Analytics haven’t always had a great relationship with sports as evidenced by the initial breakthrough in baseball, which was met with scepticism by the old guard, who insisted that statistics should not be used in a sporting environment.
However, the sporting world has changed significantly since those days and statistics are now a key cog in the setup of teams and organisations across the globe. Analysis as a tool is now entrenched in online betting, particularly in the UK and Ireland, where artificial intelligence (AI) is already being used to aid punters in their bid to join horse racing community pundits with successful bets on the sport.
Beth uses key criteria from the sport about a particular race meet, analysing the form, pedigree, weather, grounding and the racecourse in question before surmising the competitor with the best chance of winning the race. The AI system ensures that it gathers vital statistics regarding the quality of the horse before issuing its guidance for bettors. The punter can then choose whether to back the horse or to avoid it.
The use of AI in online betting raises the question as to whether professional sports teams could opt to deploy the same technology, essentially taking decisions out of the hands of their coaching and medical staff. It’s clear that some sports teams are eager to adopt these new ways as already, certain basketball teams have used statistical and analyticalinformation to monitor the fitness of their players over the course of the season. An 82-game campaign places a high demand on the body, especially for older players. It was evident for the San Antonio Spurs as, in the latter days of Tim Duncan and Tony Parker’s careers, that they were spared back-to-back games or at the least their minutes.
Coaches will undoubtedly have their own preferences as to whether they can trust their judgements regarding the fitness of their players, especially when their job could be on the line in their bid to reach the post-season. If a team were to thrust straight into a data-first policy utilising AI, it would be interesting to see how the system would work regarding the coach’s say over selection. The time may come sooner than we think, although there will inevitably be resistance.
The Oakland Athletics are an example of a team that committed to a philosophy regarding data, using analytics to select players for their roster in order to improve their fortunes on the field and financially to keep pace with their rivals in the MLB. Although it didn’t result in the ultimate success in a World Series, their model has since been emulated by teams across the league.
The Cleveland Browns attempted to bring the same system to the NFL, with Sashi Brown at the forefront. The Browns endured two years of embarrassment, but the long-term results of their approach are evident as the franchise are now a contender for the Super Bowl are years of struggle.
The Houston Rockets brought an analytical approach to the NBA, using scoring metrics to focus on three-pointers which benefitted James Harden and Chris Paul. Again it was not successful in delivering a championship even though the Rockets were extremely competitive in the Western Conference.
Teams are now looking for the next way to get ahead of the curve and AI could be the way forward if an outfit is bold enough.