Over the last ten years, data scientist have chewed up professional baseball and spit out an almost entirely new game. However, also, basketball is the new game that data science and machine learning is changing completely.

The NBA has used statistics, that may even surpass that of Major League Baseball. We know they let data into the locker room first. We all saw Moneyball movie with Brad Pitt or even read the book. Almost every team in the NBA now has a data scientist on their team who work with coaches. The job is to help to scan players to maximize talents and identify undervalued players. Many players use wearables and IoT sleep monitors to track their medical status to avoid injury. “The NBA’s best team, the Golden State Warriors, depend on their analytics success. The league even run annual Hackathon to uncover new data analyst talent “, as said in Quartz.

“ Analytics are part and parcel of virtually everything we do now,” NBA commissioner Adam Silver.

  1. Three-point Analysis strategy

The most significant change that happened to the NBA caused by analytics, the rise of the three-point shot, as a result of simple math. In 2012, the average team took only 18.4 three-point shots per game, but In 2018 there was a 70% increase. The increased use of the three-pointer was mainly a result of the analysis. A three-pointer that has only a 35% chance of going in still led to more points in comparison to a two-point jump shot that is closer to the basket. So, coaches now encourage players with strong three-point shooting skills to shot as often as possible. We can mention, for example, Kevin Durant, Klay Thompson.

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Defense analysis strategy

The more sophisticated analysis led to the other enormous changes in basketball. Teams are now much stronger when we talk about evaluating defense. Data Scientist is capable with granular tracking data, to see which players are best at controlling the most efficient three-pointers and dunks shoots. When Data Scientist use Bayesian networks, he can discover how much better team’s overall defense is when a particular player is in the game.

As a result, we have the near-extinction of certain types of players. Basketball players who take a lot of inefficient two-point shots and don’t grade out as staunch defenders are not valuable any more for teams in NBA. We can also see why player Teodosic, originally from Serbia, didn’t have much success in the NBA. All players now need to be good teammates. They need to pass the ball to another player on the court that has an opportunity for a success point.

Now before evaluating the player, the coaches look down last year statistics for a particular player with Data Scientist. Now we see that different kinds of basketball players are most valuable, comparing to the past. The NBA data revolution has also changed how much “valuable” players spent their time playing. When data show that the player is at risk of injury, he will get days off. So, hard work is the past. We are now witnesses of the smart work revolution. Silver has said that teams are even testing saliva for signs of fatigue.

The resting strategy of star players has negativity outcome for some fans who paid for tickets at the start of the season. Why? They are stuck seeing a lineup of backups play. However, on another hand, this rest strategy will help players, because it prolongs careers. In other words, fans and league will be more satisfied in the long run. In conclusion, it will make overall gameplay better too.

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The NBA’s data revolution is creating rosters of more skilled, more well-rounded players, and who better rested when they do play.

The only thing that puts Golden State Warriors apart as the superstar NBA champions, even among the champions of the past — is DATA.

The 2018 NBA playoffs were not actually so competitive. The Warriors dominated their way to another NBA championship. However, the LeBron James-led Cleveland Cavaliers — could not give much of the fight.

The anti-climactic playoffs sparked much discussion among NBA players and fans about who is at fault for the rise of the “superstar.” team. The Warriors, based in San Francisco Bay Area, seem to stay as champion in the world’s top professional basketball league for the next half-decade. At this point they do not have data competition. If the Warriors gained an unfair advantage, you can’t blame Kevin Durant for it.

The real advantage they have amongst other NBA teams — even other past championship squads — is an excellent eye for new talent. They use data to see who is the best fit and who has the possibilities to grow during the season. They have been super successful in the NBA’s amateur draft — the annual event in which teams pick the next generation of players. The average share of draftees’ minutes played in the playoffs’ final round was 38.6%.

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However, simply highlighting the share of minutes played by draftees do not justice how well the Warriors have identified talent in the draft. Funny story is of the Golden State players who have played the most minutes over that period were among the top five picks in the draft.

Warriors’ sharpshooters Steph Curry and Klay Thompson were picked 7th in 2009 and 11th in 2011 respectively. These players have proved to better than nearly all of the players pick before them.

But, just drafting the right players is not the only reason for the Warriors’ enormous success. The team also has data innovative management and a robust team development infrastructure to help Curry, Thompson, and others reach peak levels. They have also been lucky. Moreover, they were only able to get Durant because of an unusual one-year jump in the amount teams could spend on players.

Still, excellent drafting is the foundation of their greatness. They identified and nurtured talent that others didn’t see.

Thank you for reading,

Yours Manja

If you are interested in basketball analytics you can drop me a message on Linkedin or Instagram.

Or send me email at manja.bogicevic[at]kageera.com