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Next-Gen Moneyball: AI and Big Data Help the Raptors Claim Their First NBA Title

Last week, the Toronto Raptors won their first NBA Championship, beating the perennial powerhouse Golden State Warriors. While the Raptors had a slightly better regular season record, many considered them to be heavy underdogs against the star-studded 3-time champion Warriors. But the Raptors knew something many of us didn’t. In fact, they knew a lot of things many of us didn’t. And that’s a big reason why they won.

 

For those who have read the book or have seen the film “Moneyball”, you’re likely well aware how statistical analysis, scouting and team building have seen a monumental shift in the last 25 years. In basketball, APBRmetrics – like Sabermetrics in baseball – shifted the focus from traditional statistics like points-per-game to more advanced stats like estimated wins added (EWA), plus-minus rating and player efficiency rating (PER).

 

But still, teams have looked to get smarter. Advanced statistics only encapsulate one portion of a much larger picture, in a team sport where offensive and defensive systems, pace of play, team fit, and many other factors contribute to a player and team’s ultimate success.

 

The Toronto Raptors, led by GM and President of Basketball Operations Masai Ujiri, took player and team analytics a huge step further in 2016, when they started uncovering insights through the use of Big Data and AI. Their vision was to build a data-driven war room that could provide in-depth insight into their roster and instantly analyze the impact of potential player acquisitions, particularly in time-sensitive situations like minutes before the trade deadline or amidst the split-second chaos of the NBA Draft.

 

The system was designed to incorporate statistical data, advanced metrics, contractual data, social media sentiment, expert scouting evaluations and more in real time. The Raptors custom platform is a first-of-its kind analysis and visualization solution in the NBA, with interactive touchscreen tables and digital wall screens that allow members of the front office to collaborate and seamlessly share insights as they discuss potential transactions.

 

The Raptors debuted the new technology at the 2016 NBA Draft, where they landed star starting power forward Pascal Siakam with the 27th pick. According to stats website 82games.com, the chances of landing a star player with the 27th overall pick are only 5% – the lowest of any first round pick. In fact, only 1 in 4 players selected at #27 even becomes a meaningful role player.

 

This year, in his 3rd season, Siakam averaged a very, very solid 16.9 points per game and 6.9 rebounds. But more importantly, he finished 7th among power forwards in estimated wins added and 7th among power forwards in value added above a replacement-level player. All this on a $1,544,951 salary – good for 158th among NBA forwards this year. These are home-run “Moneyball” metrics, if they ever existed.

 

The technology, no doubt, played a key role in several other critical transactions, like the decision to take a chance on undrafted Wichita State guard Fred VanVleet after the 2016 draft, who was a key contributor in the Raptors’ championship run. Or the decision to retain aging free agent point guard Kyle Lowry when many disputed his value. And, of course, there was the risky decision to exchange star shooting guard DeMar DeRozan for eventual finals MVP Kawhi Leonard, who will become a free agent this offseason.

 

The moves with Lowry and Leonard were considered high-risk, high reward transactions. But the Raptors had more than the standard metrics and gut instinct on their side. By melding vast amounts of structured and unstructured data, the team was able to paint a much more complete picture of the potential effects of these transactions than even the most advanced statistics can offer, and they were rewarded handsomely for their efforts.

 

The power of harnessing Big Data is undeniable. For our clients, Catalyst’s Data Lake and Query Tool can help them see many of the same benefits the Raptors are enjoying.

 

The Data Lake houses all of your company’s structured and unstructured data, while Catalyst’s proprietary Query Tool lets you tap into all of that Big Data on demand, to create once-implausible analysis in a matter of moments.

 

The ability to layer in massive amounts of unstructured data can give you unprecedented insight into your operations. Integrating Nielsen Data or weather data with your own can allow you to create visualizations that help you understand once mysterious peaks and valleys in your profits. Integrating vast amounts of point-of-purchase data can lead to stunning insights that change the way you market and merchandise your items. Of course, these are just a few examples – with so many types of data available, the potential options are limitless.

 

“While many companies are collecting large amounts of data, far fewer have the ability to do anything useful with it without having to move mountains from an operational standpoint,” says MGMT3D Vice President Michael Steward. “We believe data provides the most value when everyone in the organization has the ability to tap into it, which is why we put so much focus on making Catalyst easy for anyone in the organization to use.”

 

In our opinion, the Toronto Raptors’ data-driven war room is a prime example of how big data and advanced analytics can be used as a powerful collaboration tool. By giving teams access to better information and a means to utilize it collaboratively, organizations empower those teams to have better-informed conversations about the impact of decisions. In a smarter world, that’s the formula for building a winner.

 

What do you think about the Raptors using big data to build their team? What do you think about empowering teams with data as a collaboration tool? We want to hear from you. Sound off on social media now and join the conversation.

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