Statistics is Even Changing Pro-Basketball
It wasn’t that long ago when the average person never heard of statistics or analytics, but today, nearly everything around us is driven in some way by statistics. In a nutshell, statistics is when raw numerical data is collected, organized, analyzed, and interpreted, usually with the aid of software applications and programming. Predictive analytics uses both new and historical data to predict outcomes.
Of course, there are many different methodologies and types of statistics, depending on what is being analyzed. Statistics are invaluable in the life sciences industry. Biostatisticians apply statistical methods to data collected from research, or clinical trials, in order to reach conclusions in the areas of medicine and public health, and their work is essential to getting new drugs to market.
Statistics and analytics can also be applied to sales and marketing, in order to help identify trends, and to evaluate the potential, or the success, of campaigns and programs. Casinos use statistics to figure odds in the games they operate in order to set payouts in a way that still keeps them profitable, and politicians use poll numbers and analytics to make decisions about their campaign strategies. In almost every line of work today, statistics enables companies to make better-informed business decisions.
Sports are no exception. In the world of sports, data has been applied to baseball for quite some time, but as we’re seeing over the last decade, basketball is rising to the top as the sport most affected by the use of analytics.
In the vintage days of the NBA (National Basketball Association), there was no such thing as a three-point line – all baskets (besides free throws) were worth two points. Now, thanks to simple math (3 > 2), the three-point shot is going up at an alarming, but efficient, rate. (See: Golden State Warriors, who have won three of the last four NBA titles.)
Sports injuries result in substantial economic loss by restricting participation, so it is no surprise that statistical analysis and medical technology are playing increasingly important roles in injury prevention and athletic performance. One of the most obvious changes in the NBA is the minutes restriction that star players are being held to, due to pre-existing injuries, or general caution. Millions of dollars are tied up in these gifted young athletes, and in most cases, a team’s season can go from being title contenders to middle of the pack, or even the bottom of the league, without these star players, so protecting them from injury is of great benefit and value.
By applying predictive analytics, sports injury risks, recovery times, and return-to-play strategies are now being mitigated to prevent re-injuries and greater financial loss. Although the average sports fan wouldn’t consider basketball a battle of physicality, in truth, the NBA schedule, with games on back-to-back nights in different cities, and occasional spans of four games in six nights, requires more grind than any other major sport. Another consideration is the extreme height of most professional basketball players, which leaves them more susceptible to ankle, foot, and knee injuries.
This 2018-2019 season, two players in particular, give us great examples of how decisions based on statistical data combined with the theory of probability, resulted in tight restrictions throughout the regular season, and therefore these athletes both were able to flourish in the playoffs.
Gordon Hayward, of the Boston Celtics, suffered one of the most gruesome injuries in professional sports history on the opening night of the 2017-18 season in Cleveland – his first ever game as a member of the team. Hayward suffered a dislocated ankle and fractured tibia, which caused him to miss the entire season. But even more devastating was the road to recovery – one in which the Celtics limited him to 25.9 minutes per game this season.
The results weren’t great, at first, since Hayward posted some of the lowest averages of his career. (Hayward himself said shortly after the injury, that he didn’t think he would ever be the same player he was before the injury.) Luckily, he plays for the talented Celtics team, and they were able to nudge him back into playing shape during the regular season.
Fortunately, the playoffs were a much different story. Even in the final nine games before the playoffs, Boston increased Hayward’s minutes a bit, to playing about 30 minutes per game, and we started to see glimpses of the player we remembered, before the devastating injury.
Similarly, Caris LeVert of the Brooklyn Nets (who wasn’t dealt nearly as bad of a hand as Hayward) found out the hard way, what the road to recovery (and limitations) is like. LeVert was playing at an All-Star level before he suffered a broken foot on Nov. 12, 2018, but the injury looked a lot worse that night. A lot of people around the game compared his injury to the severity of Hayward’s, because it looked that way at the time.
Miraculously, LeVert only suffered a dislocated foot, and had no major breaks or tears in the leg. Still, he would have to undergo a litany of tests before getting cleared to play, and even then, the Nets wanted to be cautious with such an important player, and part of their future. When LeVert was ready to return, similarly to Hayward, Brooklyn held LeVert to about 25 minutes per game. (Although, he did see as few as 15 minutes in his first game back, and as many as 42 on a following Saturday.) Over the last month of the season, LeVert regained that All-Star form, and the team couldn’t have been happier.
Years ago, players would have been rushed right back into the thick of the action without any sort of a rehab process. But today, thanks to the application of statistical analysis and medical technology, we’ve learned that restricting the number of minutes on the court for previously injured athletes is a good way to ease players back into the flow of action.
Interested in hearing more about Statisticians? Watch this 3 minute video!