Why Your Job Spec is Wrong

Let's do the maths. Stats Perform, the largest sports data company globally, employs approximately 3,200 people. Genius Sports has around 1,500. Second Spectrum had 42 before acquisition. Add Hawk-Eye, Catapult, Hudl, and every club analytics department worldwide. The total population of people with "sports technology experience" is finite and countable.

Now consider a job specification requiring "5 years of computer vision experience in sports." How many people globally have that specific background? Generous estimates suggest 50-100. Most are employed and not looking. Your job posting competes for perhaps 10 realistic candidates.

The alternative is hiring for adjacent skills. Broadcast engineers understand live production pressure. Gaming developers know real-time rendering. Sports science graduates have domain context. Data engineers from streaming companies understand scale. None have "sports CV experience" but all bring transferable capabilities.

The skill mapping exercise matters. What does "computer vision experience in sports" actually require? Algorithm development (transferable from any CV role). Stadium deployment (similar to broadcast engineering). Real-time processing (common in streaming). Sports context (can be learned). Only the combination is rare, not the individual components.

For hiring managers, this means rewriting job specifications around capabilities, not credentials. Instead of "5 years sports CV experience," try "experience deploying computer vision systems in live environments, with demonstrated interest in sports applications." The candidate pool expands dramatically.

Onboarding investment matters more than perfect hiring. A strong CV engineer can learn sports context in 6-12 months with proper mentorship. A sports professional can develop technical skills with structured training. The organisation that invests in development accesses larger talent pools than one insisting on ready-made candidates.