Changes in Power Output in NCAA Football Linemen During Competitive Season

Changes in Power Output in NCAA Football Linemen During Competitive Season. Posey, Q., R. Cole, and J. Priest, Tarleton State University, Stephenville, TX 76402 Introduction Measuring power is a practice currently being developed by researchers. An available tool is the TENDO Weightlifting Analyzer (TWA). Although the TWA is a common research tool, there is little published research. The purpose of this study is to analyze OL and DL power output during in-season football. Methods Experimental Approach Seventeen NCAA division II football players in the Lone Start Conference were monitored during organized in-season weight training workouts. TWA measured and recorded their last set of squat. Bio feedback provided by the TWA was used to analyze each group. Subjects Seventeen NCAA division II football players (Age 21.1 ± 4.6 yrs, Ht. 1.6 ± 0.01m, Wt. 123.1 ± 7.4 kg , BMI 35.3 ± 3.2 kg.m-2), volunteered for the study, and had previously trained at least twice per week for 12 weeks. Subjects were familiarized with the TWA and squat protocol during pre-season. Protocol All subjects were required to lift four times a week. On the third workout of every week subjects back-squatted. All subjects completed a standardized warm-up. Subjects determine their own lifting weight. Researchers monitored squats and emphasized bar speed. The TWA was attached to the outside of the bar and measured average power (AP) and peak power (PP) output. Measurements were uploaded from the TWA into TENDO Sports Machine computer program and exported to Microsoft Excel®. Results Repeated measures ANOVA revealed no change in AP (F(3,45)=0.996, p\u3e.05), change in overall PP (F(3,45)=15.3, p\u3c.001) across 4 measures of the competitive season. No group interaction for AP (F(3,45)=.488, p\u3e.05), but PP by group interaction (F(3,45)=6.07, p=.001). AP 1 (W) AP 2 (W) AP 3 (W) AP 4 (W) PP 1 (W) PP 2 (W) PP 3 (W) PP4 (W) OL 911 ± 136 910 ± 124 850 ± 89 893 ± 19 1507 ± 251 1775 ± 258 1207 ± 141 1690 ±142 DL 947 ± 214 905 ± 184 904 ± 184 880 ± 110 1605 ± 343 1728 ± 487 1637 ± 317 1801 ± 215 Table 1. Average (AP) and Peak Power (PP) of Offensive (OL) and Defensive Linesmen (DL) During Competitive Season. Discussion The competitive football season produced normal bumps, bruises, and sprains which impacted the results obtained from bi-weekly measures of AP and PP. The observed changes in PP were attributed to the changes in peak bar velocities for this instantaneous measure, whereas the stability of AP was explained by the less volatile factor of average bar velocities. Conclusion Organized in-season weight training activities are effective at maintaining power output of offensive and defensive linemen

The JJ Shuttle and In-Game Defensive Basketball Performance for Collegiate Male Players

Agility is widely considered an important skill related fitness component in the game of basketball. Players are tasked to execute successful and efficient accelerations, sprints, abrupt stops, quick changes of direction, varying vertical jumps, and many times a combination of these motor skills. Agility can greatly impact the skills required for an athlete to excel on the court. The purpose of this study was to investigate how the agility of basketball players affected their in-game performance during regular season conference contests. The subjects (N = 10) in this study were members of a collegiate men’s basketball team. Agility of the subjects were measured using the JJ Shuttle which produces four segment times and a total time. These five shuttle times were compared for correlation to their in-game performance during regular season conference play. Performance measures of interest were steals, blocks, and defensive rebounds. A Pearson Correlation was conducted between the JJ Shuttle time segments and total time and the steals, blocks, and defensive rebounds of each player. There was a positive correlation between the duration of Segment 3 of the JJ Shuttle and the number of blocks (r = 0.65, p \u3c 0.05). The results of this study suggest the agility of male collegiate basketball players, as measured by the JJ Shuttle, does not have a strong correlation and is a poor predictor of the in-game performance of steals, blocks, and defensive rebounds. It is suggested that future studies increase the sample size and expand the subject parameters to determine a more holistic representation of this relationship

The Impact of Kinetic Energy Factors on Pitching Performance of NCAA Baseball Players

Kinetic energy is established by a mathematical equation involving the mass of an object and the speed at which the object travels, and is a relevant measure in regards to athletic performance due to frequent transfer of energy during sport (i.e. athlete to athlete (tackling), athlete to object (throwing), object to object (tennis/baseball hitting)). Previous research has utilized the 60-yd run-shuttle to examine kinetic energy factors (k-factor) of difference sports, recognizing significant differences in energy capability between gender, sport teams, and individual sport positions. The utilization of k-factor, pertaining to baseball, has distinguished significant positional (i.e. infielder, outfielder, catcher, pitcher) differences. However, the predictive influence of k-factors on in-game baseball performance necessitates further examination. PURPOSE: Analyzing the impact of k-factor on the pitching performance of NCAA baseball pitchers was the purpose of the current investigation. METHODS: NCAA pitchers (n=10, age 20.2 ± 1.9 yrs., weight 83.8 ± 10.3 kg, height 1.85 ± 0.48 m) completed a laser timed 60-yd run-shuttle, which yielded average k-factor scores for four contiguous agility segments (K1, K2, K3, and K4 of 10, 10, 20, and 20 yds., respectively), as well as Total Average K-Factor (Kavg). In-game performance was recorded upon the completion of the regular season, and included: Earned Runs Average (ERA), Win (W), Loss (L), Appearances (APP), Games Started (GS), Innings Pitched (IP), Runs (R), Hits (H), Earned Runs (ER), Base-on-Balls (BB), Strikeouts (SO), and Opponent Batting Average (B/AVE), measures normalized for innings pitched were R, H, ER, BB, and SO. To assess the impact of k-factor on pitching performance, backwards stepwise multiple linear regression analyses were employed. RESULTS: Results from the multiple linear regressions indicate that k-factor will yield significant prediction models (P\u3c0.05) for each of the following dependent variables: W, GS, and SO/I. Average k-factor accounted for 50% of the variance in GS (R2 = 0.50; SEE = 3.1 games), and 41% of the variance in W (R2 = 0.41; SEE = 1.5 wins), while K1, K2, and Kavg yielded a model that accounted for 78% of the variance (R2 = 0.78; SEE = 0.1) in SO/I. K-Factor did not produce a significant prediction model for ERA, L, APP, IP, H/I, R/I, ER/I, BB/I, or B/AVE. CONCLUSION: These results suggest elevated k-factor scores, or a pitchers capability to proficiently transfer energy during agility drills, contribute to improvements of in-game baseball pitching performance