以局部定位系統分析大專女子籃球員於賽季之運動學參數

Abstract

前言: 籃球競賽中的跑動以及變向等動作容易使身體產生高負荷 (PlayerLoad, PL)，高負荷會造成身體疲勞，導致受傷風險增加，選手的身體負荷隨著賽季不同週期也有所不同，急性慢性負荷比值 (ACWR) 為一種長期監控身體負荷之指標，可運用於選手於賽季週期之身體負荷，先前研究多以自覺負荷量表 (RPE)、心率帶以及血乳酸等儀器量化。局部定位系統 (LPS) 結合慣性感測器 (IMU) 能得知選手更全面的量化運動表現及監控負荷。目的: 分析選手於賽季中訓練以及競賽之運動學參數，以及ACWR於賽季週期之變化。方法:募集14名大專公開一級女子籃球員，使用LPS收取一賽季之訓練以及競賽，將不同賽季週期所收取之PL、高加減速度以及移動距離，以重複量數變異數分析比較差異性。正式競賽所收取之PL、高加減速度以及移動距離以皮爾森相關係數分析相關性。將一賽季所收取之PL推算為ACWR後以描述統計敘述其變化。結果: PL、高加減速度以及移動距離等參數於重複量數變異數分析結果後得知，預賽前訓練皆為最高值，其次為複賽前訓練時期，最低為決賽前時期。皮爾森相關係數分析結果得知標準化之高加減速度和PL有較高之相關性 (r = .911 , p<.001)。主力球員和替補球員之ACWR於賽季中之競賽後會有落差。結論: 賽季不同週期的訓練主力和替補選手於競賽期間之負荷不同，在訓練方面建議須有所調整，高加減速度對於體能負荷有較高的影響趨勢。以上結果可提供專項、體能教練以及相關運科人員做為參考。Introduction: Movements such as running and changing direction in basketball competitions easily generate high physical load (PlayerLoad, PL), which can lead to fatigue and increase the risk of injury. The physical load of players varies throughout different periods of the season. The (Acute-Chronic Workload Ratio, ACWR) is a long-term monitoring index of physical load and can be applied to players' physical load during different seasonal cycles. Previous studies have often used the (Rating of Perceived Exertion, RPE), heart rate monitors, and blood lactate measurements for quantification. (Local Positioning Systems, LPS) combined with (Inertial Measurement Units, IMU) provide a more comprehensive quantification of players' performance and load monitoring. Purpose: Analyzing kinematic parameters during training and competition throughout the season, as well as the changes in ACWR during different seasonal cycles. Methods: Fourteen university- Division I level female basketball players were recruited. LPS was used to collect data from a full season of training and competition. PL, high acceleration and deceleration, and distance covered in different seasonal cycles were compared using repeated measures ANOVA. The correlation between PL, high acceleration and deceleration, and distance covered during official matches was analyzed using Pearson's correlation coefficient. Descriptive statistics were used to describe the changes in ACWR throughout the season. Results: The repeated measures ANOVA showed that PL, high acceleration and deceleration, and distance covered were highest during pre-competition training, followed by pre-semifinal training, and lowest before the finals. Pearson's correlation coefficient indicated a high correlation between standardized high acceleration and deceleration and PL (r = .911, p < .001). ACWR showed discrepancies between starting and substitute players after competitions during the season. Conclusion: The PL differs between starters and substitutes during different periods of the season, indicating the need for adjustments in training. There’s high correlation between High acceleration and deceleration and physical load. These findings can serve as a reference for sport-specific coaches, strength and conditioning coaches, and sports science professionals.