Biomechanical Loads and Their Effects on Player Performance in NCAA D-I Male Basketball Games

Basketball games and training sessions are characterized by quick actions and many scoring attempts, which pose biomechanical loads on the bodies of the players. Inertial Measurement Units (IMUs) capture these biomechanical loads as PlayerLoad and Inertial Movement Analysis (IMA) and teams collect those data to monitor adaptations to training schedules. However, the association of biomechanical loads with game performance is a relatively unexplored area. The aims of the current study were to determine the statistical relations between biomechanical loads in games and training with game performance. Biomechanical training and game load measures and player-level and team-level game stats from one college basketball team of two seasons were included in the dataset. The training loads were obtained on the days before gameday. A three-step analysis pipeline modeled: (i) relations between team-level game stats and the win/loss probabilities of the team, (ii) associations between the player-level training and game loads and their game stats, and (iii) associations between player-level training loads and game loads. The results showed that offensive and defensive game stats increased the odds of winning, but several stats were subject to positional and individual performance variability. Further analyses, therefore, included total points [PTS], two-point field goals, and defensive rebounds (DEF REB) that were less subject to those influences. Increases in game loads were significantly associated with game stats. In addition, training loads significantly affected the game loads in the following game. In particular, increased loads 2 days before the game resulted in increased expected game loads. Those findings suggested that biomechanical loads were good predictors for game performance. Specifically, the game loads were good predictors for game stats, and training loads 2 days before gameday were good predictors for the expected game load. The current analyses accounted for the variation in loads of players and stats that enabled modeling the expected game performance for each individual. Coaches, trainers, and sports scientists can use these findings to further optimize training plans and possibly make in-game decisions for individual player performance.

A Call for More Snow Sampling

The snowpack is important for water resources, tourism, ecology, and the global energy budget. Over the past century, we have gone from point measurements of snow water equivalent (SWE) to estimate spring and summer runoff volumes, to remote sensing of various snowpack properties at continuously finer spatial and temporal resolutions, to various complexities of snowpack and hydrological modeling, to the current fusion of field data with remote sensing and modeling, all to improve our estimates of the snowpack and the subsequent runoff. However, we are still limited by the uncertainty induced by scaling from point field measurements to the area represented by remote sensing and modeling. This paper uses several examples of fine-resolution sampling to issue a call to snow hydrologists and other earth scientists to collect more data, or at least to thoroughly evaluate their sampling strategy for collecting ground-truth measurements. Recommendations are provided for different approaches to have more representative sampling, when at all possible, to collect at least a few more samples or data points.

A New Cosmological Model Based on Quantization of the Zero-point Field

Cosmic inflation has presented solutions for a number of important cosmological problems, yet left some unanswered. In this paper, we present a cosmological model based on the quantization of the zero-point field which is consistent with empirical data, requires fewer assumptions, and presents answers to some of those unanswered questions. A comparison between standard cosmology and the theory presented in this paper is given below. Vacuum energy density: Standard inflationary model needs both Hubble’s constant and Matter density to estimate it. But, new cosmological model needs only Hubble’s constant. Non-vacuum energy density: Standard model can’t predict it. But, the new model can predict using only Hubble’s constant. Ratio of vacuum energy to total energy: Standard model can’t predict it. But, new model can predict it, that too without using Hubble’s constant. Energy conservation: In standard model, total energy is not conserved before inflation. But, in the new model, energy is conserved right from beginning of the universe whose net energy (including gravitational potential energy) is always zero. Flatness and homogeneity: Standard model needs Inflaton field with a specific potential energy distribution to explain it. But, new model doesn’t need any such hypothetical field, just the zero-point field is sufficient. Based on the new cosmological model, in the conclusion, realistic possibility for existence of multiverse and a mechanism for end of universe are discussed.

Factors that Differentiate Winning and Losing in Men’s University Basketball

The objective of this study is to analyse the game-related statistics that differentiate winning and losing teams, according to the finale game scores in a men’s university basketball league. Samples were gathered from the archival data of the 2019–2020 regular season of the league. Sixteen game-related statistics were analysed: two- and three-point field-goals (both successful and unsuccessful), free-throws (both successful and unsuccessful), defensive and offensive rebounds, assists, steals, turnover, blocks, second-chance points, fast break points, fouls committed and received. The data were clustered into different game types based on the final outcome point differences: all games, balanced games (11 points and below) and unbalanced games (12 points and above). Discriminant function analysis was conducted to identify the performance indicators that classify winning and losing games. The results revealed that winning and losing in balanced games were discriminated by successful two-point field goals, unsuccessful two-point field goals, unsuccessful three-point field goals, successful free-throws, assists, steals, blocks, second-chance points, fast-break points, fouls committed, and fouls received. For unbalanced games, winning and losing were distinguished by successful two-point field goals, successful three-point field goals, successful free-throws, unsuccessful free-throws, defensive rebounds, blocks, fast-break points, and fouls received. In conclusion, offensive and defensive indices are critical to winning and losing in university-level basketball.

Hybrid Basketball Game Outcome Prediction Model by Integrating Data Mining Methods for the National Basketball Association

The sports market has grown rapidly over the last several decades. Sports outcomes prediction is an attractive sports analytic challenge as it provides useful information for operations in the sports market. In this study, a hybrid basketball game outcomes prediction scheme is developed for predicting the final score of the National Basketball Association (NBA) games by integrating five data mining techniques, including extreme learning machine, multivariate adaptive regression splines, k-nearest neighbors, eXtreme gradient boosting (XGBoost), and stochastic gradient boosting. Designed features are generated by merging different game-lags information from fundamental basketball statistics and used in the proposed scheme. This study collected data from all the games of the NBA 2018–2019 seasons. There are 30 teams in the NBA and each team play 82 games per season. A total of 2460 NBA game data points were collected. Empirical results illustrated that the proposed hybrid basketball game prediction scheme achieves high prediction performance and identifies suitable game-lag information and relevant game features (statistics). Our findings suggested that a two-stage XGBoost model using four pieces of game-lags information achieves the best prediction performance among all competing models. The six designed features, including averaged defensive rebounds, averaged two-point field goal percentage, averaged free throw percentage, averaged offensive rebounds, averaged assists, and averaged three-point field goal attempts, from four game-lags have a greater effect on the prediction of final scores of NBA games than other game-lags. The findings of this study provide relevant insights and guidance for other team or individual sports outcomes prediction research.

Prediction of Offensive Possession Ends in Elite Basketball Teams

In basketball, the end of the ball possession has been described as one of the most important determinants of successful offensive play by a team. The present study aimed to: (i) investigate outcomes according to the play types of ends of the ball possession; (ii) find the most efficient ball possessions during the game; (iii) predict most efficient ends of the ball possession by time in an elite basketball competition. The sample was composed of 38,640 situations of ends of the ball possession from 240 games of the 2017–2018 regular season of the men’s Euroleague that were quantitatively analyzed. According to the results, the predictive model can be used in modern basketball. The most efficient ends of the ball possession are the 2-point field goals on the fast break (78.2%), cuts (64.8%), pick and roll (P&R) screener (61.5%), and transition and offensive rebound (57.4%) situations. This information allows a better collective understanding of basketball, and it could be a great tool to use for coaches to prove which tactical solutions are to be considered when improving offense and defense strategies. It also contributes to the design of precise practice tasks of the coach that improve the game.

Innsbruck Teleportation Experiment in the Wigner Formalism: A Realistic Description Based on the Role of the Zero-Point Field

In this article, an undulatory description of the Innsbruck teleportation experiment is given, grounded in the role of the zero-point field (ZPF). The Wigner approach in the Heisenberg picture is used, so that the quadruple correlations of the field, along with the subtraction of the zero-point intensity at the detectors, are shown to be the essential ingredients that replace entanglement and collapse. This study contrasts sharply with the standard particle-like analysis and offers the possibility of understanding the hidden mechanism of teleportation, relying on vacuum amplitudes as hidden variables.

The Performance Evolution of Match Play Styles in the Spanish Professional Basketball League

The aim of this study is to analyse the performance evolution of all, and the dominant, team’s performances throughout an eight-season period within the Spanish professional basketball league. Match-related statistics were gathered from all regular season matches (n = 2426) played during the period 2009–2010 to 2016–2017. The non-metric multidimensional scaling model was used to examine the team’s profiles across seasons and for the most successful (playoff) teams. The main results showed that: 3-point field goals made (effect size, d = 0.61; 90% confidence interval, CI = 0.23; 1.37) and missed (d = 0.72; 90% CI = 0.35; 1.46), and assists (d = 1.27; 90% CI = 0.82; 1.86) presented a positive trend with an increased number of actions across the seasons; 2-point field goals made (d = 0.21; 90% CI = −1.25; 2.02) and missed (d = 0.27; 90% CI = −0.52; 0.92) were decreased; free throws made and missed, rebounds, fouls, blocks, steals and turnovers showed a relatively stable performance. The matrix solution (stress = 0.22, rmse (root mean squared error) = 7.9 × 104, maximum residual = 5.8 × 103) indicated minimal season-to-season evolution with the ordination plot and convex hulls overlapping. The two most dominant teams exhibited unique match patterns with the most successful team’s pattern, a potential benchmark for others who exhibited more dynamic evolutions (and less success). The current findings identified the different performances of teams within the Spanish professional basketball league over eight seasons with further statistical modelling of match play performances useful to identify temporal trends and support coaches with training and competition preparations.