In this paper, we present the ON score for evaluating the performance of athletes and teams that includes a season-long evaluation system, a single-game evaluation, and an evaluation of an athlete's ...overall contribution to their team. The approach used to calculate the ON score is based on mixed-effects regression models that take into account the hierarchical structure of the data and a principal component analysis to calculate athlete rating. We apply our methodology to a large dataset of National Basketball Association (NBA) games spanning four seasons from 2015-2016 to 2018-2019. Our model is validated using two systematic approaches, and our results demonstrate the reliability of our approach to calculate an athlete's performance. This provides coaches, General Managers and player agents with a powerful tool to gain deeper insights into their players' performance, make more informed decisions and ultimately improve team performance. Our methodology has several key advantages. First, by incorporating the hierarchical structure of the data, we can obtain valuable information about an athlete's contribution within their team. Second, the use of principal component analysis allows us to calculate a single score, the ON score, that captures the overall performance of an athlete. Third, our approach is based on classical restricted likelihood methods, which makes the calculation faster than Bayesian methods typically requiring 1000 posterior samples. With our approach, coaches and managers can evaluate athletes' performance throughout the season, compare athletes and teams over a year, and assess an athlete's performance during a single game. Our methodology can also complement other ratings and box score metrics to provide a more comprehensive assessment of an athlete's performance as our method uses the hierarchical nature of performance data (i.e. player nested within team over season) which is typically ignored in player rating systems. In summary, our methodology represents a significant contribution to the field of sports analytics and provides the foundation for future developments.
The study aim was to assess the reliability to active trunk movements measurement in four sitting positions in wheelchair basketball players and to check their trunk movements in these positions. ...Eighteen volunteer wheelchair basketball athletes, with a minimum of five years' training experience, were asked to perform the maximum range of active trunk movement in three planes in four sitting positions (in a sports wheelchair with straps, without straps, on a table with feet on the floor, on a table without foot support). The range of movement was measured by the Kinect for Windows V2 sensor twice (with one-week interval). To assess the reliability, different statistical methods were used for each movement: significance of differences between the results (p-value), interclass correlation coefficient (ICC) and minimal detectable change (MDC). The limits of agreement analysis (LOA) were calculated. Differences between trunk movements in four positions were checked by the MANOVA (Wilk's Lambda and ETA2 were calculated if data were normally distributed). The significance level was set at α < .05. Friedman ANOVA and non-parametric Wilcoxon test with the Bonferroni correction were applied when data were not normally distributed. The significance level after Bonferroni correction was set at α < .013 (α = p/k, where p = .05, k-number of positions = 4). The measurement of active trunk movement in each plane was reliable (p > .05, no differences between the results, "very good"ICC, between .96-.99). In the position with straps, the trunk movement was significantly bigger than in other positions (p < .05), except for the position without straps (p > .05). The Kinect for Windows V2 sensor measured active trunk movement in a reliable manner and it can be recommended as a reliable tool for measuring trunk function. Utilizing straps by wheelchair basketball players increases their trunk movement.
Berger and Pope (2011)
show that being slightly behind increases the likelihood of winning in professional (National Basketball Association; NBA) and collegiate (National Collegiate Athletic ...Association; NCAA) basketball. We extend their analysis to large samples of Australian football, American football, and rugby matches, but find no evidence of such an effect for these three sports. When we revisit the phenomenon for basketball, we only find supportive evidence for NBA matches from the period analyzed in
Berger and Pope (2011)
. There is no significant effect for NBA matches from outside this sample period, for NCAA matches, or for matches from the Women’s National Basketball Association. High-powered meta-analyses across the different sports and competitions do not reject the null hypothesis of no effect of being slightly behind on winning. The confidence intervals suggest that the true effect, if existent at all, is likely relatively small.
This paper was accepted by Manel Baucells, behavioral economics and decision analysis.
Funding:
The authors acknowledge support fromthe Dutch Research Council.
Supplemental Material:
The data files and online appendix are available at
https://doi.org/10.1287/mnsc.2022.4372
.
ABSTRACTMangine, GT, Huet, K, Williamson, C, Bechke, E, Serafini, P, Bender, D, Hudy, J, and Townsend, J. A resisted sprint improves rate of force development during a 20-m sprint in athletes. J ...Strength Cond Res 32(6)1531–1537, 2018—This study examined the effect of a resisted sprint on 20-m sprinting kinetics. After a standardized warm-up, 23 (male = 10, female = 13) Division I basketball players completed 3 maximal 20-m sprint trials while tethered to a robotic resistance device. The first sprint (S1) used the minimal, necessary resistance (1 kg) to detect peak (PK) and average (AVG) sprinting power (P), velocity (V), and force (F); peak rate of force production (RFD) was also calculated. The second sprint (S2) was completed against a load equal to approximately 5% of the athleteʼs body mass. Minimal resistance (1 kg) was again used for the final sprint (S3). Approximately 4–9 minutes of rest was allotted between each sprint. Separate analyses of variance with repeated measures revealed significant (p ≤ 0.05) main effects for all sprinting kinetic measures except VPK (p = 0.067). Compared with S1, increased (p < 0.006) 20-m sprint time (3.4 ± 4.9%), PAVG (115.9 ± 33.2%), PPK (65.7 ± 23.7%), FAVG (134.1 ± 34.5%), FPK (65.3 ± 16.2%), and RFD (71.8 ± 22.2%) along with decreased (p < 0.001) stride length (−21 ± 15.3%) and VAVG (−6.6 ± 4.6%) were observed during S2. During S3, only RFD was improved (5.2 ± 7.1%, p < 0.001) compared with S1. In conclusion, completing a short, resisted sprint with a load equating to 5% of body mass before a short sprint (∼20-meters) does not seem to affect sprinting time or kinetics. However, it does appear to enhance RFD.
Quantifying athlete sleep patterns may inform development of optimal training schedules and sleep strategies, considering the competitive challenges faced across the season. Therefore, this study ...comprehensively quantified the sleep patterns of a female basketball team and examined variations in sleep between nights. Seven semi-professional, female basketball players had their sleep monitored using wrist-worn activity monitors and perceptual ratings during a 13-week in-season. Sleep variables were compared between different nights (control nights, training nights, training nights before games, nights before games, non-congested game nights, and congested game nights), using generalized linear mixed models, as well as Cohen's
and odds ratios as effect sizes. Players experienced less sleep on training nights before games compared to control nights, training nights, nights before games, and congested game nights (
< 0.05,
= 0.43-0.69). Players also exhibited later sleep onset times on non-congested game nights compared to control nights (
= 0.01,
= 0.68), and earlier sleep offset times following training nights before games compared to all other nights (
< 0.01,
= 0.74-0.79). Moreover, the odds of players attaining better perceived sleep quality was 88% lower on congested game nights than on nights before games (
< 0.001). While players in this study attained an adequate sleep duration (7.3 ± 0.3 h) and efficiency (85 ± 2%) on average across the in-season, they were susceptible to poor sleep on training nights before games and following games. Although limited to a team-based case series design, these findings suggest basketball coaches may need to reconsider scheduling team-based, on-court training sessions on nights prior to games and consider implementing suitable psychological and recovery strategies around games to optimize player sleep.
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