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ABSTRACT
Knowing where sports talent is produced is fundamental to identifying spatial factors in talent production and to assist with talent identification. This research develops spatial analysis methods—the ‘Talent Tracker’—to harness geographical information systems and identify regions that are over- and under-producing sports talent. The approach focuses on the professional Australian Football League (AFL), using data sourced from the AFL itself and other sources, to identify differences in regional ‘talent yield’ based on junior participation. Data from AFL draft records, informants and secondary sources identified the place of junior talent development for the 1290 players who were drafted and played at least one game of senior AFL football in the period 1997–2010. AFL national census data identified junior participation for 94 specified regions for the period 2002–09. AFL talent was assigned to these regions by using ArcGIS procedures, and the datasets synthesised to produce tables and maps of talent yield by participation for each of the 94 AFL regions. The results demonstrate the power of contemporary spatial analysis to open up new research methods for studies into sports talent production and identification. Clear spatial patterns emerge in talent yield at both the national and metropolitan scales. The results provide numerous avenues for further research to explore determining factors for the spatial patterns identified.
Acknowledgements
This work was initially funded by a small grant provided by the AFL and Griffith University, and later an Australian Research Council Linkage Project (LP 221008) exploring a much broader range of factors, which is supported by the Australian Sports Commission, the Australian Institute of Sport, the AFL, Cricket Australia and Tennis Australia. Talent Tracker would not have been possible without the support of the AFL and a number of key officers and informants, and we are particularly indebted to the many AFL historians, AFL officials and others who helped complete the database of players by place of junior production. Significant assistance was provided by Street Ryan and Associates, who were generous in sharing key data. Sincere thanks are given to our colleagues Dwight Zakus, Murray Bird, Kristine Toohey and other investigators on the ARC grant underpinning the work. Special thanks to Emily Hatfield who provided tireless assistance in gathering the data and preparing much of the early GIS analysis and Lavinia Poruschi who completed it. Thanks also to Rick Evans, Maryam Shobeirinejad and Joshua Skinner for their GIS support. The views expressed are solely those of the authors and do not represent the views of any institution. The authors take full responsibility for all errors and omissions.
Notes
2. Given a set of weighted data points, the Getis-Ord Gi statistic identifies those clusters (hot-spots) of points with values higher in magnitude than one might expect to find by random chance.