class: center, middle # Creación de Videojuegos ### Player Analytics --- class: center, middle # Player Rankings --- # Player Ratings - Let's say you have a multiplayer game - Your (competitive) players want to know how good they are - Players also don't want to play against players that are much stronger or weaker than them - But if a player beats a stronger player that should count more than if they beat a weaker player --- class: small # ELO - Chess, and many other games, use a rating system called ELO, where each player is assigned a numerical rating - When two players play, the difference in their current rating is used to calculate their "estimated chances of winning" - Each match is worth a number of points - At the end of the match, the winning player gets a percentage of those points equal to their estimated chances of losing from the losing player - A player that was unlikely to win (because their rating was much lower than their opponents) can get more points from winning than an evenly matched player --- # ELO: Limitations - The ratings just provide an *estimate* for winning probabilities, but they do not tell us how good that estimate is - Players might be incentivized to "sit on their rating" - ELO in team games may not accurately reflect any single player's skill - New players have to have some rating, which can be abused ("smurfing") --- class: small # Some Improvements - Decaying ratings over time encourages active play - The Glicko/Glicko-2 rating systems consist of two numbers: the rating and a "reliability" - This addition helps with better comparing two player's "true" skill - It also helps with smurfing, because new players have a lower "reliability" on their rating - Still does not take individual performance in team games into account (TrueSkill does/can) --- class: small # Rankings and Matchmaking - Once every player has a rating, they can be ranked in descending order - When creating matches to play, several aspects have to be taken into account: * Ratings * Teams * Parties * Queue Times - Two main approaches: * Timed Queues * On-demand matches --- class: center, middle # Player Analytics --- # Why Player Analytics? - Games are expensive to make - Players often abandon games quickly - We want to know what players *do* in our games to keep them engaged - Related: We also want to make money --- # Player Analytics - We can collect data while players play the game - This data can then be analyzed to answer questions/confirm hypotheses - The answers to these questions can then inform game design - Typically, you want the players to keep playing your game, and use all your content - Or you might want to know *what kind* of content you should make more of --- # Player Retention <img src="/CI-2700/assets/img/android_retention.png" width="100%"/> --- # Player Retention - Good first impression/new player experience - Good challenge curve/flow - Delayed rewards (daily log-in bonus, loot chest with a key coming later, etc.) - Give player a personalization/progression choice - Social features, invite and team play rewards (see also: player acquisition) --- class: small # Madden NFL - Madden NFL is a game where you play American football - Researchers (in collaboration with EA) analyzed game log data - They built a statistical model to predict how many games a player would play - Using this model, they identified features which result in higher player retention - Game design can focus on improving these features --- # Madden NFL: Features <img src="/CI-2700/assets/img/nflfeatures.png" width="100%"/> --- # Player Modeling - You can personalize content, or just focus on more common types of players - How do you determine "types" of players? - Your game can track player actions and report back! - Especially in open world games, determining what players spend their time on is beneficial to focus future development or guide players to underexplored areas --- # Player Profiles - On the other hand, you also want to know *who* plays your games - Geographic region, age range, maybe personal details, personality - Why? So you can target ads - Play patterns are also important: time of day, session length, number of games per week --- # Player Profiles: How do you get this data? - IP Geolocation tells you the region - Provide a birthday bonus to incentivize players to tell you their birthday - Connect to facebook to get personal details - Statistical models: Play style and personality have been shown to be related --- # Machine Learning for Player Analytics - Some machine learning techniques can be used to analyze collected data - For example, if we want to know which "types" of players exist, we can use clustering - If we want to *predict* what the player is likely to do, we can use supervised learning (e.g. a neural network) - We can also learn a player's preferences (e.g. which weapon they are likely to favor) --- # How to use this information in Game Play - Marketing is nice and important, but can we use this for actual mechanics? - Of course! - Say a large portion of your players typically play from 7-10pm - Put large in-game events at 8pm - You can cater the type of event to the typical player population --- # How to use this information in Game Play - If you know your players are going to favor a certain kind of weapon, you can focus on making more of these weapons in the future - You can even use the learned models *during* development of future versions to predict where players might go more - Player types can inform the kind of content you should focus on --- # Game Play Statistics - So you have your game and you collect data - Then you analyze what content which players are consuming and when - But you already invested all this time and effort? - Let's look at an example --- # Project Gotham Racing 4 <img src="/CI-2700/assets/img/pgr4.jpg" width="100%"/> --- class: small # Project Gotham Racing 4 - Single player *and* multi player - 10 different game modes: career, arcade, network race, tournaments, time attack, etc. - 29 "event types": street race, elimination, cat and mouse, etc. - 134 cars with 7 different ratings (easy to hard to control) - 9 cities with a total of 121 race tracks - Development cost: "a fortune" (per Business Director Brian Woodhouse) --- class: small # Project Gotham Racing 4: Analysis - 4 researchers (including one from the publisher) analyzed 3.1 million game logs - Overall, 30-40% of the content was used in less than 1% of games - Game modes: Network tournaments were used in less than 0.1% of games - 12 of the 29 event types were used in less than 1% of games - 50 cars were used in less than 0.25% of races - 47 race tracks were used in less than 0.5% of races *each* (13% together) --- class: small # Project Gotham Racing 4: What to do? - First observation: More is not always better - The data can be used when developing a successor - But there would also have been a better way to develop *this* game: * Release a small/core version of the game with less content * Add content *where it is needed* * You will spend less money overall, even though you provide "free content updates" - Claim: An analysis like this might cost $50-$100k, but will save $0.5-$2 million --- # Player Acquisition - Marketing: Next week - For now, let's talk about *mechanics* that encourage player acquisition - Once you have players, you want them to invite their friends - The power of social connections --- class: small # Player Acquisition - Say you have a game in which you tend to *your* farm - You can make money that allows you to buy more things for your farm - Some (fancy) things you can only buy with real money, though - Unless your friend (who also plays the game) "gives" them to you (*they* don't have to pay) - Of course you also *have to* tend to your crops (or they die) - But a friend can help you out on your farm --- # Player Acquisition: Other tactics - Get a bonus for posting a status on facebook that allows your friends to perform some in-game action - Make content harder to beat alone - There is a fine line between designing mechanics for player acquisition and aggressive marketing - The actual game play should still be enjoyable --- # But we don't have any game yet! - Many of the analytics options are only available to the developers - But there are APIs to extract replays for many games - You can run analytics on these replays to find out what players do in *other* games to learn their preference - This can inform design decisions for your own game --- class: center, middle # Unity Analytics --- class: small # Unity Analytics - Unity has built-in support for data collection - To enable analytics, open the Services window under Windows -> General -> Services and switch Analytics to "On" - By default it will collect: * New installs * Daily/Monthly active users * Sessions (total/per user) * Time spent * Users by geographic region, OS, life cycle - There are many other standard events you can use, or create custom ones - Also integrates with monetization options --- class: small # Unity Analytics: Funnels - There are many ways to analyze the collected data - One of the more common ones: Funnels - You measure the progress through some linear progression (e.g. the tutorial) - The "funnel" will show you how far players make it through the progression, i.e. where you lose players ```C# AnalyticsEvent.LevelStart(thisScene.name, thisScene.buildIndex); ``` --- # Unity Analytics: Funnels <img src="/CI-2700/assets/img/funnels.png" width="90%"/> --- # Unity Analytics: Heatmaps - You may also be interested *where* in your game things happen - Where do players go often, where do they die - There is a Heatmap extension for Unity analytics, where you send analytics events with a location - Requires Unity Pro :( --- # Unity Analytics: Heatmaps <img src="/CI-2700/assets/img/heatmap.png" width="90%"/> --- class: small # The EU General Data Protection Regulation - Remember getting emails from every website telling you that they collect personally identifiable information? - That's because of the EU GDPR - Use the Unity Analytics Data Privacy Plug-in - This plug-in includes a button you can place in your game for players to opt out of data collection (and see which data was already collected) - Make sure, *you* don't send personally identifiable information in your analytics events --- # The Flip Side <iframe width="560" height="315" src="https://www.youtube.com/embed/nqGcXOksFGg" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> --- class: small # References - [The Glicko/Glicko-2 rating systems](http://www.glicko.net/glicko.html) - [Player Retention](https://medium.com/roblox-developer/player-retention-a-first-look-at-how-to-get-players-coming-back-to-your-game-9ae68826f466) - [Data-Driven Game Development](https://www.loggly.com/blog/data-driven-game-development-analytics/) - [Data Analytics for Game Development (NIER Track)](https://games.soe.ucsc.edu/sites/default/files/p940-hullett_0.pdf) - [Jim Whitehead on Analytics](https://slideplayer.com/slide/5354512/) - [Raph Koster on Social Mechanics](https://www.gdcvault.com/play/1014555/Social-Mechanics-for-Social-Games) - [Unity Analytics](https://blogs.unity3d.com/es/2016/05/03/analytics-the-player-lifecycle/)