From F1 racing to League of Legends — How eye tracking can improve your game performance

Mobalytics and Tobii launched a brand-new way for League of Legends (LoL) players to evaluate and improve based on their gaze data. It is a first of its kind solution for competitive gamers and will be a game-changer for players who are interested in improving.

With eye tracking, we can determine how good someone is at a game just by knowing what they look, and pay attention to, during a match. It requires understanding visual attention, which is the term for cognitive operations that people use to select relevant information and filter out irrelevant information. As humans, we filter out much of the visual field so we can pay attention to the most important things we think we need to know.

Even when playing a game, people use the same visual attention behaviors that they use in the real world to see opportunities as well as threats. Player concentration or visual attention is the player's scarcest and most important resource.

With eye tracking, observers can measure how a player’s eyes behave and where they look on screen. It is like tapping into the mind of the player and getting an objective glimpse into their performance potential.

Eye tracking research has been conducted for decades to study human behavior and performance. Since the mid-1900s, scientists have studied professional performance to explain why some individuals are so good at what they do. They have found that expert performance is not tied to general intelligence. Instead, expert performances are bound to specific domains. Meaning that expertise in one activity does not necessarily imply skills are transferrable to a different activity.

Scientists also understand that professionals and experts share similar scan path characteristics. For example, experts quickly move their eyes to task-relevant areas and fixate their eyes to extract valuable information before rapidly moving to the next task-relevant area. For instance, in sports research, professional soccer players tend to have an increased search rate looking at the periphery of their vision and the position of other players, while less-skilled players tend to track the ball. Skilled tennis players tend to look at the opponent’s shoulder and trunk area to predict how the opponent is going to strike the ball while less-skilled players usually look at the opponent’s head. In intense performance situations such as Formula 1 racing, professional drivers have extremely short fixation times (100 ms) on the rear mirror. Still, that time is enough to see if there are racing cars in close pursuit. And while taking a curve, the driver’s visual attention is tight on the apex point to be able to make a perfect turn. 

Similar cases of expert performances and eye-movements are found in competitive gaming. Games have two amazing properties for studying performance: Players are stationary making eye tracking a breeze, and players have clear results with immediate feedback, nicely categorizing their skill through the matchmaking ladders of the game. 

In League of Legends and other MOBA games, players need to look at task-relevant areas to comprehend what is going on. Compared to FPS games, the MOBA game genre also informs the player of game attributes through HUD elements such as the mana/health bar, ability cooldowns, Minimap, chat window, and the clock. This wealth of data encourages players to develop scan paths of eye movements over large parts of the game screen to reach a sense of situational awareness. It is well known that the Minimap is a highly task-relevant area. Until recently, it was not evident just how much a player’s gaze pattern changed from rank to rank. Identifying differences like these enable us to map visual attention between skill levels.

Higher rank players could process information more efficiently than low ranked players. Good players spend less time looking at task-relevant areas than novices. 

From this research, a set of metrics that assess visual attention was created. But will gaze performance metrics in LoL lead to better performance? Well, yes. 

When you play a set of LoL games, the eye tracker and analytics software will collect data on your gaze points on the screen. The software can follow what screen regions you look at and in what situations you look at the regions.

It assesses what you do well and what you need to improve on, expressed through five different metrics:

Tunnel Vision

Zone out occurs when you spend too long focusing on a specific thing (basically tunnel visioning) and limit the amount of information you’re taking in. Any fixation that lasts longer than 1.5 seconds is considered a zone out. 

Information Processing

Fixations tell us where you are focusing and how long you focus there before deciding to look elsewhere. This is a great indicator of how much information you can process and how rapidly you can process it. 

Minimap Awareness

Consistently checking the minimap is a key part of optimal play in League of Legends. This is especially true for the early game when other tasks like the last hitting eat up most of your attention. 

Pick Awareness

Getting picked off is one of the most important indicators of how a player’s awareness breaks down at specific moments in time. Most players get picked off because they are unaware of essential circumstances in their game, such as enemy poisoning around vision. 

Fight Preparation
Where you look when you’re fighting is extremely important in League of Legends. Factors like optimally positioning, watching for cooldowns, and looking out for flankers can all make a huge difference in coming out with a team fight victory. 

With these five metrics, you will receive personalized advice on how to improve. And when you’re ready for it, you can compare your score with your friends, players in the higher rank, and pros. 

By: Dr. Anders Frank, Game Experience Researcher at Tobii & Dr. Amine Issa, Founder at Mobalytics