How we use data to think in multiple time scales
(Wie wir Daten nutzen, um in mehreren Zeitskalen zu denken)
Great companies strike the correct balance between the short term (the necessity for success now) and the long term (ensuring that success is sustained in the future). This is also true for great sports teams. In sport, the critical determinant to both short and long term success is the availability of appropriate (quantity and quality) athlete talent. Unlike `professional` sports, the Great Britain Cycling Team (GBCT) is not able to `buy talent`. Therefore medal competitiveness at World Championships (annual) and Olympic Games (quadrennial) is often dependent upon a decision made 4 to 8 years prior to the event. The intention of this session is to provide insight into how GBCT uses data to make athlete talent decisions (rider retention/promotion/removal) with these multiple time scales in play. The content will focus predominantly at the `Academy` stage and will brought to life via real world examples.
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| Schlagworte: | |
|---|---|
| Notationen: | Ausdauersportarten Leitung und Organisation Naturwissenschaften und Technik Nachwuchssport |
| Tagging: | Big Data |
| Sprache: | Englisch |
| Veröffentlicht: |
o. O.
innovation enterprise -on demand-
2016
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| Online-Zugang: | https://ieondemand.com/presentations/how-we-use-data-to-think-in-multiple-time-scales |
| Dokumentenarten: | Video |
| Level: | mittel |