Predictors of individual player performance in junior Australian football

2014 ◽  
Vol 18 ◽  
pp. e82
Author(s):  
C. Tangalos ◽  
S. Robertson ◽  
M. Spittle ◽  
P. Gastin
Keyword(s):  
Individual Player ◽  
Australian Football ◽  
Player Performance

scholarly journals Can Elite Australian Football Player’s Game Performance Be Predicted?

2021 ◽  
Vol 20 (1) ◽  
pp. 55-78
Author(s):  
J. Fahey-Gilmour ◽  
J. Heasman ◽  
B. Rogalski ◽  
B. Dawson ◽  
P. Peeling
Keyword(s):  
Model Performance ◽  
Test Set ◽  
Australian Football League ◽  
Team Selection ◽  
Individual Player ◽  
Australian Football ◽  
Team Characteristics ◽  
Using Data ◽  
Player Performance ◽  
Game Performance

Abstract In elite Australian football (AF) many studies have investigated individual player performance using a variety of outcomes (e.g. team selection, game running, game rating etc.), however, none have attempted to predict a player’s performance using combinations of pre-game factors. Therefore, our aim was to investigate the ability of commonly reported individual player and team characteristics to predict individual Australian Football League (AFL) player performance, as measured through the official AFL player rating (AFLPR) (Champion Data). A total of 158 variables were derived for players (n = 64) from one AFL team using data collected during the 2014-2019 AFL seasons. Various machine learning models were trained (cross-validation) on the 2014-2018 seasons, with the 2019 season used as an independent test set. Model performance, assessed using root mean square error (RMSE), varied (4.69-5.03 test set RMSE) but was generally poor when compared to a singular variable prediction (AFLPR pre-game rating: 4.72 test set RMSE). Variation in model performance (range RMSE: 0.14 excusing worst model) was low, indicating different approaches produced similar results, however, glmnet models were marginally superior (4.69 RMSE test set). This research highlights the limited utility of currently collected pre-game variables to predict week-to-week game performance more accurately than simple singular variable baseline models.

Predictors of Individual Player Match Performance in Junior Australian Football

2015 ◽  
Vol 10 (7) ◽  
pp. 853-859 ◽  
Author(s):  
Christie Tangalos ◽  
Samuel J. Robertson ◽  
Michael Spittle ◽  
Paul B. Gastin
Keyword(s):  
Skill Development ◽  
Vertical Jump ◽  
Video Recording ◽  
Likert Scale ◽  
Good Ability ◽  
Individual Player ◽  
Australian Football ◽  
Player Performance ◽  
Shuttle Run

Context:Player match statistics in junior Australian football (AF) are not well documented, and contributors to success are poorly understood. A clearer understanding of the relationships between fitness and skill in younger players participating at the foundation level of the performance pathway in AF has implications for the development of coaching priorities (eg, physical or technical).Purpose:To investigate the relationships between indices of fitness (speed, power, and endurance) and skill (coach rating) on player performance (disposals and effective disposals) in junior AF.Methods:Junior male AF players (N = 156, 10–15 y old) were recruited from 12 teams of a single amateur recreational AF club located in metropolitan Victoria. All players were tested for fitness (20-m sprint, vertical jump, 20-m shuttle run) and rated by their coach on a 6-point Likert scale for skill (within a team in comparison with their teammates). Player performance was assessed during a single match in which disposals and their effectiveness were coded from a video recording.Results:Coach rating of skill displayed the strongest correlations and, combined with 20-m shuttle test, showed a good ability to predict the number of both disposals and effective disposals. None of the skill or fitness attributes adequately explained the percentage of effective disposals. The influence of team did not meaningfully contribute to the performance of any of the models.Conclusions:Skill development should be considered a high priority by coaches in junior AF.

Classifying player positions in second-tier Australian football competitions using technical skill indicators

2021 ◽  
pp. 174795412110102
Author(s):  
Adrian J Barake ◽  
Heather Mitchell ◽  
Constantino Stavros ◽  
Mark F Stewart ◽  
Preety Srivastava
Keyword(s):  
Positional Information ◽  
Multinomial Logistic Model ◽  
Sports Science ◽  
Lower Tier ◽  
Australian Football League ◽  
Australian Football ◽  
Second Tier ◽  
Performance Statistics ◽  
Player Performance ◽  
Player Positions

Efficient recruitment to Australia’s most popular professional sporting competition, the Australian Football League (AFL), requires evaluators to assess athlete performances in many lower tier leagues that serve as pathways. These competitions and their games are frequent, widespread, and challenging to track. Therefore, independent, and reliable player performance statistics from these leagues are paramount. This data, however, is only meaningful to recruiters from AFL teams if accurate player positions are known, which was not the case for the competitions from which most players were recruited. This paper explains how this problem was recently solved, demonstrating a process of knowledge translation from academia to industry, that bridged an important gap between sports science, coaching and recruiting. Positional information which is only available from the AFL competition was used to benchmark and develop scientific classification methods using only predictor variables that are also measured in lower tier competitions. Specifically, a Multinomial Logistic model was constructed to allocate players into four primary positions, followed by a Binary Logit model for further refinement. This novel technique of using more complete data from top tier competitions to help fill informational deficiencies in lower leagues could be extended to other sports that face similar issues.

scholarly journals Biomechanical Loads and Their Effects on Player Performance in NCAA D-I Male Basketball Games

2021 ◽  
Vol 3 ◽  
Author(s):  
Sigrid B. H. Olthof ◽  
Tahmeed Tureen ◽  
Lam Tran ◽  
Benjamin Brennan ◽  
Blair Winograd ◽  
...  
Keyword(s):  
Movement Analysis ◽  
Individual Performance ◽  
Loss Probabilities ◽  
Point Field ◽  
Individual Player ◽  
Measurement Units ◽  
Player Performance ◽  
Game Performance ◽  
Level Training ◽  
And Training

Basketball games and training sessions are characterized by quick actions and many scoring attempts, which pose biomechanical loads on the bodies of the players. Inertial Measurement Units (IMUs) capture these biomechanical loads as PlayerLoad and Inertial Movement Analysis (IMA) and teams collect those data to monitor adaptations to training schedules. However, the association of biomechanical loads with game performance is a relatively unexplored area. The aims of the current study were to determine the statistical relations between biomechanical loads in games and training with game performance. Biomechanical training and game load measures and player-level and team-level game stats from one college basketball team of two seasons were included in the dataset. The training loads were obtained on the days before gameday. A three-step analysis pipeline modeled: (i) relations between team-level game stats and the win/loss probabilities of the team, (ii) associations between the player-level training and game loads and their game stats, and (iii) associations between player-level training loads and game loads. The results showed that offensive and defensive game stats increased the odds of winning, but several stats were subject to positional and individual performance variability. Further analyses, therefore, included total points [PTS], two-point field goals, and defensive rebounds (DEF REB) that were less subject to those influences. Increases in game loads were significantly associated with game stats. In addition, training loads significantly affected the game loads in the following game. In particular, increased loads 2 days before the game resulted in increased expected game loads. Those findings suggested that biomechanical loads were good predictors for game performance. Specifically, the game loads were good predictors for game stats, and training loads 2 days before gameday were good predictors for the expected game load. The current analyses accounted for the variation in loads of players and stats that enabled modeling the expected game performance for each individual. Coaches, trainers, and sports scientists can use these findings to further optimize training plans and possibly make in-game decisions for individual player performance.

Managerial Quality, Team Success, and Individual Player Performance in Major League Baseball

ILR Review ◽  
1993 ◽  
Vol 46 (3) ◽  
pp. 531-547 ◽  
Author(s):  
Lawrence M. Kahn
Keyword(s):  
Major League Baseball ◽  
Costs And Benefits ◽  
Team Success ◽  
Management Literature ◽  
Major League ◽  
Individual Player ◽  
Player Performance ◽  
The Impact ◽  
Managerial Quality

This paper uses 1969–87 major league baseball data to investigate the impact of managerial quality on team winning and individual player performance. Managerial quality and player performance are measured as predicted pay based on salary regressions; these market-based measures permit conclusions about costs and benefits of managerial quality. There are two major findings. First, when player inputs are controlled for, higher-quality managers lead to higher winning percentages. Second, players tend to play better, relative to their prior performance levels, the higher the manager's quality. These findings suggest that, as emphasized by the human resource management literature, the quality of management makes an important difference in the performance of organizations.

Effects of Domestic Air Travel on Technical and Tactical Performance and Recovery in Soccer

2014 ◽  
Vol 9 (3) ◽  
pp. 378-386 ◽  
Author(s):  
Peter Fowler ◽  
Rob Duffield ◽  
Joanna Vaile
Keyword(s):  
Performance Indicators ◽  
Study Data ◽  
Air Travel ◽  
Soccer Players ◽  
Psychological State ◽  
Sleep Quantity ◽  
Situational Variables ◽  
Before And After ◽  
Individual Player ◽  
Australian Football

The current study examined the effects of short-haul air travel on competition performance and subsequent recovery. Six male professional Australian football (soccer) players were recruited to participate in the study. Data were collected from 12 matches, which included 6 home and away matches against the same 4 teams. Together with the outcome of each match, data were obtained for team technical and tactical performance indicators and individual player-movement patterns. Furthermore, sleep quantity and quality, hydration, and perceptual fatigue were measured 2 days before, the day of, and 2 days after each match. More competition points were accumulated (P > .05, d = 1.10) and fewer goals were conceded (P > .05, d = 0.93) in home than in away matches. Furthermore, more shots on goal (P > .05, d = 1.17) and corners (P > .05, d = 1.45) and fewer opposition shots on goal (P > .05, d = 1.18) and corners (P < .05, d = 2.32) occurred, alongside reduced total distance covered (P > .05, d = 1.19) and low-intensity activity (P < .05, d = 2.25) during home than during away matches. However, while oxygen saturation was significantly lower during than before and after outbound and return travel (P < .01), equivocal differences in sleep quantity and quality, hydration, and perceptual fatigue were observed before and after competition away compared with home. These results suggest that, compared with short-haul air travel, factors including situational variables, territoriality, tactics, and athlete psychological state are more important in determining match outcome. Furthermore, despite the potential for disrupted recovery patterns, return travel did not impede player recovery or perceived readiness to train.

Factors Affecting Match Running Performance in Professional Australian Football

2017 ◽  
Vol 12 (9) ◽  
pp. 1199-1204 ◽  
Author(s):  
Samuel Ryan ◽  
Aaron J. Coutts ◽  
Joel Hocking ◽  
Thomas Kempton
Keyword(s):  
Aerobic Fitness ◽  
High Speed ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Time Trial ◽  
Related Factors ◽  
Running Performance ◽  
Total Distance ◽  
Individual Player ◽  
Australian Football

Purpose:To examine the influence of a range of individual player characteristics and match-related factors on activity profiles during professional Australian football matches. Methods:Global positioning system (GPS) profiles were collected from 34 professional Australian football players from the same club over 15 competition matches. GPS data were classified into relative total and high-speed running (HSR; >20 km/h) distances. Individual player aerobic fitness was determined from a 2-km time trial conducted during the preseason. Each match was classified according to match location, season phase, recovery length, opposition strength, and match outcome. The total number of stoppages during the match was obtained from a commercial statistics provider. A linear mixed model was constructed to examine the influence of player characteristics and match-related factors on both relative total and HSR outputs. Results:Player aerobic fitness had a large effect on relative total and HSR distances. Away matches and matches lost produced only small reductions in relative HSR distances, while the number of rotations also had a small positive effect. Matches won, more player rotations, and playing against strong opposition all resulted in small to moderate increases in relative total distance, while early season phase, increased number of stoppages, and away matches resulted in small to moderate reductions in relative total distance. Conclusions:There is a likely interplay of factors that influence running performance during Australian football matches. The results highlight the need to consider a variety of contextual factors when interpreting physical output from matches.

Factors Affecting Match Performance in Professional Australian Football

2014 ◽  
Vol 9 (3) ◽  
pp. 561-566 ◽  
Author(s):  
Courtney Sullivan ◽  
Johann C. Bilsborough ◽  
Michael Cianciosi ◽  
Joel Hocking ◽  
Justin T. Cordy ◽  
...  
Keyword(s):  
Physical Activity ◽  
Multiple Regression ◽  
Performance Characteristics ◽  
Stepwise Multiple Regression ◽  
Skill Performance ◽  
Australian Football ◽  
Activity Measures ◽  
Player Performance ◽  
Perception Of Performance ◽  
Peak Speed

Objectives:To determine the physical activity measures and skill-performance characteristics that contribute to coaches’ perception of performance and player performance rank in professional Australian Football (AF).Design:Prospective, longitudinal.Methods:Physical activity profiles were assessed via microtechnology (GPS and accelerometer) from 40 professional AF players from the same team during 15 Australian Football League games. Skill-performance measure and player-rank scores (Champion Data Rank) were provided by a commercial statistical provider. The physical-performance variables, skill involvements, and individual player performance scores were expressed relative to playing time for each quarter. A stepwise multiple regression was used to examine the contribution of physical activity and skill involvements to coaches’ perception of performance and player rank in AF.Results:Stepwise multiple-regression analysis revealed that 42.2% of the variance in coaches’ perception of a player’s performance could be explained by the skill-performance characteristics (player rank/min, effective kicks/min, pressure points/min, handballs/min, and running bounces/min), with a small contribution from physical activity measures (accelerations/min) (adjusted R2 = .422, F6,282 = 36.054, P < .001). Multiple regression also revealed that 66.4% of the adjusted variance in player rank could be explained by total disposals/min, effective kicks/min, pressure points/min, kick clangers/min, marks/min, speed (m/min), and peak speed (adjusted R2 = .664, F7,281 = 82.289, P < .001). Increased physical activity throughout a match (speed [m/min] β – 0.097 and peak speed β – 0.116) negatively affects player rank in AF.Conclusions:Skill performance rather than increased physical activity is more important to coaches’ perception of performance and player rank in professional AF.

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