Over the past two decades, sport has been revolutionised by the use of advanced statistics. In the 2003 book ‘Moneyball: The art of winning an unfair game', and the film adaptation, manager Billy Beane defies convention and focuses on numbers, rather than feelings, to assemble a successful team on a shoestring budget. Since then, sporting teams have adopted statistical methods to gain a critical edge, but businesses have been slower to identify the transferable benefits. Indeed, for some business managers, it is quicker and easier to find out how well their sports team is performing than the team that they manage.

To break this down, we can look at a much-loved sport of mine – ice hockey. Ice hockey is fast, varied and physical, combining skill, grit, and of course, the occasional fight. There are a lot of different game scenarios, and also a lot of luck involved, which makes it interesting from an analytical perspective. Teams have a rigidly-applied salary cap, so determining a player's long-term underlying value is critical when trading players and signing contracts.

So how do you determine the good from the lucky? Let's take a look at the best goal-scorers for the 2018-2019 season. Alex Ovechkin is the best goal-scorer in the NHL today, so it's no surprise that he's at the top of the charts with 49 goals in 76 games. Second is Leon Draisaitl, with 46 goals in 76 games. Does this mean that 'Drai' is almost as good as 'Ovi'? Well, no. But why?

Draisaitl spends more time on the ice per game, so his production per 60 minutes is relatively lower than Ovechkin. In addition, Draisaitl has a shooting percentage of 21.9% this season, compared to Ovechkin at 15.7%. While both are ahead of their career average, Draisaitl's previous high for a season was 16.9%, far higher than this term. And using ‘PDO’, an advanced stat that measures luck, our man in second place is at 102 vs 96.7 last season, whereas Ovi is at 102.9, but he has consistently exceeded 100 for 11 of his 12 seasons in the National Hockey League (NHL).

Still with me?! The point is that whilst both players have scored around fifty goals this season, it is far more likely that this feat will be repeated next season by Ovechkin than by Draisaitl as he has a history of consistently high performance. So how do we transfer this to business? The question is, how do you differentiate between employee and business results that are outliers versus those that are expected?

First you need to know which stats are important, and disregard those that are misleading. To use another ice hockey example, John Gibson is the best performing goaltender in the NHL this season, but you wouldn't know it from a cursory glance at his team, who have the second worst goal difference in the whole league. Gibson is doing his best, but he's been let down by the rest of the team; a problem that is as common in business as it is in sport. Looking at the detail can identify areas and people who are performing well in a challenging environment.

Second, you need to know how to gather this data; where it resides and how you can perform analytics on it. If you don’t have that data right now, how do you go about collecting it, and how long will it take to build up enough data to have a useful dataset? And once you've done all that work, how do you consume that data?

Different people like to view data in different ways, to get the most out of it for their purposes. Going back to ice hockey again, while an analyst will be perfectly at home with these stats for Alex Ovechkin:

An offensive coach might prefer to see this: 

And the head coach might want to look at something like this: 

From Sean Tierney aka @chartinghockey

How you display data is critical to the success of your organisation, depending on who the audience is, so consider who will be making decisions based on the data you present. But bear in mind that data for its own sake does not benefit the business, so be selective. Insights that drive actions are vital in the intelligent use of data, and with a range of tools available, the possibilities are vast. With the volumes of data growing all the time, finding the right insights and showing them to the right people at the right time is critical. As an example, while storage metrics are important to collect, you don't want to see every cache miss on the wall of the operations centre. But if that array goes offline, you'll want more than a log entry. There is a balance to strike and careful consideration to take when assessing the data to include, alongside who is viewing it.

Adopting the intelligent use of data across your organisation will not replace all the other factors that make you successful, but as Billy Beane found out, it can help you gain a competitive edge over your rivals.

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