NHL Edge IQ | Opportunity Analysis

How epic was that shot? Opportunity Analysis brings data to the debate

Learn about the science behind the brand-new NHL EDGE IQ stat that debuted in April 2023.

• An exclusive look from Brant Berglund, NHL Senior Director of Coaching and General Manager Applications, as he details the science behind Opportunity Analysis and how this helps analyze the play on the ice.

  Every week of the National Hockey League (NHL) season, fans see TV rankings of the best plays of the week, and every week, fans debate those rankings. Most people agree that a great shot is one that had a low probability of success, and a great save is one that stopped a shot with a high probability of success. But what were those probabilities, really?

  A new NHL EDGE IQ metric powered by Amazon Web Services (AWS) lends more fodder to these and other debates and promises new insights across the sport. That metric, Opportunity Analysis, determines how difficult a shot is based on a number of different factors, using a combination of historical and real-time data. 

  During live games, Opportunity Analysis uses data from the NHL EDGE Puck and Player Tracking system, up to the moment of release on every shot, to measure the factors most critical to the play. 

  "Opportunity Analysis is the first comprehensive and rigorous analysis that can be used in near real time to understand the shot setup, opportunity, and circumstances around the development of a shot," says Leon Li, AWS principal cloud architect.

  The metric could be the genesis of new, more data-driven fan debates — a development the NHL welcomes as it seeks ways to make the game more accessible to fans.

  “We're going to be able to use this metric as a tool for fans and broadcasters to help foster understanding and enable them to formulate their own theories,” explained Brant Berglund, NHL senior director of coaching and general manager applications. “It's not about giving people the answer. It's about relying on the accuracy of the data, removing as much of the subjective as possible, and empowering people to assess the data and make their own decisions. We're excited to hear people debate the data — the discussion is the best part.”

  Opportunity Analysis assesses the factors that make up a shot, providing an output ranking of high, medium, or low, with "high" being the greatest chance of the shot resulting in a goal. The factors include elements such as the angle of the shooter, proximity to the goal, and how much distance the goalie had to cover to block the attempt. 

  Opportunity Analysis distills an unprecedented amount of data — dozens of factors, many tracked with sub-second latency — into one comprehensive metric. 

  “We were able to look at so many factors through the volume of real-time NHL EDGE Puck and Player Tracking data available over the course of the season. That's the comprehensive aspect of it," Li says. "The rigorous aspect of it is us, as data scientists, working with NHL's technology and hockey experts and data engineers to vet the accuracy of the data and generate features that make sense in the context of the game."

  Opportunity Analysis is the latest metric to emerge from the in NHL's ongoing effort to develop unique data sources and analytic techniques to help break down the intricacies of the sport. Over the past 15 years, the NHL has implemented the Hockey Information and Tracking System (HITS) as the official scoring and events data platform, and most recently launched NHL EDGE Puck and Player Tracking technology. That system, which is installed in all 32 NHL venues, includes infrared emitters and cameras that track sensors embedded within the puck and the sweaters of every player.  

  In 2021, NHL and AWS began collaborating to make the most of these data sources. In 2022, Face-Off Probability — the first AI/ML-driven NHL analytic — launched within the NHL EDGE IQ platform, helping determine who is most likely to win a specific face-off based on multiple historic and in-game data points. This built upon the foundation of Shot and Save Analytics, two advanced stats that offer an in-depth look at a team or player's scoring performance and a goalie's save performance, respectively.

  The layers of data associated with Opportunity Analysis are a gold mine for fans, broadcasters, and the League alike, according to Berglund. This innovative metric reveals not only the difficulty level of a given shot, but insights such as how fast the puck was traveling, the goalie's height, the shooter's change in angle, and others.

  "With this product, we’re going to be able to output massive amounts of data on the play leading up to every shot, curated in very close to real time," Berglund says. "That's even more valuable than the rating in many ways — that we're going to actually output that much, that our talented broadcasters have that at their fingertips to talk about during the game, and that fans will have access via those channels, too."

  Opportunity Analysis attempts to answer the common lament — “How good was that scoring chance?!” — with a data-driven approach. Just how tough was the situation generating the shot, from a historical perspective? What, exactly, made the shot a near impossibility, a sure thing, or something in between?

  NHL and AWS trained a machine learning model to rate the likelihood that certain combinations of circumstances around a shot would result in a goal.

  "We wanted to be open-minded and preserve the possibility that the data could challenge conventional logic about scoring opportunities." Berglund says. "Sometimes it did, sometimes it didn't.

  For example, Opportunity Analysis verifies the intuition that, on average, shots closer to the net have a better chance of going in than shots from farther away. But other factors are more subtle. While it's still too early to say why or how much, the data have revealed an association between scoring rates/projected goal rates and where the puck passes the blue line before a shot.

  "The beauty of this project is that it's forcing all stakeholders to use data to think about the game in different ways," Berglund says. "And hopefully, consumers will, too."

  AWS's processing power and cloud infrastructure made it possible for the NHL team to approach its data in ways it couldn't before. The security and scalability of AWS SageMaker "allowed the NHL to trust AWS with very valuable, comprehensive data and allowed us to quickly iterate and develop the model," Li explains.

  AWS Kinesis made it possible to capture and process live game action, including snapshots of time that occur around a given shot. Kinesis sends the information to the model in SageMaker, which then returns a high, medium, or low rating and the top contributing factors that can be routed to analysts for integration in broadcast analysis.

  "That real-time aspect is very important for us," Li says. "So is the scalability, given that the NHL is generating thousands of records per second, and multiple games can be happening in parallel."

  Berglund expects that, as the NHL dives further into the key factors of shots’ likelihood of success, other features that could illuminate the sport will emerge. After all, with so many ways to engage beyond the game itself, including second-screen experiences, no one is a casual fan anymore. More access and features will mean more ways for fans — and everyone involved in the sport — to unpack the action and formulate their own theories about what makes a successful player or team.

Opportunity Analysis gives new insight into the quality of a shot

This brand new analytic, part of NHL EDGE IQ powered by AWS, uses a novel ML model that analyzes millions of historical and real-time data points to show fans how difficult a shot was at the moment of release, calculated just seconds after the play. 

• Go under the ice with the AWS Pro Serve team to learn how they used a novel ML model that analyzes millions of historical and real-time data points to show fans how difficult a shot was at the moment of release, calculated just seconds after the play.

  Imagine you’re watching your favorite team battle it out against a divisional rival. Nobody has scored yet. Suddenly, in the middle of a fierce battle, the rookie left wing steals the puck and turns the play in the other direction. Willing him to generate an opportunity to score, you’re ticking the boxes in your mind of what does he need to do to increase his chances of beating the goalie: shot location, puck movement, goalie positioning, traffic on the ice and rush of the play. Whether you're new to the sport or have been die-hard hockey fan for your entire life, there's never been a way to figure out for sure what will happen next. You hold your breath as every piece of the play falls into place. He shoots—he misses. What looked like a guaranteed goal was, somehow, not even close. Would a more experienced player have scored? Or was the shot deceptively impossible? If only there was some way to know. 

  Now, there is. The NHL has tapped AWS data scientists and machine learning engineers to launch a groundbreaking new analytic: Opportunity Analysis, part of NHL EDGE IQ powered by AWS. Using a powerfully comprehensive combination of historical and real-time data, the machine learning model describes how likely an in-game situation preceding a shot is to result in a successful goal, and explains how the various factors present in the in-game situation contributed to the predicted likelihood. For the first time ever, the NHL’s fans, broadcasters, coaches, teams will have access to quantitative, predictive results in near real time.

  If that sounds like an impressive feat of ML engineering, it is. Consider first the volume of data: over a decade of NHL seasons – each with 1200+ games broken down into fractions of a second, with each fraction of a second divided among a constellation of variables on the ice. Hundreds of data points are collected per second in each game using new NHL EDGE puck and player tracking data, which tracks inputs from sensors in players’ sweater as well as in the puck, and multiply by the speed at which hockey games are played. It doesn’t take long to see how exponentially bigger the already daunting questions about mining the data, determining key inputs, and then harnessing enough power to turn it into something meaningful are when you’re working with this much raw material.

  So how does Opportunity Analysis actually work? The machine learning model starts by looking at the impact of dozens of factors present on the ice in real-time, such as the shot location, puck movement, goalie positioning, traffic and positioning of the puck. During each live game, NHL EDGE puck and player tracking data is streamed into the AWS Cloud .which is combined with historical NHL statistical data and processed through the ML model to predict the likelihood of each shot attempt resulting in a goal, and how much each factor increased or decreased that probability. Effectively, the Opportunity Analysis machine learning model takes a highly detailed picture of the scenario surrounding a shot at the moment of release, compares it to thousands of others, and outputs a high, medium or low rating with factor explanations based on how many times similar scenarios resulted in a successful shot.

  To build the Opportunity Analysis model, data scientists and engineers at AWS collaborated closely with NHL analytics and hockey experts to evaluate influential factors that affect a given shot’s opportunity rating, output as a rating of high, medium or low depending on the factors. At the moment the shooter seizes an opportunity to take a shot (at the release of the shot), where the project team evaluated elements such as distance, locations, angles, speed, and how many times the puck changed directions or whether it crossed the middle of the ice before the shot was taken. It’s been critical to ensure these factors are accurate, as they form the backbone the ML model uses to make the prediction.

  The working team from AWS and the NHL anchored the effort with experts’ understanding of the mechanics of a professional hockey game, and layered the AWS technology over the data with appropriate nuance. After training the Opportunity Analysis model and diligently working through multiple iterations, engineers were able to dial in the accuracy of the factor generation and get a highly accurate prediction. Amazon SageMaker was essential to the success and completion of this project thanks to its unique ability to scale up on demand (up to 32 CPU cores in our case for data parsing and training of the model). AWS was able to quickly experiment with, iterate, and develop the model, and the ease of deploying and scaling SageMaker endpoints simplified the architecture for in-game live inference. Furthermore, use of the security controls of Amazon SageMaker enabling least privilege data access along with other AWS security services including AWS Identity and Access Management, AWS Cloud Trail, and AWS Key Management Service. This level of security allowed the NHL to trust that its confidential and valuable data would be safe with AWS, and allowed for the development of the Opportunity Analysis model within the AWS Cloud infrastructure.

  At current pace, the processing time for  model to return a result is attributed to the number of factors, variables, and data points used in the calculation, as well as the ability to explain the in-game situations underlying the prediction. With this, the impressiveness of the undertaking becomes very clear. Amazon Kinesis Data Streams and Kinesis Data Analytics applications were critical to making live, in-game predictions, enabling the system to perform opportunity analysis calculations for up to 16 live NHL games simultaneously, and the out-of-the-box multi-availability zone replication and automatic failover features of Kinesis contributed to the resilience of our real time application. Amazon EventBridge, Amazon Lambda, Amazon CloudWatch Metrics, and Amazon SNS also formed part of the live in-game architecture to operate the infrastructure based on the NHL game schedule, and enabled a robust system to monitor system operations and alert operators during live-games. Engineers are continuing to make progress reducing latency, which has exciting implications for the game experience on the ice and in the stands (or your living room). The possibilities for engineering at large are pretty exciting, too. What stories could the wide world of data tell if latency wasn’t a concern?

  In the future, AWS and NHL are interested in furthering the work on Opportunity Analysis to explore a prescriptive model, one that would go beyond describing a specific shot opportunity to help assess strategy and determine whether or not a tactic that was used in a specific shot opportunity was effective. In the short term, the introduction of Opportunity Analysis is helping provide fans with a more detailed and nuanced story of why a player missed or scored, based on the assessment of the shot at the time of release. And while it might not soften the blow of a tough loss, it could help build a deeper sense of connection and understanding for fans who are looking to get more out of this incredible game.