Sabermetric system (Coaching Edge, July 2019)

​Bias. Unconscious or not, it’s likely something we’re all guilty of. The way we look at people, we’ve already made assumptions and initial decisions on how they look, how they dress, what kind of attitude they have.
 
Not so long ago, bias had a huge role when baseball scouts selected players for their teams, including how they were valued during the transfer window. If a player wasn’t the right height, the wrong weight, or if a pitcher pitched a ball weirdly, if batters took walks instead of hitting home runs, the system more or less cut them out. Rumours also indicted hardline scouts that dismissed players if they didn't have ‘good-looking’ girlfriends, arguing they ‘had no self-confidence’.
 
The story of the Oakland Athletics and their general manager Billy Beane changed all that. Chronicled in the film Moneyball, the A’s were very much small fry compared to bigger sides like the New York Yankees and the Boston Red Sox. Ahead of their 2004 season, he knew he could not compete with the spending power of those heavyweights. A change was needed if they were to remain competitive.
 
He was persuaded to look into sabermetrics. First coined by baseball statistician Bill James, the term originates from the acronym for Society for American Baseball Research. When Beane was considering players, in order to secure wins, he learned he had to buy runs, and to buy runs he had to focus on players who were statistically more likely to get on-base. Beane analysed the cheapest players with the best on-base percentages and set out to create his new squad.
 
And despite not winning the Series, the A’s won 20 consecutive games, at the time the biggest winning streak in MLB history. Sabermetrics gave the A’s the chance to level the playing field by bringing in those unfairly undervalued players that were dismissed outright by the scouts.
 
Now, sabermetrics has found a home in many applications, including business and even healthcare. It’s a system that cuts through individual bias, championing hard data over assumptions, stereotypes and gut feelings, things that have held baseball back for much too long, symptomatic of an ageing yet still resistant old guard. Sabermetrics has changed the game - some have argued - into a less romanticised pursuit, but nevertheless, not long after the A’s achievement, baseball clubs up and down the US have been adopting versions of Beane’s model as their own.
 
Of course, this approach can’t be applied as usefully to all sports as it does to baseball, a sport where most plays revolve around the battle between batter and pitcher, and scoring runs is predicated entirely on batters getting on base.
 
At the grassroots level it can be difficult to implement sabermetrics in-game, where labour, time and technology might be restricted, and you’re analysing your tactics as a whole. But if you’re willing to put in the groundwork and explore further, you’re likely to find ways to enhance your own teams’ plays. Plays which create opportunities, and opportunities to score or win.
 
Start off small to find your feet; analyse players or games according to the parameters you’re looking for - whether average speed, goal clearances, shot positions, interceptions or completed passes. As long as you apply your measurements consistently across the board, you’ll have a good picture of what's happening at the macro level in games. This is the analysis that can yield patterns, and these patterns can highlight your team or player’s strengths and weaknesses.
 
Combine this with a camcorder setup and you can break the stats down even further off the field. There’s a few sports analytics companies that offer not just software help investigate plays, but action-tracking cameras that operate almost autonomously. And the more complete the scrutiny of past games, the better the indicator of future performance.
 
It’s important to say that sabermetrics isn’t going to revolutionise your team’s performance overnight, especially in continuous games where the variables are exponentially increased. Rather, it’s a data-driven tool that can offer you insight on how to apply your players where they are the best use, how to select the right players and how to maximise those play-making chances. It’s the science of winning.