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By Ryan Rose
In fantasy sports, participants put together a “dream team” based on predictive analytics, such as how this quarterback has performed statistically in the past three games or how that pitcher has interacted with left-handed batters historically. Similarly, horseracing fans take predictive analytics very seriously. Bets can be determined on such granular details as how that colt performs on mud versus turf.
For those not initiated with this admittedly geeky pastime, here’s a basic breakdown of how fantasy sports work: Essentially, it’s an online game in which participants assemble imaginary or visual teams comprised of real players of real teams. These teams compete based on the statistical performance of those players in real games. This performance is then converted into points that are compiled and totaled according to a roster selected by the fantasy team’s manager.
I’ve never liked football, but I’m crazy about fantasy football. And that’s because I love statistics and especially, analytics.
Essentially, when it comes to fantasy sports, you’re looking to create the best team you can create. It used to be done all on graph paper, but these days, it's digital. In a way, it’s like dungeons and dragons but for jocks.
Computers can help you put together these all-start fantasy teams. If we’re talking football, then you pick a wide receiver, a quarterback, etc. – someone to represent all of the positions you’d have on an IRL (in real life) team. The same goes for any other fantasy sport.
It all comes down to looking at the predictive analytics of each player to determine how they would perform in a given setting.
So how does this translate to the workplace?
It might sound strange, but when it comes to putting together strong teams of engaged employees, a similar strategy can be used.
In the same way that you would look for the best running back or shortstop, now you’re looking at the stats around each expert in each subject matter. Basically, you’re drafting/building a team of knowledge experts.
Traditionally, many learning professionals in corporate settings have looked to intuition or hearsay to identify individuals as experts. In fact, many organizations struggle when trying to accurately identify skills strengths and gaps in their teams because these decisions are often gut-driven rather than based in analytics. What’s more, many
organizations don’t know how or where to pull the data needed to create the most successful teams possible.
Analytics are the opposite of the gut reaction. There is data everywhere to pull insights from – it’s just a matter of harnessing it. For most organizations, this doesn’t require adding additional tools or software; typically, the data is there but it’s either not being used or not used effectively.
Analytics can be pulled from formal training records, such as learning management systems, formal classes the employees’ have taken, certifications and degrees they might hold, etc. There’s also analytics to be pulled from individuals’ social learning activities – which encompass activities such as blogs they read and interact with, social media activity, and online collaboration tools. With a system in place, data from all of these can be tracked, collected and analyzed in a much more effective manner than say, giving people written assessments to fill out.
Peer assessments, be they formal or informal, also are a key asset. The trick is to ensure there is a unified place where all of this information can be pulled together into once place, where that data can then be weighed against the actual work being done.
Once this information is evaluated, learning leaders can create teams of varied skill sets and levels of expertise to optimize performance. It’s also important to keep teams balanced as things change – an employee leaves the company, gets promoted, for instance. When you have all of the data available in one easy-to-view spot, that makes it easy to ensure you’re keeping the balance even when the roster changes.
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