Classifying batted ball outcomes from Division I collegiate baseball players.

Modern technology challenges anecdotal beliefs on baseball performance. The study's purpose examines these beliefs by classifying batted ball outcomes. Three categories of independent variables (anthropometry, in-game situation, technique-based), from 1,922 batted ball outcomes produced by 230 players, were used to classify the likelihood of hits during 2021 college baseball games. Anthropometry included player's heights and weights. In-game situation entailed batter side, same side, ahead count, and pitch type. Technique-based variables measured by TrackMan radar included exit speed (ExSp), launch angle (LA), batted ball distance (BBD), and hang time (HT). Binary logistic regression analysis was performed with batted ball outcome as the dependent variable. Independent variables provided a good fit (χ2 (10) = 522.358, p < 0.01) and correctly classified nearly three-fourths of outcomes. Height (ß = 0.030, p < 0.05), ExSp (ß = 0.023, p < 0.05), LA (ß = 0.028, p < 0.01), and BBD (ß = 0.067, p < 0.01) each had significant positive associations, yet HT (ß = -1.661, p < 0.01) had a significant negative association, with batted ball outcomes. TrackMan provided four significant independent variables. Anthropometry's contribution to batting outcome was modest, while in-game situation's impact was non-significant; results contradict anecdotal beliefs of their importance.

Shoulder Movement-Centered Measurement and Estimation Scheme for Underarm-Throwing Motions.

Underarm throwing motions are crucial in various sports, including boccia. Unlike healthy players, people with profound weakness, spasticity, athetosis, or deformity in the upper limbs may struggle or find it difficult to control their hands to hold or release a ball using their fingers at the proper timing. To help them, our study aims to understand underarm throwing motions. We start by defining the throwing intention in terms of the launch angle of a ball, which goes hand-in-hand with the timing for releasing the ball. Then, an appropriate part of the body is determined in order to estimate ball-throwing intention based on the swinging motion. Furthermore, the geometric relationship between the movements of the body part and the release angle is investigated by involving multiple subjects. Based on the confirmed correlation, a calibration-and-estimation model that considers individual differences is proposed. The proposed model consists of calibration and estimation modules. To begin, as the calibration module is performed, individual prediction states for each subject are updated online. Then, in the estimation module, the throwing intention is estimated employing the updated prediction. To verify the effectiveness of the model, extensive experiments were conducted with seven subjects. In detail, two evaluation directions were set: (1) how many balls need to be thrown in advance to achieve sufficient accuracy; and (2) whether the model can reach sufficient accuracy despite individual differences. From the evaluation tests, by throwing 20 balls in advance, the model could account for individual differences in the throwing estimation. Consequently, the effectiveness of the model was confirmed when focusing on the movements of the shoulder in the human body during underarm throwing. In the near future, we expect the model to expand the means of supporting disabled people with ball-throwing disabilities.

Multi-segment trunk models used to investigate the crunch factor in golf and their relationship with selected swing and launch parameters.

The use of multi-segment trunk models to investigate the crunch factor in golf may be warranted. The first aim of the study was to investigate the relationship between the trunk and lower trunk for crunch factor-related variables (trunk lateral bending and trunk axial rotation velocity). The second aim was to determine the level of association between crunch factor-related variables with swing (clubhead velocity) and launch (launch angle). Thirty-five high-level amateur male golfers (Mean ± SD: age = 23.8 ± 2.1 years, registered golfing handicap = 5 ± 1.9) without low back pain had kinematic data collected from their golf swing using a 10-camera motion analysis system operating at 500 Hz. Clubhead velocity and launch angle were collected using a validated real-time launch monitor. A positive relationship was found between the trunk and lower trunk for axial rotation velocity (r(35) = .47, P < .01). Cross-correlation analysis revealed a strong coupling relationship for the crunch factor (R(2) = 0.98) between the trunk and lower trunk. Using generalised linear model analysis, it was evident that faster clubhead velocities and lower launch angles of the golf ball were related to reduced lateral bending of the lower trunk.

A proposed equation for calculating the total horizontal distance in projectiles of varying launch and landing levels.

Background: The projectile draws a path for the flight of the tool, a horizontal distance can be calculated according to the Galileo Galilei Law of Projectiles (GGLP), but only if the two points of the launch and fall of the tool are equal, otherwise we need an equation to be added to the (GGLP) to calculate the real distance that was generated due to the difference between the launch and fall points. There are several equations to calculate this, but they are complex and can be simplified. Methods: The proposed equation was tested by exporting samples from three different throwing events (javelin, shotput, disc) data in track and field games, to calculate the horizontal throwing distance. The proposed equation was based on the basics of mathematics and geometry. The equation was tested in terms if the height is zero, the proposed equation is suitable even for projectiles with equal levels, and the credibility of the proposed equation with the previous equation was tested statistically. Results: It was found that there were no differences between the two equations (p>0.05). and due to the relative ease of access of the proposed equation to very similar results, researcher suggests applying the proposed equation. Conclusions: The proposed equation contained the height factor in the previous equation, and when tested by several criteria, the proposed equation has proven its credibility, statistically and graphically. The ranges of theoretical achievement calculated by the proposed equation are often closer to the real achievements.