Influence of Preseason Versus In-Season Play on Achilles Tendon Injuries in the National Football League

Background: A ruptured Achilles tendon (AT) can sideline a player for 6 to 12 months and reduce their power rankings by more than 50%. Previous research has compared AT rupture rates in different game conditions. Purpose: To determine environmental and physiological risk factors for AT tears, given the minimal amount of research on AT ruptures in the National Football League (NFL). Study Design: Case series; Level of evidence, 4. Methods: NFL players with a diagnosed AT tear between 2009 and 2016 were selected as the study population for this retrospective analysis. Data on NFL injury were collected from an established database composed of publicly available information. Player profiles were employed to determine position, team, and game statistics at the time of injury. The proportion of NFL rookies was approximated by summing the number of draft picks and the number of signed, undrafted free agents and measured against the number of roster spots before the season. Results: Between 2009 and 2016, there were 101 documented AT tears. Of these, 64% (65/101) occurred before the official season, including preseason games. Of the 36 tears that occurred in-season, 34 were during games. Overall, 29% (19/65) of the preseason tears occurred in rookies and 100% (36/36) of the in-season tears affected nonrookies. Of the rookies with AT ruptures, 42.11% returned to play in the NFL, while 62.20% of the nonrookies came back to partake in future seasons. Despite an average age of 26.7 years, the tear distribution was bimodal with players aged 24 and 36 years exhibiting the highest rates of tear. Conclusion: In our review of AT tears in NFL athletes, a large percentage of the tears occurred in rookie players, especially during the preseason. We also found that tears during the season occurred in only nonrookies, suggesting that the preseason is when rookies experience the greatest risk for injury.

Towards a Conceptual Framework of Comprehensive Video Game Player Profiles: Player Models, Mental Models, and Behavior Models

Current approaches to player profiling are limited in that they typically employ only a single one of numerous of available techniques shown to have utility for categorizing and explaining player behavior. We propose a more comprehensive Video Game Player Profile Framework that considers the demographic, psychographic, mental model, and behavioral modeling approaches shown to be effective for describing gamer populations. We suggest that our proposed approach can improve the efficacy of video game player profiles by grounding data-driven techniques in game analytics with the theoretical backing of demographic, psychometric, and psychographic measurements. We provide an overview of our proposed framework, discuss the usage and relevance of each component technique, and provide a proof-of-concept demonstration with archived data.

Annotation-Assisted Clustering of Player Profiles in Cultural Games: A Case for Tensor Analytics in Julia

Computer games play an increasingly important role in cultural heritage preservation. They keep tradition alive in the digital domain, reflect public perception about historical events, and make history, and even legends, vivid, through means such as advanced storytelling and alternative timelines. In this context, understanding the respective underlying player base is a major success factor as different game elements elicit various emotional responses across players. To this end, player profiles are often built from a combination of low- and high-level attributes. The former pertain to ordinary activity, such as collecting points or badges, whereas the latter to the outcome of strategic decisions, such as participation in in-game events such as tournaments and auctions. When available, annotations about in-game items or player activity supplement these profiles. In this article, we describe how such annotations may be integrated into different player profile clustering schemes derived from a template Simon–Ando iterative process. As a concrete example, the proposed methodology was applied to a custom benchmark dataset comprising the player base of a cultural game. The findings are interpreted in the light of Bartle taxonomy, one of the most prominent player categorization. Moreover, the clustering quality is based on intra-cluster distance and cluster compactness. Based on these results, recommendations in an affective context for maximizing engagement are proposed for the particular game player base composition.

Clustering performance in the European Basketball according to players’ characteristics and contextual variables

The aim of this study was two-fold: (i) to identify differences in the game performance profiles of basketball players in Euroleague and European national championships; and (ii) to analyse the influence of player-related (e.g. age or court-position) and contextual variables (e.g. distance travelled or game break) in players’ game-related statistics. Archival data from 2014/2015 and 2015/2016 seasons ( n = 16,268 individual game performance records) of Euroleague and European national championships were separately analyzed using a two-step cluster model and then a discriminant analysis to differentiate the obtained groups. The clustering process allowed identifying three and five different performance player profiles for Euroleague and national championships, respectively. The discriminant analysis identified differences in Euroleague playing profiles, particularly related to performers’ age and court-position. In national championships, this trend was not observed. Regardless of the competition, a relationship was perceived between players’ performance and minutes played; conversely, the players’ performance seems to not be affected by contextual variables. The current results provide evidence of different players’ profiles in elite basketball to coaches regarding the influence of player-related and contextual variables. Coaching staffs may account for this information for better management and understanding of their team composition when playing in different competitions.

Identification and Preference of Game Styles in LaLiga Associated with Match Outcomes

The objective was to model the teams’ styles of play (SoPs) in elite football and relate them to the match result. For this, the twenty Spanish first division teams in the 2016–2017 season were analysed, using nine interaction performance indicators (IRi). A principal component (PC) analysis was applied. From two PCs four SoPs were established: deep or high-pressure defending, and elaborate or direct attack. The SoPs were distributed according to average performance obtained throughout the championship. The connection between the preferred SoP and the final result was estimated. Teams with elaborate offensive styles and teams defensively minded got better results. In addition, most of the teams showed variability in their SoP. The applications of the study are (1) the IRi have served to identify SoP and can be used as a reference to optimize team performance; (2) teams should have a varied SoP repertoire, as well as being prepared to deal with different SoPs; (3) particular player profiles should be connected with the desired SoP when creating the squad and (4) clubs should develop a varied range of SoPs at their academies.

Identification and Preference of Game Styles in LaLiga Associated with Match Outcomes

The objective was to model the teams’ style of play (SoP) in elite football and relate them to the match result. For this, the twenty Spanish first division teams in the 2016-17 season were analysed, using nine interaction performance indicators (IRi). A principal component (PC) analysis was applied. From two PCs four SoPs were established: deep or high-pressure defending, and elaborate or direct attack. The SoPs were distributed according to average performance obtained throughout the championship. The connection between the preferred SoP and the final result was estimated. Teams with elaborate offensive styles and teams defensively minded got better results. In addition, most of the teams showed variability in their SoP. The applications of the study are: 1) the IRi have served to identify SoP and can be used as a reference to optimize team performance; 2) teams should have a varied SoP repertoire, as well as being prepared to deal with different SoPs; 3) particular player profiles should be connected with the desired SoP when creating the squad. 4) clubs should develop a varied range of SoPs at their academies.

High School Rhythm Section

Today, music educators and students have many resources available to study the specifics of playing any instrument. However, as educators it should be our job to guide students to use their ears along with all of the other resources. This chapter focuses on ways that the jazz ensemble conductor can coach high-school-level rhythm section players to make rehearsals more beneficial to all and to attain more authentic performances. The chapter includes details on various jazz styles, instrument-specific tips, and remedies for common issues within the rhythm section. It is divided into six main sections: (a) player profiles, (b) teaching jazz ensemble concepts, (c) awareness of other players, (d) improvisation, (e) chart reading, and (f) self-directed learning. Readers should find the information in this chapter helpful in developing their own rehearsal techniques that will fit their specific teaching situations.