Correlation Between Grip Strength at Various Arm Orientations and Hitting Performance Metrics of Division I Collegiate Baseball Players

Dominate and non-dominate grip strength (GS) significantly correlated with bat speed (BS) in the sport of baseball. Various arm orientations occur throughout the swing; moreover, additional metrics beyond BS are indicative of baseball hitting performance. The correlation between various GS and hitting outcomes have not been empirically examined. PURPOSE: The aim of the current investigation was to examine the relationship of GS at various arm angles to various hitting performance metrics. METHODS: Division I collegiate baseball players (n = 17; height: 180.92 ± 5.61 cm; weight: 82.1 ± 11.12 kg) performed dominate and non-dominate maximal GS at five different arm and forearm orientations utilizing the Jamar Hydraulic Hand Dynamometer: 90-degree elbow flexion with (1) neutral (NDN), (2) supinated (NDS), and (3) pronated (NDP) forearm placement, as well as 120-degree elbow extension with 90-degree shoulder abduction with (4) supinated (AS) and (5) neutral (AN) forearm grips. At each angle, three attempts were permitted to exert maximal force, recorded in kg. Hitting metrics were gathered via Blast Motion Bat Sensors and Yakkertek Ball-Tracking System - metrics included: BS, peak hand speed (PHS), vertical bat angle (VBA), time to contact (TTC), attack angle (AA), power (PW), on plane efficiency (OPE), plane score (PS), rotational acceleration (RA), early connection (EC), connection at impact (CAI), as well as average exit-velocity (AEV), peak exit-velocity (PEV), hard hit percentage (HHP), damage percentage (DP), and average launch angle (ALA). A Pearson product-moment correlation coefficient (p \u3c .05) was employed to assess the relationship between GS and hitting performance. RESULT: Positive significant correlations were recognized between the following variables: dominate NDN and HHP (r = .559, p = .02), DP (r = .647, p = .007), and BS (r = .515, p = .034); non-dominate NDP and HHP (r = .497, p = .042), DP (r = .664, p = .005), and TTC (r = .519, p = .033); and non-dominate NDS and DP (r = .770, p \u3c .001), PS (r = .515, p = .035), OPE (r = .510, p = .036). A negative significant relationship was identified between non-dominate NDS and EC (r = -.629, p = -.007), and CAI (r = -.587, p = -.013). CONCLUSION: Supporting previous investigations, these results suggest dominate NDN, non-dominate NDP, and non-dominate NDS yielded the greatest influence on hitting performance among the tested GS positions; thus, potentially providing coaches with arm orientation specific GS training recommendation for baseball hitters

Establishing a Predictive Equation for Anaerobic Capacity Utilizing the 300-yard Shuttle Field Test

Anaerobic capacity can be tested through the Running-based Anaerobic Sprint Test (RAST), Wingate test (gold standard), and the 300-yard shuttle. While each testing is recognized as a valid method of assessing anaerobic capacity, previous investigations found no significant correlation between Wingate test and time to completion of 300-yard shuttle test. The insignificant relationship found between the 300-yard shuttle times and the Wingate outputs insinuate a need for further research investigating the correlations between these two anaerobic tests. PURPOSE: The aim of this study was to determine the influence of 300-yard shuttle measures on anaerobic capacity obtained via the Wingate test. METHODS: Twenty-two Division I softball players (20.41 +1.50 yr) completed two anaerobic testing sessions. Session 1 consisted of the 30s all out Wingate test. Sessions 2 was completed 48 hours following session 1 and involved the performance of two 300-yard shuttle run tests separated with 5 minutes rest. The Wingate test data included: anaerobic peak power (PP), average power (AP), power drop (PD), power drop per second (PD/s), maximal speed (MS), and power at maximal speed (PMS). The recorded 300-yard shuttle measures were time and kinetic energy factor (K-factor) (new anaerobic variable) for both attempts, as well as average time and average K-factor. K-factor during the 300-yard shuttle was calculated by utilizing the mass of participants multiplied by speed (distance divided by time elapsed) squared. A backwards stepwise multiple linear regression was employed to examine the influence of 300-yard shuttle on anaerobic capacity measure obtained via Wingate test. RESULTS: Statistical analysis identified the second 300-yard shuttle attempt time (S300) predicting AP as the model of best fit, which S300 explaining 32.7% of the variance of AP; furthermore, generating the following predictive equation: AP = 9.91 – (.049 x S300). Secondly, 84.2% of the variance in PD was explained by Average K-factor (AKF), establishing PD = -.85 + (.098 x AKF) as a predictive equation. Lastly, AKF, also, predicted 84.3% of the variance in PD/s: PD/s = -.028 + (.003 x AKF). CONCLUSION: An aspect of these finding contradicted preview investigations, as the S300 was recognized as a significant predictor of AP, suggesting faster 300-yard shuttle performance may increase AP. The positive significant correlation between the AKF and Wingate PD and PD/s suggest higher AKF may influence greater measures of PD and PD/s. These finding appear to support that calculating K-factor provides a richer understanding of field tested (300-yard shuttle) anaerobic capacity

The Relationship between 60-yard sprint, 30-yard sprint, Standardized Base Stealing Sprint, and Offensive Baseball Performance

Athletic performance testing protocols strive to accurately predicting or gain better understanding of an athlete’s performance within a particular sport or game. Regarding baseball, Wolfe and colleagues (2012) examined the predictive validity of the 60-yard shuttle run on pitching performance and concluded that strikeouts and innings pitched were significantly related to elevated kinetic energy factors of pitchers obtained from the shuttle run performance. Concerning for baseball position players, the 60-yard sprint (60YS) is traditionally utilized to showcase “baseball speed”, with minimal empirical evident supporting predictability to baseball specific performance outcomes. PURPOSE: The aim of the current investigation was to have examine the relationship between 60YS and offensive baseball performance outcomes, as well as the 30-yard sprint (30YS) test, and newly created standardized 1st to 2nd sprint (STS) test relationship to offensive baseball performance outcomes. METHODS: Division I baseball position players (n = 17; height: 180.92 ± 5.61 cm; weight: 82.1 ± 11.12 kg) performed three sprinting tests: 60YS, 30YS, and STS. Each test was recorded using the Brower Timing Gate System, with sprint time recorded in second. All testing was completed prior to the first game of the team’s college baseball season. Offensive baseball performance measures were recorded throughout 61 regular season games. The following baseball performance data was collected from the university’s official NCAA game performance website: total stolen bases (SB), stole base attempts (AT), stolen base percentage (SBP), at bats (AB), hits (H), doubles (DB), triples (TR), homeruns (HR), runs (R), base-on-balls (BB), hit by pitch (HBP), on base percentage (OBP), slugging percentage (SLP), touched bases (TB), runs batted in (RBI), and batting average (AVE). Pearson’s product-moment correlation (p \u3c .05) was employed to examine the correlation between sprint tests and offensive baseball performance. RESULTS: The statistical analysis revealed significant correlations between STS (p = .002, r = -.762), 30 yd sprint (p = .048, r = -.556), and 60 yd sprint (p = .038, r = -.578) and SB. Additionally, a significant correlation was identified between OBP and STS (p = .022, r = -.625), 30YS (p = .027, r = -.609), and 60YS (p = .020, r = -.633). Aside from these two baseball performance metrics, 30YS and 60YS had no significant correlation with baseball performance. However, STS, additionally, significantly (p \u3c .05) correlated with AT, AB, H, TR, HR, R, BB, SLP, TB, RBI, and AVE. CONCLUSION: The STS, 30YS, and 60YS had a significant relationship with offensive baseball performance. However, the results of 30YS and 60YS only correlated with two offensive measures, while STS had a significant correlation with all but 3 offensive performance metrics. These findings suggest STS may be a more relevant measure for predicting offensive baseball performance than the traditional 30YS and 60YS tests

Contracting on litigation

Two risk‐averse litigants with different subjective beliefs negotiate in the shadow of a pending trial. Through contingent contracts, the litigants can mitigate risk and/or speculate on the trial outcome. Contingent contracting decreases the settlement rate and increases the volume and costs of litigation. These contingent contracts mimic the services provided by third‐party investors, including litigation funders and insurance companies. The litigants (weakly) prefer to contract with risk‐neutral third parties when the capital market is transaction‐cost free. However, contracting with third parties further decreases the settlement rate, increases the costs of litigation, and may increase the aggregate cost of risk bearing.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149242/1/rand12274.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149242/2/rand12274_am.pd

The Restyled Federal Rules of Evidence

A lightly edited transcript of the Symposium held at the William & Mary School of Law on October 28, 2011

Intuition: Myth or a Decision-making Tool?

Faced with today’s ill-structured business environment of fast-paced change and rising uncertainty, organizations have been searching for management tools that will perform satisfactorily under such ambiguous conditions. In the arena of managerial decision making, one of the approaches being assessed is the use of intuition. Based on our definition of intuition as a non-sequential information-processing mode, which comprises both cognitive and affective elements and results in direct knowing without any use of conscious reasoning, we develop a testable model of integrated analytical and intuitive decision making and propose ways to measure the use of intuition

Choosing Among Alternative New Product Development Projects: The Role of Heuristics

The initial screening decision that marketing managers make is critical. It requires the selection of which innovation project to invest in, which is fundamental to marketing success. However, our knowledge of how managers make these decisions and how this impacts performance is limited. By drawing upon cognitive psychology and the managerial decision-making literature, we address two critical questions. The first question focuses on identifying specific decisionmaking types (e.g., specific heuristics, intuition) used when making an innovation-screening decision. Based on this analysis and prior research, we develop specific decision-maker profiles about how an individual manager decides. The second research question is about connecting these profiles with performance. Specifically, it addresses what the consequences of different decision-maker profiles are on the perceived accuracy and speed of decision-making? Data were collected from 122 senior managers in these industries. We find that when heuristics are used alone, or concurrently with intuition, managers make decisions that are as accurate as when they rely on analytical decision-making. However, the process is significantly faster. The findings provide an important step towards a more comprehensive understanding of decisionmaking at the front-end of innovation