Los tradicionalistas de la industria del deporte se resisten al análisis de datos. ¿Cómo puedes convencerlos de su valor?
Incluso los veteranos más experimentados de la industria del deporte pueden ver la luz cuando se les muestra cómo el análisis de datos mejora la toma de decisiones. Para cerrar la brecha:
- Demostrar éxitos pasados. Utilice estudios de casos en los que el análisis de datos mejoró significativamente el rendimiento del equipo.
- Conéctate en un terreno común. Destaque cómo la analítica apoya los métodos tradicionales en lugar de sustituirlos.
- Ofrece experiencias prácticas. Permita que los escépticos interactúen con las herramientas analíticas para comprender sus beneficios prácticos.
¿Cómo ha integrado con éxito el análisis de datos en un entorno tradicional?
Los tradicionalistas de la industria del deporte se resisten al análisis de datos. ¿Cómo puedes convencerlos de su valor?
Incluso los veteranos más experimentados de la industria del deporte pueden ver la luz cuando se les muestra cómo el análisis de datos mejora la toma de decisiones. Para cerrar la brecha:
- Demostrar éxitos pasados. Utilice estudios de casos en los que el análisis de datos mejoró significativamente el rendimiento del equipo.
- Conéctate en un terreno común. Destaque cómo la analítica apoya los métodos tradicionales en lugar de sustituirlos.
- Ofrece experiencias prácticas. Permita que los escépticos interactúen con las herramientas analíticas para comprender sus beneficios prácticos.
¿Cómo ha integrado con éxito el análisis de datos en un entorno tradicional?
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As a CEO who has worked in professional sport for over 30 years. I see this question as click bait, I do not know a single coach, player or person in The industry who doesn’t want to use data to improve. Sport measures as much as possible to try and improve, no need for convincing.
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Nishant Joshi(editado)
With some learning and 16 years of experience in Sports & fitness industry from a trainer to a CBO I can say -Traditionally also sports performances were data driven but scale and means were different. With adoption of technology at such a fast speed the traditionalist are facing allignement issue. The are not resistant but need better understanding. This adoption can be increased by 1) Educating them the latest technology. 2) By giving realistic examples and showcasing result achieved with ease through data analysis. 3) By making easier and user friendly dashboards so that any body can take advantage of data. Data is a form of knowledge these days the more we teach them more traditionalists will use it.
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While traditionalists believe that outcomes in sports cannot be predicted to any degree, latest use of data analytics has proven otherwise. It is still not a 100% predictable, but isn’t that the beauty of sports? Traditionalists should see data as an enabler for better strategy and preparation as far as athletes, coaches and teams are concerned, making each game more and more competitive and in turn increasing the quality of the sport.
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Traditionalist is an interesting term. At sixty involved in both amateur and professional sport for over fifty years as a player, coach, DOR and CEO I am traditional with massive data value. My aim was always to put athletes first, in a safe environment with a culture of acceptance and education, fine tuned to assist athletes and stakeholders to meet their goals and objectives through their participation. I can’t speak for all ‘traditionalists’ but I have always used whatever data was available and if it was not available have gathered it myself. If data is user friendly and put into terms I understand and can employ I would happily use it. Don’t write us off because we don’t use buzz words or because we don’t immediately see value.
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As soon as someone says, "We have always done it this way." I lose interest. Invest in people who have a growth mindset and let the jaws of the traditionalists gape in the distance.
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Integrating data analysis into traditional environments requires a strategic and adaptable approach. In my experience, the key is often starting with data that supports existing opinions rather than contradicting them. This builds trust with stakeholders, showing them how data can complement their insights instead of challenging their expertise. For example, at Orlando City Soccer Club, we initially used analytics to confirm coaches' observations about player fatigue, which led to quicker buy-in. Once trust was established, we gradually expanded the scope, introducing more advanced metrics like load monitoring and injury prevention strategies, which yielded measurable improvements.
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what's more in France politicians and local authorities get involved in sport and don't leave room for the private sector for fear of losing voter power through associations and clubs (you only have to see how many clubs in France, rugby, football, basketball, handball have their own halls or stadiums...) and sport in France is only supported by volunteering, which is fabulous, but volunteering is not very professional at the same time.
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Successful integration of data analytics in traditional context is not that easy as it seems whereby the following is the case. The key factors driving success in such environment are trust building and the ability to show measurable added value. Stakeholders should be engaged from the start of the project, followers should be given examples of what can be achieved rapidly, and training should be continuous. Slow adoption makes it easier to adapt to the culture of the nation or region in question. Remember the aim is not to substitute people's skills, but add value to those skills. This is not an easy process and may take some time; however, patience and perseverance in the process will do lots of magic.
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