Golf Club Selection with AI-Based Game Planning.

In the dynamic realm of golf, where every swing can make the difference between victory and defeat, the strategic selection of golf clubs has become a crucial factor in determining the outcome of a game. Advancements in artificial intelligence have opened new avenues for enhancing the decision-making process, empowering golfers to achieve optimal performance on the course. In this paper, we introduce an AI-based game planning system that assists players in selecting the best club for a given scenario. The system considers factors such as distance, terrain, wind strength and direction, and quality of lie. A rule-based model provides the four best club options based on the player's maximum shot data for each club. The player picks a club, shot, and target and a probabilistic classification model identifies whether the shot represents a birdie opportunity, par zone, bogey zone, or worse. The results of our model show that taking into account factors such as terrain and atmospheric features increases the likelihood of a better shot outcome.

Discovery and Characterisation of Forward Line Formations at Centre Bounces in the Australian Football League.

The extent of player formation usage and the characteristics of player arrangements are not well understood in Australian football, unlike other team-based invasion sports. Using player location data from all centre bounces in the 2021 Australian Football League season; this study described the spatial characteristics and roles of players in the forward line. Summary metrics indicated that teams differed in how spread out their forward players were (deviation away from the goal-to-goal axis and convex hull area) but were similar with regard to the centroid of player locations. Cluster analysis, along with visual inspection of player densities, clearly showed the presence of different repeated structures or formations used by teams. Teams also differed in their choice of player role combinations in forward lines at centre bounces. New terminology was proposed to describe the characteristics of forward line formations used in professional Australian Football.

Sequential movement pattern-mining (SMP) in field-based team-sport: A framework for quantifying spatiotemporal data and improve training specificity?

Athlete external load is typically quantified as volumes or discretised threshold values using distance, speed and time. A framework accounting for the movement sequences of athletes has previously been proposed using radio frequency data. This study developed a framework to identify sequential movement sequences using GPS-derived spatiotemporal data in team-sports and establish its stability. Thirteen rugby league players during one match were analysed to demonstrate the application of the framework. The framework (Sequential Movement Pattern-mining [SMP]) applies techniques to analyse i) geospatial data (i.e., decimal degree latitude and longitude), ii) determine players turning angles, iii) improve movement descriptor assignment, thus improving movement unit formation and iv) improve the classification and identification of players' frequent SMP. The SMP framework allows for sub-sequences of movement units to be condensed, removing repeated elements, which offers a novel technique for the quantification of similarities or dis-similarities between players and playing positions. The SMP framework provides a robust and stable method that allows, for the first time the analysis of GPS-derived data and identifies the frequent SMP of field-based team-sport athletes. The application of the SMP framework in practice could optimise the outcomes of training of field-based team-sport athletes by improving training specificity.

Clustering of match running and performance indicators to assess between- and within-playing position similarity in professional rugby league.

This study aimed to determine the similarity between and within positions in professional rugby league in terms of technical performance and match displacement. Here, the analyses were repeated on 3 different datasets which consisted of technical features only, displacement features only, and a combined dataset including both. Each dataset contained 7617 observations from the 2018 and 2019 Super League seasons, including 366 players from 11 teams. For each dataset, feature selection was initially used to rank features regarding their importance for predicting a player's position for each match. Subsets of 12, 11, and 27 features were retained for technical, displacement, and combined datasets for subsequent analyses. Hierarchical cluster analyses were then carried out on the positional means to find logical groupings. For the technical dataset, 3 clusters were found: (1) props, loose forwards, second-row, hooker; (2) halves; (3) wings, centres, fullback. For displacement, 4 clusters were found: (1) second-rows, halves; (2) wings, centres; (3) fullback; (4) props, loose forward, hooker. For the combined dataset, 3 clusters were found: (1) halves, fullback; (2) wings and centres; (3) props, loose forward, hooker, second-rows. These positional clusters can be used to standardise positional groups in research investigating either technical, displacement, or both constructs within rugby league.

A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches.

Recent advances in sensor technologies, in particular video-based human detection, object tracking and pose estimation, have opened new possibilities for the automatic or semi-automatic per-frame annotation of sport videos. In the case of racket sports such as tennis and padel, state-of-the-art deep learning methods allow the robust detection and tracking of the players from a single video, which can be combined with ball tracking and shot recognition techniques to obtain a precise description of the play state at every frame. These data, which might include the court-space position of the players, their speeds, accelerations, shots and ball trajectories, can be exported in tabular format for further analysis. Unfortunately, the limitations of traditional table-based methods for analyzing such sport data are twofold. On the one hand, these methods cannot represent complex spatio-temporal queries in a compact, readable way, usable by sport analysts. On the other hand, traditional data visualization tools often fail to convey all the information available in the video (such as the precise body motion before, during and after the execution of a shot) and resulting plots only show a small portion of the available data. In this paper we address these two limitations by focusing on the analysis of video-based tracking data of padel matches. In particular, we propose a domain-specific query language to facilitate coaches and sport analysts to write queries in a very compact form. Additionally, we enrich the data visualization plots by linking each data item to a specific segment of the video so that analysts have full access to all the details related to the query. We demonstrate the flexibility of our system by collecting and converting into readable queries multiple tips and hypotheses on padel strategies extracted from the literature.

Predicting Wins, Losses and Attributes' Sensitivities in the Soccer World Cup 2018 Using Neural Network Analysis.

Predicting the results of soccer competitions and the contributions of match attributes, in particular, has gained popularity in recent years. Big data processing obtained from different sensors, cameras and analysis systems needs modern tools that can provide a deep understanding of the relationship between this huge amount of data produced by sensors and cameras, both linear and non-linear data. Using data mining tools does not appear sufficient to provide a deep understanding of the relationship between the match attributes and results and how to predict or optimize the results based upon performance variables. This study aimed to suggest a different approach to predict wins, losses and attributes' sensitivities which enables the prediction of match results based on the most sensitive attributes that affect it as a second step. A radial basis function neural network model has successfully weighted the effectiveness of all match attributes and classified the team results into the target groups as a win or loss. The neural network model's output demonstrated a correct percentage of win and loss of 83.3% and 72.7% respectively, with a low Root Mean Square training error of 2.9% and testing error of 0.37%. Out of 75 match attributes, 19 were identified as powerful predictors of success. The most powerful respectively were: the Total Team Medium Pass Attempted (MBA) 100%; the Distance Covered Team Average in zone 3 (15-20 km/h; Zone3_TA) 99%; the Team Average ball delivery into the attacking third of the field (TA_DAT) 80.9%; the Total Team Covered Distance without Ball Possession (Not in_Poss_TT) 76.8%; and the Average Distance Covered by Team (Game TA) 75.1%. Therefore, the novel radial based function neural network model can be employed by sports scientists to adapt training, tactics and opposition analysis to improve performance.

Team and seasonal performance indicator evolution in the ANZ Championship netball league.

Analysis of netball has received scant attention in the literature and there is little understanding of the dynamics of netball game-play. This study aimed to analyse team and seasonal performance indicator (dis)similarity in the ANZ Championship (netball). Total season values for nine performance indicators were analysed for the ten ANZ Championship teams from 2009 to 2016. The data were analysed using a multivariate, distance-based, approach. Specifically, non-metric multidimensional scaling was used to examine seasonal and team (dis)similarity. After declining from 2009, shooting percentage, goal assists, centre pass receives, penalties and turnovers began to rise from 2011. Both penalties and turnovers declined from 2015, in addition to attempts at goal. The two-dimensional multivariate ordination plot showed relative similarity between each team and season over the observational period, suggesting stagnant game-play dynamics. Further, despite idiosyncratic seasonal profiles, teams generally followed similar directional progression across the ordination surface. Despite being observed in other team invasion sports, league-wide synchronous evolutionary tendencies were not observed within the ANZ Championships between the 2006 to 2016 seasons. However, certain teams did display seasonal fluctuation in their observed multivariate profile, with these seasonal idiosyncrasies being discussed relative to coaching and playing roster changes specific to the analysed team.

Evolution of game-play in the Australian Football League from 2001 to 2015.

This study investigated the evolution of game-play manifested via team performance indicator characteristics in the Australian Football League (AFL) from the 2001 to 2015 seasons. The mean values for 18 performance indicators were collated for every AFL team over 15-seasons. A multivariate analysis was used to uncover temporal trends in the dataset. Compared to the 2004 season, the 2005 to 2010 seasons were characterised by large growth in the counts of handballs (d = 0.83; 90% CI = 0.22-1.43), disposals (d = 1.24; 90% CI = 0.59-1.87), uncontested possessions (d = 1.37; 90% CI = 0.71-2.01), clangers (d = 2.14; 90% CI = 1.39-2.86) and marks (d = 1.43; 90% CI = 0.76-2.07). Contrastingly, the effective disposal percentage declined rapidly during the same period. The number of inside 50 m counts remained stable throughout the 15-season period. The ordination plot of league-wide performance indicator characteristics illustrated a distinct cluster from the 2001 to 2004 seasons, an abrupt shift from the 2005 to 2009 seasons, and an emergent (re)stabilisation from the 2010 to 2015 seasons. The results demonstrate the synchronous league-wide evolution of game-play in the AFL from the 2001 to 2015 seasons. Amongst other constituents, this evolution likely reflects the introduction of modernised coaching strategies, rule changes and changing perceptions of rule interpretations.

Integration of model-based recursive partitioning with bias reduction estimation: a case study assessing the impact of Oliver's four factors on the probability of winning a basketball game.

In this contribution, we investigate the importance of Oliver's Four Factors, proposed in the literature to identify a basketball team's strengths and weaknesses in terms of shooting, turnovers, rebounding and free throws, as success drivers of a basketball game. In order to investigate the role of each factor in the success of a team in a match, we applied the MOdel-Based recursive partitioning (MOB) algorithm to real data concerning 19,138 matches of 16 National Basketball Association (NBA) regular seasons (from 2004-2005 to 2019-2020). MOB, instead of fitting one global Generalized Linear Model (GLM) to all observations, partitions the observations according to selected partitioning variables and estimates several ad hoc local GLMs for subgroups of observations. The manuscript's aim is twofold: (1) in order to deal with (quasi) separation problems leading to convergence problems in the numerical solution of Maximum Likelihood (ML) estimation in MOB, we propose a methodological extension of GLM-based recursive partitioning from standard ML estimation to bias-reduced (BR) estimation; and (2) we apply the BR-based GLM trees to basketball analytics. The results show models very easy to interpret that can provide useful support to coaching staff's decisions. Supplementary Information: The online version contains supplementary material available at 10.1007/s10182-022-00456-6.

Analysis of styles of play according to season and end of season rank in the National Rugby League.

OBJECTIVES: This study aimed to identify styles of play in the National Rugby League (NRL) relative to season and end of season rank (position on the NRL ladder) across the 2015-2019 seasons. DESIGN: Retrospective, longitudinal analysis of performance indicators. METHODS: Forty-eight performance indicators (e.g. runs, tackles) from all NRL teams and matches during the 2015-2019 seasons (n=2010) were quantified. Principal component analysis (PCA) was then used to identify styles of play based on dimensions (Factors) of performance indicators. Multivariate analysis of covariance (MANCOVA) was then used to explain these emergent styles of play relative to 'season' and 'end of season rank'. RESULTS: The PCA revealed nine Factors (six attacking, two defensive and one contested style) accounting for ∼51% of seasonal team performance variance. These nine Factors differed across 'seasons', with four showing an effect against 'end of season rank'. From these four, two Factors (ball possession and player efforts) impacted upon the combined effects of 'season' and 'end of season rank'. CONCLUSIONS: The PCA identified nine Factors reflecting a spread of attacking, defensive and contested styles of play within the NRL. These styles differed relative to season and a team's end of season ranking. These results may assist practitioners with the recognition of more contemporary styles of play in the NRL, enabling the development of strategies to exploit competition trends.

Success Factors in the FIFA 2018 World Cup in Russia and FIFA 2014 World Cup in Brazil.

Research on success factors in football focusing on national teams is sparse. The current study examines the success factors during the World Cup 2018 in Russia and the World Cup 2014 in Brazil. A total of 128 matches were analyzed using a generalized order logit approach. Twenty-nine variables were identified from previous research. The results showed that defensive errors (p = 0.0220), goal efficiency (p = 0.0000), duel success (p = 0.0000), tackles success (p = 0.0100), shots from counterattacks (p = 0.0460), clearances (p = 0.0130), and crosses (p = 0.0160) have a significant influence on winning a match during those tournaments. Ball possession, distance, and market value of the teams had no influence on success. Overall, most of the critical success factors and those with the highest impact on winning close games were defensive actions. Moreover, the results suggest that direct play and pressing were more effective than ball possession play. The study contributes to a better understanding of success factors and can help to improve effectiveness of training, match preparation, and coaching.

Examining the evolution and classification of player position using performance indicators in the National Rugby League during the 2015-2019 seasons.

OBJECTIVES: This study aimed to: 1) examine recent seasonal changes in performance indicators for different National Rugby League (NRL) playing positions; and 2) determine the accuracy of performance indicators to classify and discriminate positional groups in the NRL. DESIGN: Retrospective, longitudinal analysis of individual performance metrics. METHODS: 48 performance indicators (e.g. passes, tackles) from all NRL games during the 2015-2019 seasons were collated for each player´s match-related performance. The following analyses were conducted with all data: (i) one-way ANOVA to identify seasonal changes in performance indicators; (ii) principal component analysis (PCA) to group performance indicators into factors; (iii) two-step cluster analysis to classify playing positions using the identified factors; and (iv) discriminant analysis to discriminate the identified playing positions. RESULTS: ANOVA showed significant differences in performance indicators across seasons (F=2.3-687.7; p=0-0.05; partial η2=0.00-0.075). PCA pooled all performance indicators and identified 14 factors that were included in the two-step cluster analysis (average silhouette=0.5) that identified six positional groups: forwards, 26.7%, adjustables, 17.2%, interchange, 23.2%, backs, 20.9%, interchange forwards, 5.5% and utility backs, 6.5%. Lastly, discriminant analysis revealed five discriminant functions that differentiated playing positions. CONCLUSIONS: Results indicated that player's performance demands across different playing positions did significantly change over recent seasons (2015-2019). Cluster analysis yielded a high-level of accuracy relative to playing position, identifying six clusters that best discriminated positional groups. Unsupervised analytical approaches may provide sports scientists and coaches with meaningful tools to evaluate player performance and future positional suitability in RL.

Non-metric multidimensional performance indicator scaling reveals seasonal and team dissimilarity within the National Rugby League.

OBJECTIVES: Analysing the dissimilarity of seasonal and team profiles within elite sport may reveal the evolutionary dynamics of game-play, while highlighting the similarity of individual team profiles. This study analysed seasonal and team dissimilarity within the National Rugby League (NRL) between the 2005 to 2016 seasons. DESIGN: Longitudinal. METHODS: Total seasonal values for 15 performance indicators were collected for every NRL team over the analysed period (n=190 observations). Non-metric multidimensional scaling was used to reveal seasonal and team dissimilarity. RESULTS: Compared to the 2005 to 2011 seasons, the 2012 to 2016 seasons were in a state of flux, with a relative dissimilarity in the positioning of team profiles on the ordination surface. There was an abrupt change in performance indicator characteristics following the 2012 season, with the 2014 season reflecting a large increase in the total count of 'all run metres' (d=1.21; 90% CI=0.56-1.83), 'kick return metres' (d=2.99; 90% CI=2.12-3.84) and decrease in 'missed tackles' (d=-2.43; 90% CI=-3.19 to -1.64) and 'tackle breaks' (d=-2.41; 90% CI=-3.17 to -1.62). Interpretation of team ordination plots showed that certain teams evolved in (dis)similar ways over the analysed period. CONCLUSIONS: It appears that NRL match-types evolved following the 2012 season and are in a current state of flux. The modification of coaching tactics and rule changes may have contributed to these observations. Coaches could use these results when designing prospective game strategies in the NRL.

Statistical analysis of frequencies of opponents׳ eliminations in Royal Rumble wrestling matches, 1988-2018.

The datasets and their analyses presented in this paper revealed some frequencies of opponents׳ eliminations by entrance or order of elimination in Royal Rumble wrestling matches from 1988 to 2018. The frequency of eliminations by the order of entrant is quite different from order of eliminations. Statistical methods, algorithms and machine learning methods can be applied to the raw data to obtain more hidden trend not included in this article.

Effects of Pacing Properties on Performance in Long-Distance Running.

This article focuses on the performance of runners in official races. Based on extensive public data from participants of races organized by the Boston Athletic Association, we demonstrate how different pacing profiles can affect the performance in a race. An athlete's pacing profile refers to the running speed at various stages of the race. We aim to provide practical, data-driven advice for professional as well as recreational runners. Our data collection covers 3 years of data made public by the race organizers, and primarily concerns the times at various intermediate points, giving an indication of the speed profile of the individual runner. We consider the 10 km, half marathon, and full marathon, leading to a data set of 120,472 race results. Although these data were not primarily recorded for scientific analysis, we demonstrate that valuable information can be gleaned from these substantial data about the right way to approach a running challenge. In this article, we focus on the role of race distance, gender, age, and the pacing profile. Since age is a crucial but complex determinant of performance, we first model the age effect in a gender- and distance-specific manner. We consider polynomials of high degree and use cross-validation to select models that are both accurate and of sufficient generalizability. After that, we perform clustering of the race profiles to identify the dominant pacing profiles that runners select. Finally, after having compensated for age influences, we apply a descriptive pattern mining approach to select reliable and informative aspects of pacing that most determine an optimal performance. The mining paradigm produces relatively simple and readable patterns, such that both professionals and amateurs can use the results to their benefit.