Talk about sports analytics to a traditional coach and he will rubbish it with a frown. For most coaches, it’s a concept that needlessly complicates matters which can be better sorted with sporting acumen gathered through experience and intuition. At a time when most decisions are being taken on analytics based predictions, distrusting the predictability of numbers over the unpredictability of human nature in sports is like living in a state of self-denial. Whether they accept or not, the fact remains that sports is gradually evolving into a game of numbers and equations. Get the stats right and you stand a big chance of winning the game. In fact, it wouldn’t be wrong to presume, that analytics will soon become the future for sport.
How Coaches Will Make the Most of Sports Analytics
At the MIT Sloan Sports Analytics Conference, technologist Ray Hensberger showed how analytics can be used to build baseball decision-making models such as what a pitcher is going to throw—and when. He started with a big list of 900 pitchers and based on a set of comprehensive parameters whittled it down to 400. The basis of exclusion included factors such as those having thrown less than 1,000 pitches total over the three seasons, ball-strike count, pitch type, game situation, right-handed batter versus left-handed batter, zone history etc. Every pitcher-specific model was made to go through five stages of cross-validation testing to provide insights on how the model would behave to real-time pitches. The end result was a set of pitcher-customized models, with nearly 75% predictability across all pitchers in real game situation.
A look at how the US women’s cycling team used analytics to rise from underdogs to silver medalists at the 2012 London Olympic Games shows how sports analytics can also be used to identify and check off-the-field factors (such as diet, sleep patterns, and training rigours) to optimize on-field performance. Of the many factors, it was found that one cyclist – Jenny Reed – met expectations level during training if she slept at a lower temperature the night before. So she was provided with a temperature water-cooled mattress for a sound overnight sleep. This means sports analytics can also be used to tailor customized programs to get the best out of every team member. Likewise when the on-field data is combined with various human variables such as fatigue, stress, etc. it is also possible to determine the hidden trends that lead to injury. So, sports analytics not only make athletes play at their best, but also keep them at better physical condition.
A yet another important use of sports analytics is to place values on each player based on individual games and situations. While it’s not unusual to predict a football or basketball player’s performance over a season with the help of stats, analytics can take it a step further and break it down to every pass thrown the player’s way and the percent of times the player was effective in utilizing the passes. This insight can be used to place a player in the right position to derive optimal advantage for the team. Putting such insights to use in real-time situations can help any team come out with the best.
What is truly exciting is that it’s just the beginning. As technology keeps advancing – from portable to wearable tech – analytics will begin to a play a more dominant, and perhaps, the all important role in sports.