁）在6周的季前训练期内的累积TL进行量化。在此训练期之前和之后，参与者进行了30-15间歇性体能测试（30- 15 IFT），用于确定球员的“开始体能”和“结束体能”。总共收集了12个TL变量，并将这些变量与作为协变量的“起始适合度”一起沿着，针对“结束适合度”进行回归。然而，数据中相当大的多重共线性（9个变量的VIF >1000）意味着多元线性回归（MLR）过程是不稳定的，因此我们开发了一种新的LOVO PLSCA适应性来量化预测变量的相对重要性，从而最大限度地减少多重共线性问题。因此，LOVO PLSCA被用作通知和完善MLR过程的工具。结果LOVO PLSCA将超高速（>7 m·s-1）下的累积距离作为影响运动员体能提高的最重要TL变量，该变量导致奇异值惯性下降最大（5.93）。当包含在精细线性回归模型中时，该变量，沿着“起始适应度”作为协变量，解释了v30- 15 IFT“结束适应度”中73%的方差（p<0.001），并完全消除了任何多重共线性问题。结论LOVO PLSCA技术似乎是一个有用的工具，用于评估预测变量的相对重要性的数据集，表现出相当大的多重共线性。当用作过滤工具时，LOVO PLSCA产生了一个MLR模型，该模型证明了当“开始健身”作为协变量时，“结束健身”和预测变量“在非常高的速度下的累积距离”之间的显着关系。因此，LOVO PLSCA可能是体育科学家和教练寻求分析使用GPS和MEMS技术获得的数据集的有用工具。
Objectives Professional sporting organisations invest considerable resources collecting and analysing data in order to better understand the factors that influence performance. Recent advances in non-invasive technologies, such as global positioning systems (GPS), mean that large volumes of data are now readily available to coaches and sport scientists. However analysing such data can be challenging, particularly when sample sizes are small and data sets contain multiple highly correlated variables, as is often the case in a sporting context. Multicollinearity in particular, if not treated appropriately, can be problematic and might lead to erroneous conclusions. In this paper we present a novel ‘leave one variable out’ (LOVO) partial least squares correlation analysis (PLSCA) methodology, designed to overcome the problem of multicollinearity, and show how this can be used to identify the training load (TL) variables that influence most ‘end fitness’ in young rugby league players. Methods The accumulated TL of sixteen male professional youth rugby league players (17.7 ± 0.9 years) was quantified via GPS, a micro-electrical-mechanical-system (MEMS), and players’ session-rating-of-perceived-exertion (sRPE) over a 6-week pre-season training period. Immediately prior to and following this training period, participants undertook a 30–15 intermittent fitness test (30-15IFT), which was used to determine a players ‘starting fitness’ and ‘end fitness’. In total twelve TL variables were collected, and these along with ‘starting fitness’ as a covariate were regressed against ‘end fitness’. However, considerable multicollinearity in the data (VIF >1000 for nine variables) meant that the multiple linear regression (MLR) process was unstable and so we developed a novel LOVO PLSCA adaptation to quantify the relative importance of the predictor variables and thus minimise multicollinearity issues. As such, the LOVO PLSCA was used as a tool to inform and refine the MLR process. Results The LOVO PLSCA identified the distance accumulated at very-high speed (>7 m·s-1) as being the most important TL variable to influence improvement in player fitness, with this variable causing the largest decrease in singular value inertia (5.93). When included in a refined linear regression model, this variable, along with ‘starting fitness’ as a covariate, explained 73% of the variance in v30-15IFT ‘end fitness’ (p<0.001) and eliminated completely any multicollinearity issues. Conclusions The LOVO PLSCA technique appears to be a useful tool for evaluating the relative importance of predictor variables in data sets that exhibit considerable multicollinearity. When used as a filtering tool, LOVO PLSCA produced a MLR model that demonstrated a significant relationship between ‘end fitness’ and the predictor variable ‘accumulated distance at very-high speed’ when ‘starting fitness’ was included as a covariate. As such, LOVO PLSCA may be a useful tool for sport scientists and coaches seeking to analyse data sets obtained using GPS and MEMS technologies.