When Charles Reep’s primitive analysis of 1950s soccer came to the (incorrect) conclusion that soccer teams were more likely to score from plays of three passes of fewer, he set in motion something that is still growing today. Reep’s findings, though fundamentally flawed, raised the notion that individual metrics - Reep focused on chance conversion - could be isolated and improved on, and that the effect of their improvement could be measured.
Fast forward 60 years, and sports analytics is exploding. Through decades of scepticism, traditionalists delighted as innovators floundered, and an industry-wide opinion that gut instinct was as or more valuable than number-crunching prevailed. Analytics has now seen widespread adoption, though, and you won’t find a top level soccer, football, baseball or basketball team without a dedicated analytics division working to find areas in which a decisive extra 1% could be squeezed. The Oakland As’ well documented, data driven successes is a high-profile and in many ways anomalous example, but sports analytics teams work daily to inform decision-makers in institutions across all major sports.
And the sports analytics market has grown in line with the industry’s appreciation of its uses, a trend set to continue into the next decade. The global market is expected to balloon, from $123.7 million this year to $616.7 million by 2021 - one of the more conservative estimates - at a Compound Annual Growth Rate (CAGR) of 37.9%. In terms of the regions expected to hold the largest market shares, North America dominates thanks to ‘the dynamic market environment and higher technological adoption,’ according to Business Wire. Europe follows, while Asia-Pacific (APAC) is expected to see the highest CAGR in the period, thanks largely to an emerging soccer market that is seeing incredible investment.
Data and analytics can influence everything from player acquisition to ticket pricing, but analytics teams are currently dealing with over-saturation - the idea that all data could potentially be useful has sports teams mining everything they possibly can for information. Companies that offer solutions to the hordes of data collected across sports will profit as the identification of the data points that are actually relevant to coaching staff and decision-makers is one of sports analytics’ key challenges.
Where predictive analytics has had a major impact is in injury prevention. A player’s biometric data is collected by teams and analyzed by the likes of Kitman Labs - the first company to use machine learning to evaluate injury risk - who present injury risk alerts specific to each athlete. A coach can now pull out their smartphone and quickly check, via an app, which of their players is in need of a rest or is at risk of picking up an injury. This kind of information was simply not available to coaches before the analytics boom and, according to Wired, ‘a three-year trial has shown that Kitman’s athlete management system can reduce injuries by 30% using known injury indicators.’
As the market grows, its applications will diversify, and developments in sports analytics are by no means limited to athlete fitness. As each sports team looks for the marginal gains that can be the difference between winning or losing, those who find ways to best utilize their data will reap the rewards. Sports have always been, at their core, numbers games - the explosion of data collection only saw these numbers multiply and diversify. As sports teams learn to harness and extract value from their data for both competitive and commercial gain, the market surrounding that process will continue to grow its already mouthwatering valuation.