IEEE Game Innovation Conference 2013 - Tuning Mobile Game Design Using Data Mining
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Tuning Mobile Game Design Using Data Mining PL Lanzi, D Loiacono, E Parini, F Sannicolo’, C Scamporlino, & M Pirovano
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IEEE Game Innovation Conference 2013 - Tuning Mobile Game Design Using Data Mining
- 1. Tuning Mobile Game Design Using Data Mining PL Lanzi, D Loiacono, E Parini, F Sannicolo’, C Scamporlino, & M Pirovano Game Innovation Conference 2013 – Vancouver September 23-25
- 2. Traditional Game Development • • • 2 Development of video games on traditional platforms (PC/console) follows well-defined best practices Significant portions of the overall cycle are devoted to playtest and testing The final product is generally expected to be almost perfect and well-polished concept 1-2 years Game Innovation Conference 2013 – Vancouver September 23-25 final product
- 3. Mobile Game Development • • • • • Several companies follow the same development used for traditional platforms and invest 1-2 years on large projects However the approach is infeasible for most mobile/indie companies which cannot sustain such a “long” cycle Success in the mobile market appears not to follow established criteria Long projects are perceived as too risky Recent strategies favor the rapid exploration of new ideas and follow up only the more successful ones Development 2-3 months (4-6 applications per year) Follow up only to the most successful ones § § Game Innovation Conference 2013 – Vancouver September 23-25 3
- 4. our experience the task develop one video game for Windows Phone to participate to the 2012 Microsoft Imagine Cup the challenges short development (four months from start to end) small user base (almost nobody we knew had a Windows Phone) variety of platform with rather different features secrecy! the app could not be distributed before submission Game Innovation Conference 2013 – Vancouver September 23-25
- 5. our approach instrument the application code to trace almost everything the users do perform very short playtesting sessions (1-2 days) apply data mining to the collected data to extract typical users’ behavior to evaluate gameplay check users’ behavior on different platforms Game Innovation Conference 2013 – Vancouver September 23-25
- 6. Bad Blood – A Serious Game About Diseases • • • • Casual game for Windows phones developed during the Videogame Design and Programming course at the Politecnico di Milano Bad Blood aims at spreading the knowledge about human diseases through a series of games settled in blood vessels, in the respiratory system, and in the brain Five continents, in which players can select a specific region (e.g., West Australia) that also corresponds to a disease and thus to a specific scenario Four game mechanics: attack, tap, survive and puzzle Game Innovation Conference 2013 – Vancouver September 23-25 6
- 7. http://www.youtube.com/watch?v=J-VPhs1ywOU Game Innovation Conference 2013 – Vancouver September 23-25
- 8. Collecting Game Data • • • • 8 Our analysis focused on the two game modes with the highest interactivity (attack and tap) Before playing, users were asked for age and gender Code was instrumented to collect any possible information (raw data) about user behavior every 200ms The raw data were then elaborated to compute several variables including length and direction of the swipe gesture center position of the players’ cells during collisions number of opponents in every screen the number of hits and misses in every seconds the positions of the hits and misses … § § § § § § Game Innovation Conference 2013 – Vancouver September 23-25
- 9. Mining Users’ Data: Population Game Innovation Conference 2013 – Vancouver September 23-25 9
- 10. Attack Mode: Trajectory of Users’ Swipes Game Innovation Conference 2013 – Vancouver September 23-25 10
- 11. Good Taps & Bad Taps Game Innovation Conference 2013 – Vancouver September 23-25 11
- 12. Our Flawed Gameplay Game Innovation Conference 2013 – Vancouver September 23-25 12
- 13. How We Solved the Issue in Time for Submission • • • We modified the gameplay before the final submission to the competition Each level in attack mode has a random instant mini boss fight involving bigger bacteria and viruses The users has to instantly increase the firing rate to be able to destroy the enemy before it can hit the player or disappear at the bottom of the screen Game Innovation Conference 2013 – Vancouver September 23-25 13
- 14. Conclusions and Take-Home Message • • • • • 14 We would never make the submission with a more traditional approach to playtesting Completely Instrumenting the code helped us getting the best out of the relatively few users we could test our game with The analysis of the collected data helped us Improving the touch interface (and colliders’ placement) Discovering a major design flaw that would have made the game boring § § We did not win the Microsoft Imagine Cup 2012! L But we won “Share Care” a major national competition for serious games devoted to blood donation and a special prize for innovation J Game Innovation Conference 2013 – Vancouver September 23-25
- 15. Thank You! download it @ http://www.badbloodgame.net/ Game Innovation Conference 2013 – Vancouver September 23-25
Editor's Notes
- Qui ho messo due cover una di GTA V (ovviamente un successoneche non e’ statoancoracriticato) e SimCity che e’ statomassacrato.
- in questocaso ho messo ungiocotradizionale (Deadspace e dots, piu’ sempliceche ha venduto di piu’).
- sopra e’ attack, sotto e’ survive
- Figure 3a shows the distribution of the swipe angle where 0 corresponds to the vertical direction, negative angles correspond to swipes toward the upper left corner and positive angles correspond to swipes toward the upper right corner of the screen; we also note a slight bias toward the right side of the screen (the skewness of the distribution is −1.82). Figure 3b shows the distribution of the length of the swipe gestures performed by the users. As it can be noticed, gestures tend to be very short (typically less than 40 pixels long) and long gestures are rare.
- Figure 4d shows the distribution of taps on good (i.e., enemy) targets (placed in position ⟨0,0⟩). We note that the distribution is not centered on the origin but it has its peak at the lower left of it; this can be easily explained by noting in mobile games, players rarely cover the target with their full finger and that since the enemies arrive from the top of the screen so users tend to tap them from the lower bottom side. Figure 4e shows the distribution of wrong taps around enemies (assuming them in position ⟨0,0⟩) and include two types of mistakes: voluntary taps on wrong target (the players did not get the game mechanic correctly) or involuntary taps (the players aimed at something else but hit the wrong target). First, the distribution of the good taps (Figure 4d) around enemies is very different from the distribution of the wrong taps (Figure 4d). In fact, while good taps are more frequent in the areas very close to the target center, the bad taps are not centered around the target and are very frequent also in areas quite far from the target, i.e., at the edge of the collider box. This suggests that most of the wrong taps are involuntary and that players are able to clearly distinguish the good targets from the bad ones. Second, collected data show that players are much more accurate on the upper part of the screen with respect to the lower part (see Figure 4c) coherently with the designed mechanics. In fact, the targets should reach the bottom of the screen only when the player is busy with other targets and, thus, we expect a decrease in the tap accuracy. However, this finding also suggests that we could extend the design (and perhaps increasing the difficulty) by adding new enemy entering directions (e.g, having some enemies appearing from the left of the screen and exiting on the right).
- Nell’analisi ci siamoaccortiche per la modalita’ di attackl’utentecontinuava a lanciareglobulibianchi con un ritmocostanteindipendentemente dal numero di nemicipresenti.Per variareilritmoabbiamointrodottoil boss. Il Globulonedellaprossima slide.our analysis identified a serious flaw in the attack mode: the collected game data showed that players keep firing the white blood cell at a rather steady rate and almost uniformly all over the screen. Therefore, players’ behavior is almost the same, disregarding the actual number of targets on the screen and time passed from the beginning of the level (see Figure 6), i.e., there are neither changes in the pace of the games nor thrills. Based on this analysis we decided to modify the gameplay before the final submission to the competition by adding, to each level in attack mode, an instant mini boss fight consisting of bigger bacteria and viruses randomly scheduled. These requires that the users instantly increase the firing rate to be able to destroy the enemy before it can hit the player or disappear at the bottom of the screen. Figure 7 shows a screenshot of the new boss fight element that can be randomly introduced during an attack mode level.