Team Strength Research Articles

Low dimensionality of phenotypic space as an emergent property of coordinated teams in biological regulatory networks.

Cell-fate decisions involve coordinated genome-wide expression changes, typically leading to a limited number of phenotypes. Although often modeled as simple toggle switches, these rather simplistic representations often disregard the complexity of regulatory networks governing these changes. Here, we unravel design principles underlying complex cell decision-making networks in multiple contexts. We show that the emergent dynamics of these networks and corresponding transcriptomic data are consistently low-dimensional, as quantified by the variance explained by principal component 1 (PC1). This low dimensionality in phenotypic space arises from extensive feedback loops in these networks arranged to effectively enable the formation of two teams of mutually inhibiting nodes. We use team strength as a metric to quantify these feedback interactions and show its strong correlation with PC1 variance. Using artificial networks of varied topologies, we also establish the conditions for generating canalized cell-fate landscapes, offering insights into diverse binary cellular decision-making networks.

Weighted Approaches to Assessing Injury Burden in Team Sports

Injuries have a large impact on a team’s success during the season. Typically teams with more injuries, especially those affecting the most valuable players, will perform worse than similar teams with fewer injuries. A common way to quantify a team’s injury burden is by simply counting the number of injuries or games missed due to injury. However, these measures do not account for the individual value of the players injured. We assess two frameworks for how injuries may be weighted by player value. We propose a new position-weighted metric based on franchise tag values to measure injury burden in American football, called weighted Adjusted Games Lost (wAGL), and we test another player-level metric, Wins Above Replacement-Adjusted Injuries Lost (WAIL). WAIL uses a proprietary calculation of player Wins Above Replacement from Pro Football Focus. By comparing these measures to a gold standard of injury burden using pre- versus end-season betting lines and team strength estimates, we find that wAGL is a better measure of injury burden than current alternatives across all team-seasons considered.

Using the wisdom of crowds in sports: how performance analysis in football can benefit from the information enclosed in betting odds

ABSTRACT Betting odds have proven to contain highly accurate information on football matches, which researchers have used to study sports integrity, human behaviour, economic theories or forecasting, but widely neglected in performance analysis to date. The paper intends to stimulate changing this as betting odds contain a wealth of information usable to control for the inherent playing strength of teams as a situational variable and thus to better distinguish between strength, performance and success. Crowd wisdom and market efficiency are discussed as theoretical concepts driving the accuracy of betting odds. Moreover, based on a dataset of nearly 100 000 matches from 17 seasons in 17 European men’s football leagues, this study demonstrates that odds outperform methods related to points or goals in predicting the match winner as well as the anticipated number of goals by both teams. The code used in this study is made available to public in order to provide performance analysts with tools to extract information from odds. Finally, examples on how to benefit from betting odds in performance analysis are highlighted. Results of these use cases suggest that the relationship between performance and success can indeed be confounded by team strength, emphasising the need for further investigation.

Elementary econometric and strategic analysis of curling matches

PurposeWe develop a Markov model of curling matches. This enables strategic and econometric analyses to be performed alongside computer simulation work.Design/methodology/approachWe develop a Markov model of curling matches, parametrised by the probability of winning an end and the probability distribution of scoring ends. In practical applications, these end-winning probabilities can be estimated econometrically and are shown to depend on which team holds the hammer as well as the offensive and defensive strengths of the respective teams. Using a maximum entropy argument, based on the idea of characteristic scoring patterns in curling, we predict that the points distribution of scoring ends should follow a constrained geometric distribution.FindingsWe provide analytical results detailing when it is optimal to blank the end in preference to scoring one point and losing possession of the hammer. Statistical and simulation analysis of international curling matches is also performed.Originality/valueOur work adds to the theory and application of sports analytics, especially Markov models, and to the econometric and strategic analysis of curling matches.

Models and Rating Systems for Head-to-Head Competition

One of the most important tasks in sports analytics is the development of binary response models for head-to-head game outcomes to estimate team and player strength. We discuss commonly used probability models for game outcomes, including the Bradley–Terry and Thurstone–Mosteller models, as well as extensions to ties as a third outcome and to the inclusion of a home-field advantage. We consider dynamic extensions to these models to account for the evolution of competitor strengths over time. Full likelihood-based analyses of these time-varying models can be simplified into rating systems, such as the Elo and Glicko rating systems. We present other modern rating systems, including popular methods for online gaming, and novel systems that have been implemented for online chess and Go. The discussion of the analytic methods are accompanied by examples of where these approaches have been implemented for various gaming organizations, as well as a detailed application to National Basketball Association game outcomes.

Evaluation of a public health workplace staff psychosocial risk assessment and wellbeing initiative

Abstract Adverse psychosocial work environments can have a detrimental effect on staff wellbeing causing chronic stress leading to burnout. Despite legislation mandating regular risk assessments, only one third of workplaces assess psychosocial risks. A 2020 European report identified one of the main barriers being a reluctance to talk openly about psychosocial issues. This paper describes an evaluation of a staff psychosocial risk assessment and wellbeing initiative in a Public Health Department in Ireland. The initiative commenced in October 2022 during a period of major health service organisational change. Informed by positive organisational psychology conceptual frameworks, guided by the European Psychosocial risk assessment framework (PRIMA-EF) and utilising the HSE Work Positive (CI) survey tool it was possible to constructively identify and categorise psychosocial risks across the Public Health team. In the evaluation, a mixed methods approach was used based on Donabedian’s structures, processes and outcomes overarching framework and Nielsen’s Model of Process Evaluation Framework. The first three iterative quality improvement cycles of the intervention were reviewed. The evaluation demonstrated that all of Nielson’s three indicators for a successful organisational level staff wellbeing intervention had been met: there was evidence of improvement in both working conditions and staff wellbeing; iterative quality improvement cycles including appreciative inquiry staff collaborative engagement ensured staff views and key learning was captured; new organisational structures within the Department enabled the sustainability of this process. The evaluation indicates that the staff psychosocial risk assessment process is beneficial for staff wellbeing and that the methods used are appropriate. The resources used in this process continue to be readily available within the health service organisation. The evaluation supports continuation of this process in its present form. Key messages • This evaluation supports staff psychosocial risk assessment and mitigation processes and provides a framework for healthcare managers to implement such processes and comply with legislation. • This evaluation indicates that effective and sustainable collaborative staff engagement can be achieved using a positive focus which builds on team strengths and harnesses collective wisdom.

Competing against former teammates predicts team victory

The small but growing body of research on team vs. team competition focuses on predicting the winner based on multilevel factors, including the team's strength and prior relations among team members within a team. Our research demonstrates the significance and power of prior relations among members between competing teams in predicting the outcome of a contest. Leveraging data over 8 seasons of the Indian Premier League (IPL), we demonstrate the effects of competing against former teammates on a team's victory in IPL matches. If two teams, A and B, are competing in a match, and nA players from A are former teammates of players on B and nB players from B are former teammates of players on A, then if team A has smaller values of nA, it will have a competitive advantage over Team B with a higher value of nB. We call the magnitude of the difference of nA and nB the “ecosystem” factor in predicting performance. Using regression and stochastic network models, we find that the ecosystem factor significantly impacts the outcome of a match. Our findings have implications for franchise owners. While recruiting a player, franchise owners should not rely solely on the player's ability but also leverage the rivalry between former teammates.

Own goals in football: do they occur randomly or exhibit systematic patterns?

ABSTRACT Due to the low-scoring nature of soccer every single goal can drastically change the outcome of a match. Therefore, most large-scale performance studies aim at defining systematic processes and factors involved in goal scoring, while the scoring of the seemingly unintentional goals (like own goals) appears to be overlooked so far. In this study, factors for scoring own goals are investigated and compared to the factors connected to other (normal) goals. Therefore, a large dataset consisting of all goals from seven full seasons of the English Premier League (seasons 12/13 to 18/19) was analysed. Factors investigated include a priori factors like matchday, playing minute and team strength, but also specific situational variables like the zone from where the goal was scored, the body parts used, scoreline, defensive order and the preceding action. Favourites (p < .001) and home teams (p <0.05) profit more often from own goals than outsiders and away teams, suggesting systematic influences on own goal scoring. However, own goals significantly differ from all goals in the zone of goal scoring where more than 50% of the own goals are scored from the zone closest to goal (Zone 1). Additional aspects and potential reasons for the observed patterns are discussed.

Evolution of passing network in the Soccer World Cups 2010–2022

ABSTRACT This study investigates the evolution of passing networks (PN) at both team and player levels in the FIFA World Cups (WC) from 2010 to 2022. Analyzing 256 matches (7328 player observations) using a multiple-camera tracking system across four WCs, we considered six playing positions: goalkeeper (n = 521), central defender (n = 1192), fullback (n = 1223), midfielder (n = 2039), winger (n = 1320), and central forward (n = 1033). We used 17 network metrics and considered contextual variables such as team formation, and team ranking. Linear mixed-effect models analyzed differences in team and player PN parameters by year and team strength. Results showed a shift from possession-play to direct-play from the 2010 to 2018 WCs, with possession-play returning in 2022. Specifically, high- and low-quality teams significantly decreased their density, average degree (AD), modularity, and average path length in 2018 (p < 0.05). High-quality teams showed increased density, AD, and average weighted degree in 2022 (p < 0.05). Midfielders and central forwards exhibited significantly lower centrality parameters, whereas central defenders and goalkeepers showed increased centrality parameters (p < 0.05). This study highlights the evolutionary trends of passing relationships from a network analysis perspective over twelve years, providing insights into the changing dynamics of team interactions and positional prominence in elite soccer.

Emergency preparedness in the central sterile supply department: a multicenter cross-sectional survey

ObjectiveTo investigate the current situation of emergency preparation and emergency drill in the CSSD, and analyze its influence on the nurses’ emergency attitude and ability.MethodsThis study employed a multicenter stratified sampling method, conducted from January to June 2023 using the online survey, participants completed the general data, emergency preparedness and drill questionnaire, public health emergency response questionnaire and emergency capacity scale. An independent samples t test or Kruskal-Wallis test was used to analyse differences in nurses’ emergency capacity and attitudes.ResultsThe data from 15 provinces 55 hospitals in China. Overall, 77.58% of participants’ institutions set up emergency management teams, 85.45% have an emergency plan and revise it regularly. 92.12% store emergency supplies. All survey staff participated in the emergency drill, which predominantly consisted of individual drills (51.52%), with 90.30% being real combat drills, 49.09% of participants engaging in drills every quarter, and 91.52% of the drill’s participants exceeding 50%. The respondents’ emergency attitude score was (29.346 ± 6.029), their emergency ability score was (63.594 ± 10.413), and those with rescue experience showed a more positive attitude (Z = -2.316, P = 0.021). Different titles, education levels, rescue experience and the frequency of emergency drill affected the emergency rescue ability of the respondents (P < 0.05).ConclusionsMost medical institutions establish emergency management systems and plans, yet the content lacks geographical specificity.The duration and participation of emergency drills are high, but the effectiveness of the drills needs to be further improved, and the response capacity and attitudes of CSSD nurses are low. It is recommended that agencies develop comprehensive and targeted contingency plans to strengthen the inspection and evaluation of team strength, equipment and safeguards against the contingency plans, so as to ensure that the measures mandated by the contingency plans can be implemented promptly after the emergency response is initiated.

Predicting the Winner of a Twenty20 International Cricket Match: Classification and Explainable Machine Learning Approach

We present a supervised machine learning approach to predict the winner of a Twenty20 (T20) international match. The prediction dynamically changes as the match progresses. We also use explainable machine learning techniques (SHAP scores) to understand the importance of various features in making the decision at various stages of the T20 match. We present results on a dataset of 808 men's T20 international matches. The dynamic accuracy increases from about 55% in the initial stages of the T20 match to a maximum of about 85% in the final stages of the match (with an overall accuracy of about 63% in innings 1 and 74% in innings 2). SHAP scores reveal that team strength is an important feature in making the prediction in initial stages of the match; however, in the final stages, match situation plays the dominant role in the decision making process. Our work may help team coaches and captains to assess their chances of winning and/or chart a course towards winning in the ongoing T20 match, as well as be useful for sports analytics and gambling websites and apps.

Research on the Application of Kohonen Neural Networks in Football Clustering Analysis

This study applies Kohonen neural networks for clustering analysis in sports. By iteratively optimizing the objective function, it effectively avoids numerous subjective factors, providing a novel and efficient approach for obtaining objective clustering results. The results demonstrate that clustering analysis using Kohonen neural networks offers a clear practical value in evaluating the comprehensive strength of soccer teams. It serves as an effective method for rational, effective, objective, and quantifiable assessment of team tactics and strategies. Furthermore, this method is readily applicable to other comprehensive evaluations in competitive sports.

Are there meaningful changes in the performance of professional soccer teams from the first half of the match to the second? Evidence from the data of eight consecutive seasons of the Chinese Super League

ABSTRACT In the aim to better understanding the dynamic of performance changes between the first and second halves of professional soccer teams, statistics of 1899 matches from 2012 to 2019 of the Chinese Super League (CSL) were collected and analysed. The generalised mixed linear modelling was used to estimate the mean change of each of 40 physical and technical performance-related parameters of soccer teams from the first half of the match to the second, controlling the effects of match status, match location, team strength and opponent strength. Results showed that, out of the selected 40 parameters, only goals (first half vs second half M ± SD: 0.60 ± 0.76 vs 0.82 ± 0.92, ES; ±99% confidence limits: 0.21; ±0.05), shots (5.7 ± 2.8 vs 6.8 ± 3.3, 0.34; ±0.05), individual possession in the attacking third (55 ± 21 vs 62 ± 25, 0.31; ±0.05), cross (9.7 ± 4.6 vs 10.9 ± 5.8, 0.24; ±0.06) and yellow cards (0.75 ± 0.84 vs 1.24 ± 1.07, 0.45; ±0.06) showed substantially increment from the first half to the second, other parameters presented only trivial changes. Hence, it is concluded that the physical performance of soccer teams in the second half did not show meaningful change, while technical actions related to attacking tended to increase. The findings can offer soccer coaches valuable insights into when and how to adjust their tactics, manage their players, thus can maintain or enhance the team’s match performance.

553 Creating an In-Person Workshop Series Addressing Core Team Science Principles for Early Career Investigators

OBJECTIVES/GOALS: A barrier to the proliferation of team science is that academicians are often trained in disciplinary silos where “independent” research contributions are lauded. To tackle some of the most pressing scientific challenges, dismantling silos and increasing team science training efforts that focus on early career investigators is a must. METHODS/STUDY POPULATION: A team science training workshop for early career investigators from varied disciplinary backgrounds was informed by a 20-item needs assessment that addressed essential team science competencies and was completed by early career investigators participating in federally funded professional development programs on our campus. During the workshop, the benefits of cross-disciplinary teaming was discussed. Strategies including team formation, team effectiveness and/or dysfunction, diagnosing team strengths and weaknesses, and teaming in community settings were discussed. Instructional methods included short presentations, video clips, case studies, group discussions, pair and share activities, and panel discussions with expert role models encouraged active learning. RESULTS/ANTICIPATED RESULTS: The impact and value of the workshop series to participant’s professional development and knowledge of team science concepts will be evaluated before and after the workshop. Multiple Likert-scale items focused on team science competencies (e.g., confidence in your ability to carry out responsibilities specific to your role on a team, recognize when the team is not functioning well; engage team science practices in on-going research), and open-ended questions (e.g., importance of engaging community partners in academic research teams, vision of what factors contribute to an effective team science collaboration) will be completed by program participants before and after completing the workshop. DISCUSSION/SIGNIFICANCE: Effective collaboration among scientists with expertise in different disciplines is needed to address and solve complex scientific problems. We believe our interactive approach to team competency training sessions would work in a variety of settings and improve team skills.

Weekly Programming of Hamstring-Related Training Contents in European Professional Soccer.

Hamstring injuries in soccer continue to be a challenge for professionals who work with soccer players daily. Although its origin is multifactorial, the proper management of neuromuscular fatigue during the training microcycle is a very important factor to consider. There are no clear guidelines regarding the weekly distribution of certain exercises that demand the hamstrings. The main objective of this study was to describe the usual training practices of professional European soccer teams. An international observational survey design was applied to some of the strength and conditioning coaches of professional soccer teams. The survey included different neuromuscular demanding exercises for the hamstrings. For each exercise, the strength and conditioning coaches had to respond in relation to their frequency of use and timepoint depending on the day of the weekly microcycle. Although there is no strong consensus in this regard, there does seem to be a trend when applying certain exercises, especially on the days matchday-4 and matchday-3.

Utilizing Machine Learning for Comprehensive Analysis and Predictive Modelling of IPL-T20 Cricket Matches

Objective : Current study intends to develop a predictive model for Indian Premier League (IPL) cricket match results using machine learning techniques. In order to provide a precise framework that allows for the prediction of IPL match outcomes, it aims to examine player statistics, match dynamics, and historical data. Method : SVM, Random Forest, Logistic Regression, Decision Tree, and KNN models were used in this study to predict player performance on any given day. Form, fitness, and previous results were among the historical player data that were used as characteristics. Each model preceded through training and testing phases, with accuracy, precision, and recall metrics evaluated to determine the most effective algorithm for forecasting player performance. Findings : Final studies indicated that relative team strength of competitor teams, recent form of players, and opponent pairings are distinguishing features for predicting the performance of both players and teams on any given day. The multi-machine learning approach-based model that was constructed demonstrated an accuracy of 0.71, further indicating improved performance for the given challenge. Modelling team strength is similar to modelling individual player batting and bowling performances, which is the cornerstone of our approach. Novelty : This paper was designed based on a novel approach leveraging combinatorial machine learning methods. This has been found to demonstrate unprecedented performance improvement in predicting a player’s performance on a given day. Additionally, the presented approach may prove valuable in opening new avenues to advance machine learning applications in sports analytics by addressing the limitations of existing methods. Keywords: Machine Learning, Sports analytics, SVM, Random Forest, KNN

Winner prediction in an ongoing one day international cricket match

Cricket is a team sport with an intricate set of rules, where players specialize in multiple skills such as batting, bowling, and fielding. Playing conditions and home advantage also impact the game. Thus, it is quite challenging to build an accurate quantitative model for the game. In this paper, we provide a data driven approach to predict the winner of a cricket match. We divide the ongoing match into various states and provide a prediction for each state using supervised machine learning models. We employ dynamic features that account for the current match situation, together with the static features like team strength, winner of the toss, and the home advantage. We also use SHAP scores—an explainable AI technique—to interpret the proposed prediction model. We use ball-by-ball data from 1359 men’s one day international cricket matches played between January 2004 to January 2022 to present our results. We achieved the best in-play prediction accuracy of about 85% . SHAP scores reveal that during initial phases of the match, the model treats static features like team strength more important than others, in making the predictions. But as the match progresses, dynamic features capturing the current match situation become exceedingly important. Our work may be useful in preparing tools for in-play winner prediction for live cricket matches that can be used in websites and mobile applications covering the sport, in providing analytics during live television commentary, and in legal betting platforms.

Spectators or Influencers? The Crowd Effect Upon Winning in the NFL: A Natural Experiment

Abstract: Previous research has determined that home field advantage (HFA) is positively related to crowd density. Isolating this effect is a substantial empirical challenge as crowd density is endogenous with home win-likelihood via fan interest. We consider a natural-experimental setting that introduces exogenous crowd-density variation into National Football League (NFL) games. COVID-19 safety protocols allow us to disentangle crowd-presence, crowd-density, and built-environment effects upon HFA. We find strong evidence that crowd presence is a significant, substantial source of HFA, but crowd density is not. No-crowd games in 2020 featured no measurable HFA conditional upon team strengths, whereas partially fan-restricted games featured no significant decline in HFA relative to games without crowd restrictions (2016‒2019 and 2021). Results suggest HFA is fully attributable to crowd presence, with no evidence of stadium familiarity or travel distance effects. Betting markets efficiently predicted the partial-crowd effect from season's outset but adjusted incrementally (behaviorally) to incorporate the true no-crowd effect only by season's end.

Entrepreneurial team personality-faultline strength and performance: the non-linear relationship and boundary conditions

PurposeThe purpose of this paper is to explore the effect of a team’s personality faultlines on entrepreneurial performance. Additionally, this paper verifies the moderating effect of shared leadership on the connection between the two varies. The authors focus on the upper echelons theory and social identity theory as well as personality faultlines in entrepreneurial teams.Design/methodology/approachAn online survey was conducted using data collected in China. Data from 618 members from 144 new venture teams were collected to examine the authors’ hypothesis.FindingsThe authors find a U-shaped relationship between the strength of entrepreneurial teams' personality-faultline and entrepreneurial performance. Additionally, shared leadership negatively moderates this U-shaped relationship. Specifically, when the entrepreneurial team has a high level of shared leadership, both the positive and negative relationships between the strength of the personality faultlines and entrepreneurial performance are weaker.Originality/valueThe research offers important theoretical and practical implications for the formation of entrepreneurial teams.

The competitive balance of UEFA and CONMEBOL football leagues: On managing the number of teams in league design

AbstractThis article augments the literature on the analysis of competitive balance, encompassing its measurement, comparisons across leagues and seasons, and ramifications for league management. Central to our examination is the notion of competitive balance, which is intimately tied to the concentration concept. This concept can be depicted as a dual metric—capturing both inequality and the number of competing teams. Historically, its magnitude has been gauged using standardised measures of inequality and concentration. Among these, the Herfindahl–Hirschman index and its subsequent refinements stand out. We employ the distance‐to‐competitive‐balance index, which, contingent upon the metric space chosen, qualifies either as an inequality or concentration measure. Our analysis spans the 2009/2010 to 2018/2019 seasons for both the Union of European Football Associations (UEFA) and South American Football Confederation (CONMEBOL) championships in the pre‐COVID‐19 era. We discern a tangible and significant correlation between the competitive equilibrium of leagues and the performance metrics of national teams. Both the disparities in strengths of teams and the number of participating teams emerged as paramount variables shaping this competitive balance. Further, we delineate the elasticities of competitive balance relative to these determinants. Our insights bear implications for designing football championship structures and delineating sports policies; thus, they are valuable for relevant sports governing bodies and federations. The strategic decision to enlarge team count vis‐à‐vis curtailing discrepancies in team competencies is nuanced, varying across federations and confederations. The Big Five European leagues display heightened sensitivity to inequality factors (with interventions such as financial fair play, salary caps or revenue distribution yielding pronounced effects), whereas for CONMEBOL, elasticity in relation to team count holds greater significance.