We came to the realization many years ago that one day distributed work would be the norm. I thought it would be the steady march of technology that would make it happen, and I wanted us to be prepared for that day. We also saw many advantages to a team that was good at distributed work, and ever since we had this realization we made sure that any new process or workflow would work even if we were not sitting next to each other.

Then 2020 happened, we all went home, and we had a chance to put all these ideas into practice.

We – my team at Massive Cinematics – have learned a lot about distributed teams over the last 18 months. This article isn’t going to explain everything about working remotely (there are some great resources out there that do), but it does share some of the things we’ve picked up; a few things that worked for us.

It’s crucial that you establish processes and habits during quiet times that are robust enough to handle periods of high stress. That’s a theme common to a lot of the things we’ve learned – you need to fine-tune those processes until they’re dependable and people trust them. I invest time making sure that our creatives have the support they need, that tasks are clearly defined and assigned, and that we’re all working according to the guidelines we co-create. I never regret doing this, and I always regret when I don’t.

It’s crucial that you establish processes and habits during quiet times that are robust enough to handle periods of high stress.

We keep and assign all our tasks in Shotgrid, but the specific technology platform isn’t important as long as it’s searchable and reliable. We keep tasks out of email (we really avoid email all together inside the team) and even though we make heavy use of chat apps we make sure to not assign the tasks in chat. If you let managers throw around tasks in chat then it’s only the friendliest and most approachable people on your team that will end up with a disproportionally large part of the burden. Keeping all the tasks in Shotgrid makes sure that the workload is transparent for everyone.

Meetings is the most valuable communication tool that we have, and it’s also the most expensive one to use. Like the careful craftspeople we are, we don’t abuse our most precious tool and therefore we use it for things where only meetings will work, such as problem solving, brain storming, building consensus and diffusing conflict.

Meetings is the most valuable communication tool that we have, and it’s also the most expensive one to use.

Meetings should not be used for communicating pure information. No real-time info dumps. If you need to inform the team of something, there’s a channel for that – write a list or make a video. People need time to reflect on detailed material if you want them to give a thoughtful response, so first share the information and arrange a meeting later to discuss it (and if you don’t need to discuss it, don’t book a meeting).

Daily sync meetings (that mostly serve to build team culture and create a sense of connection) are prefaced by a daily scheduled post in the chat app where everyone types up their issues or blockers. By the time we have the daily meeting most blockers are already solved. The remaining issues, usually things that require discussion, are then dealt with during the daily meeting.

Our daily meetings are semi-optional. No-one must attend, unless they’ve been specifically asked for. Some people enjoy meetings and thrive on the social interaction. That’s great. Welcome! Some people prefer to focus on their ongoing tasks, and that’s fine too. Without exception, we make sure that someone takes thorough meeting notes. That way everyone, especially managers, can keep abreast of what’s going on. However, everyone must be available for the meetings. If somebody calls on you because they need your input or opinion, you need to be ready to jump in.

Meetings can be good for building a positive team spirit, but they’re not the only way of creating a healthy culture, even online. As in the real world, team culture emerges mostly through small interactions, so there a few things that we make sure we keep doing (and remind each other of if they don’t happen). Some of them are straightforward like thanking people for their work and publicly praising good communications efforts, though some have more to do with our attitudes. This includes things like assuming a positive intent in all communications and being willing to fix things if you notice that they’re broken.

Like so many things around distributed teams, maintaining a healthy culture is about making time to refine routines and then sticking to them. If you make the effort to strengthen your working practices when you can, they’ll see you through the tougher times.

Reading and listening tips

• Graduating Thesis on Innovation Management from Copenhagen Business School: Virtual Team Culture – Hoe & Marinelli
• Book: Deep Work – Call Newport
• Book: It Doesn’t Have to be Crazy at Work – Jason Fried and David Heinmeier Hansson
• Book: Shape Up: Stop Running in Circles and Ship Work that Matter – Ryan Singer
• Book: The year without pants – Scott Berkun
• Book: ReWork – Jason Fried and David Heinmeier Hansson
• Podcast: Distribute – Matt Mullenweg
• Podcast/Book: Freakonomics – Dubner & Levitt
• Podcast: Akimbo – Seth Godin
• Website: Why great teams embrace remote work (https://info.trello.com/hubfs/How_To_Embrace_Remote_Work_Trello_Ultimate_Guide.pdf)

But there’s a few pitfalls that come along with these new opportunities. It might sound odd coming from someone like me – I’ve worked with AI in games for over 8 years – but there is a risk of game developers trying to do too much with their AI. The purpose of AI is to help us make better games, but it’s incredibly easy to lose sight of that. There’s a temptation to push our AI further just because we can, and that can often be counterproductive.

I’ve identified three myths for AI in action games to look out for. If we keep these in mind, it will help us stay focused on what we’re supposed to be doing – giving players fun, challenging, and engaging gaming experiences.

Myth 1: Intelligence is everything

This is an easy trap to fall into. You might think that if you’ve made the best, smartest AI possible, then you’ve succeeded. But fun gameplay does not logically follow from smart AI. AI only needs to be intelligent enough to support the intended gaming experience. Anything beyond that is unnecessary (even though devising more powerful AI solutions can be a lot of fun.)

Tools for creating AI – machine learning, for example – are complicated and costly. They can take many years to develop for use in a shippable game. If you go beyond what you actually need for the game, the bang for your buck just isn’t there.

In many ways, player perception is more important than reality. You could have the smartest AI in the world but if the player doesn’t realize that, then what’s the point? I’ve seen lots of comments about games I’ve worked on, and sometimes players praise enemies’ intelligent decision-making and attribute them extra properties when the AI is actually fairly basic. This is partly down to animations and barks: if enemies look and sound like they’re doing something intelligent, then most people will believe it. I’m not saying that it’s fine to have low-quality AI, but if an action game is fun and layered with convincing sound, animation, and other feedback, then a reasonable level of intelligence is enough.

Myth 2: A squad manager and group tactics are a must

There’s a theory that action games need a meta-AI to handle group tactics and decide on enemies’ overall strategy. It sounds like a sophisticated and even logical approach, but it won’t necessarily improve the game.

In a first-person or third-person action game, you generally can’t zoom out and see the full picture. You can’t get a tactical overview or predict the enemy’s patterns of attack as a whole. You have a very limited range of perspectives, so there’s really no way of knowing what’s going on behind the line of enemies you’re engaging with.

A ticketing system is so much simpler and cheaper, and the player likely won’t notice the difference. Imagine the player is facing a group of enemies. One enemy claims the ‘moving ticket’ – that means that they’ll start moving or pushing towards the player. If you’ve set up the system so there’s only one of these tickets, the other enemies observe this and stay in place to provide cover fire. Done well, it’s a really effective system.

It’s all about perception, and animation and voiceovers can do a lot of the work here. When players hear enemies shouting and gesturing at each other, that’s as likely to give the impression of a coordinated attack as an actual group AI manager.

Myth 3: AI needs to respond immediately to player tactics

In action games, you generally want the player to be proactive rather than reactive. If an AI is on top of the player’s every move, continually pinning them back, it increases the level of stress and eventually decreases the level of fun. Sure, there are situations when it’s cool to switch things around and put that kind of pressure on the player but, in general, proactivity is fun, and we want to keep the player in this state as much possible.

Lightning-fast AI generates movement and noise, which can just be too chaotic, exhausting, and frustrating. You want players to feel in control, and instant AI reactions can undermine that.

A delayed AI response is very often a good thing. It gives players windows of opportunity for them to exploit that brief moment of time to strike at the enemy – and feel really smart when they’re doing it. It also reduces the cognitive load of the player considerably since they’re not having to constantly reanalyze their situation.

Again, animation and voiceovers help sell this. They make enemy reactions realistic but also exploitable. If enemies look and sound surprised when they’re, for example, hit from behind, that pause gives players a chance to make their move.

Ultimately, creating the smartest, fastest, most comprehensive AI is not a recipe for a good action game. You need to make sure that your AI matches your goals, and your main goal is to create a playable, enjoyable game. There’s always a temptation to go big, to keep adding more features and strengthening your AI, but oftentimes this isn’t a good use of resources; beyond a certain point, it’s not going to improve the gaming experience, and you’re better off focusing on improving the impact of your AI. Just remember – and hopefully I don’t sound too much like a second-rate life-coach – don’t lose track of your goals!

Drew Rechner is Associate Game Director at Massive Entertainment – A Ubisoft Studio. He wants to thank Philip Dunstan, who he’s worked alongside in AI and action games for several years. If you want to go a bit deeper into how Massive uses AI, here’s a video of Philip talking about its role in Tom Clancy’s The Division 2.

What can you expect to test in a playtest? Do you have to be a super skilled player? And how do we select our playtesters? In this article, our Games Lab Coordinator Mikkel Colbe Nielsen explains the ins and outs of playtesting!

At the Games Lab we help solve real challenges in the games thanks to the people who come here to playtest.

It’s a way to find a direction for the game and its features, and at Massive, we believe that the more you know, the easier it is to make good decisions. And playtesting is a really good guide to make these decisions.

I mean, how can you know a game is good if you don’t test it? We make games for the players, so it’s essential that players actually try out our games and voice their opinions about what we are doing. And we are listening!

But it can be difficult to understand what you should expect when you sign up to become a playtester.

What will you test? Do you have to be a super skilled player? And what qualities make a good playtester?

In this article, I explain the ins and outs of playtesting to make it easier for you to understand what it means to be a playtester, and what you should expect if you would come to Massive for a test!

1. You don’t have to be an expert!

Players come in all shapes and forms and we encourage everyone in the Skåne region who are interested to apply! And you don’t have to be an expert gamer or have played our games to sign up.

We welcome everyone, and every unique perspective offers us valuable insights on how to improve our games.

It doesn’t matter how old you are, who you are, or if you’re a person with disabilities – we welcome everyone, and every unique perspective offers us valuable insights on how to improve our games. It’s not only because we care about inclusivity and diversity (which we really do!) but also because it provides us with the most accurate data, since our players are so varied and different.

The most important thing is that you are having a good experience.

We don’t want you to feel like you struggle and feel that you’re not enjoying yourself. But as long as you’re willing to try it and feel that you’re having a good experience, we encourage you to come!

2. Selecting who can come and play

Another mystery to solve is how we select our playtesters.

If you sign up to become a playtester, you are added to our Massive database. Depending on the test, we have different sets of criteria we need to fulfil to make the study scientific and statistically valid, often meaning that we are looking for as a wide sample as possible. For example, we could then use these criteria to draw a random selection from our database.

An example of a criteria we might look at is your play history. We might want to test a certain feature for a project that requires specific knowledge of a game or genre, so we would need playtesters with that particular knowledge or skills.

The most important thing is that we try to be as inclusive as possible, to reflect and represent all the voices of all the different players out there that are playing and enjoying our games.

This is especially important when we look at testing changes to a game that already exists. If you have played the game before, you can better determine if you think the changes are good or not! But don’t worry, different tests have different criteria, and we have plenty of tests that don’t require any previous skills or knowledge about games.

To me, as a Lab Coordinator, one of the most important things we look for are availability and location. Since all our tests are on-site due to security, we want you to be located relatively close to our studio in Malmö. This is mainly because we can never tell you beforehand what you are testing, and if it’s a 30 minutes test, we don’t want you to waste an entire day on I mean, if you really want to, it’s entirely up to you.

But – as I’ve said before – the most important thing is that we try to be as inclusive as possible, to reflect and represent all the voices of all the different players out there that are playing and enjoying our games.

3. Compensation?

Will you get compensated? Yes!

At Massive, we give you both monetary compensation for your time and free lunch, drinks, and fika (that is, coffee, tea, and snacks in Swedish).

4. What you can expect to test

One thing I can say, however, is that we never require you to prepare yourself for a test. That is something we take very seriously, as we want you to remain as unbiased as possible!

Usually, our production either approaches us with something that needs to be tested, or they need some knowledge on a specific area before they want to make a decision. It’s our job to assist them in helping them making the most informed decisions by providing them with relevant data and research.

It can be anything from the general feeling of a whole game or very specifically, for example, how you click on different buttons

In general, our playtesting looks at the broader appeal, flow, and feel, contrary to our in-house Quality Control team, which is more technically oriented.

We appreciate if you tell us if you find bugs or technical errors (and we do report them!), but usually this is not what we’re testing (we have our amazing Quality Control team for that). So please, make sure you understand what we’re asking of you in the test, so you don’t focus on things we are not looking at in that test.

The tests also vary in time. Some tests last 30 minutes to an hour, while some – mainly those that require extensive playthroughs of a game – can last several days or even weeks.

5. Honesty is key

We can’t really say what makes a playtester “good” or “bad” since it’s not really something you can be good or bad at, but having an open mind and being truthful to the questions we ask is absolutely vital.

You can never make a mistake when playtesting – everything you do matters in one way or another.

We just want the truth, so we know in which direction to go – we’ll get it somehow!

And do we only want to hear good things? No! More often than not, negative feedback can be even more valuable than only positive feedback – we want it to be honest! And you can never make a mistake when playtesting – everything you do matters in one way or another.

We in the Games Lab are not production, and there is never a developer in the room during the test. We have to remain unbiased and neutral. So even if that means that you didn’t like any of what you tested the whole day, please tell us! We want to hear this.

We are always looking for the truth to guide us to make our games the best possible for as many people as possible.

And we can’t make that happen without you, the players.

• Sign up to become a playtester!
• Read more about playtesting at Massive.

I joined Massive more than four years ago as a Biometric Specialist to help our talented team to reinforce their creative decisions with insights gathered from different sources, like player behavior, opinions, and even bodily responses to game events as players are engaging with them.

I have a background in both academia and the game industry, with over 30 different titles shipped. I have also been tutoring students who wanted to pursue a career in User Research, Human Computer interaction and Data Science before I joined Massive.

Through my extensive network – and with the assistance of our Massive data science team (Alessandro Canossa, Sasha Makarovych) –  we set up a collaboration with a group of well-known researchers from University of York (Christoph Sebastian Deterding and Anders Drachen) and Northeastern University (Casper Harteveld) to examine the effects of communities in games and individuals who have the most influence on them.

The results of this study then helped our developers to create an improved social experience in the game.

In order to investigate the communities, we applied one of the most important methods of mapping and measuring community structures: Social Network Analysis. With this toolset and by using exclusively multiplayer features of Tom Clancy’s the Division, we identified individuals who are at the center of their networks.

We then examined whether these individuals have an impact on their fellow community members in terms of playtime and engagement in group-activities. Finally, we compared their social impact with a random sample of players and a sample of top 1% of our players in terms of performance in the game.

The results showed that not only are these individuals behaving differently from top performers and the average player, but their social behaviors are also imitated by their immediate circle.

The results showed that not only are these individuals behaving differently from top performers and the average player, but their social behaviors are also imitated by their immediate circle.

To show this phenomenon, we isolated players that joined an influencer and monitored their behavior for two weeks before and two weeks after the connection was made.

Just two weeks after joining an influencer’s friends, players showed on average a 30 minutes increase in total playtime and 18% increase in their group playtime. These numbers are much lower or even non-existing for players who joined top performers and a random sample.

Just two weeks after joining an influencer’s friends, players showed on average a 30 minutes increase in total playtime and 18% increase in their group playtime.

Through these studies we also discovered that the impact of the influencers is palpable even after a year of joining their circle – both in engagement with the game for longer periods of time as well as playing in groups.

Using Social Network Analysis metrics allowed us to distill a list of socially impactful players with sustainable influence over playtime and time spent in groups, which in turn helped our game designers to create an improved social experience for our players. Our community developers can also leverage these results to more effectively communicate with and analyze player feedback.

Results of these studies will be published by and presented in Computer-Human Interaction Conference (CHI) in Glasgow on May 2019 and has already been awarded an honorable mention by the editorial board.

In order to make the best game experiences possible, Massive is dedicated to utilize new and innovative ways to know players and create “player centered” games.

• Are you interested in leveraging data driven insights to push Ubisoft games to the next level? We now have an open position for Digital Media Analyst. Apply here!
• Read more on the details of the study here.

In this article, Alessandro Canossa, who works as a Data Scientist in our Massive Consumer Experience team, talks about the research he and some colleagues have done on player motivation in Tom Clancy’s The Division and how it can help shape gameplay as we know it.

Every developer wants to make a game that is fun, but fun isn’t the same for everyone.

When I joined Massive and Ubisoft two years ago, I was thrilled to work at a company where I could actively work with the exciting challenge of deducing advanced intelligence from gameplay data.

Before I started working at Massive, I was Associate Professor at Northeastern University in Boston working with the burgeoning field of Game Analytics, and I even wrote one of the first books on the subject.

Thanks to my academic past, I had the opportunity to work together with my previous colleagues David Melhart, Antonios Liapis, and Georgios Yannakakis at the Institute of Digital Games at the University of Malta on a special project: predicting what motivates different people to play a game based solely on their gameplay behavior.

So which basic psychological needs are satisfied by different video games? What makes a game actually fun to play?

To answer these questions, Massive Entertainment has developed a questionnaire called “Ubisoft Perceived Experience Questionnaire” (UPEQ). The questionnaire is based on a model called Self-Determination Theory, and reveals how each individual player is driven by a different configuration of these factors: competence, autonomy, relatedness and presence.

However, no matter how good a questionnaire is, it is difficult to collect responses for the millions of players engaging with a game.

But, by harnessing innovative machine learning techniques, we found a way to predict player motivations based solely on gameplay behavior and the questionnaire responses from a handful of players, at almost certainty levels. The details of this study can be found here.

This is an incredible result in itself, because normally we would only have information on motivation from the few hundreds of players who take a survey. Now, we can infer it for all the millions of players that play our games.

To predict player motivation the researchers used data gathered from almost 300 voluntary players of Tom Clancy’s The Division and ran it through a preference learning algorithm.

Using game metrics, playstyles, and the UPEQ, the algorithm was ‘taught’ to predict presence and the motivations of players as defined by the psychological theory of self-determination, namely competence, autonomy, and relatedness.

After training, the algorithm can predict how a player feels in relation to competence, autonomy, relatedness and presence with up to 97% accuracy based only on the gameplay metrics.

It is no surprise that motivations and desires affect how someone plays a game, but tying specific in-game actions to specific motivational factors has proven tricky.

Preference learning and solely measuring the relations between the data points is a crucial innovation leading to a better understanding of player behavior and the accompanying motivations.

Player experience is subjective and because of this, its measurement seems to be relative.

The gameplay of players demonstrating a high degree of competence differs greatly from those who lack competence, but there is a whole array in between where the distinctions are not so obvious to the human eye.

The ordinal machine learning approach, however, is able to predict complex psychological constructs in all of its subtleties and nuances. And the success of this experiment shows that it is possible to use high-level gameplay metrics and survey-based annotation to model complex emotional and cognitive states.

At Ubisoft we will leverage this deeper knowledge about our consumers to provide better, more personalized gameplay experiences in the future.

• Are you interested in pushing Ubisoft games to the next level? We now have an open position for Digital Media Analyst.
• Want to learn more about about the “Ubisoft Perceived Experience Questionnaire” (UPEQ)? Read about its creation and validation here.
• If you want to read up on the subjective nature of player experiences, check out the article here.