An Analysis of WoW Players’ Game Hours and Further Words

1. An Analysis of WoW Players’ Game Hours and Further Words Pin-Yun Tarng1, Kuan-Ta Chen2, Polly Huang1 1Department of Electrical Engineering, National Taiwan University 2Institute of Information Science, Academia Sinica

2. Motivation • Online gaming has become increasingly popular in recent years, and World of Warcraft is the most famous one among them. • Purpose • Game subscription time prediction • Benefit • Gamer dissatisfaction alarm • Optimized future resource arrangement

3. What We Do • Measure the players’ game session traces proposed a measurement methodology and performed a large-scale measurement (> 600 days) • Understand the characteristics of players’ game hours Players tend to play long and frequently. • Examine the predictability of the game play characteristics Short-term prediction is feasible

4. Talk Progress • Overview • Measuring game play traces • How long do gamers play • When do gamers play • Predictability analysis • Conclusion • Further Prediction Work • Current Work

5. Why World of Warcraft • The fourth game set developed by Blizzard Entertainment. • The most famous MMOG (MassivelyMultiplayer Online Game) for now World of Warcraft

6. Our Basic Method for Measuring • We create a character on WoW server, and keep it online all the time. • In WoW, the command ‘\who’will ask the game server to reply with a list of players who are currently online • If we continuous collect the name list, then we’ll be able to know the login time, logout time of each character.

7. The Idea Scan who is online for every ten minutes scan scan scan scan scan 10 mins 06:00 Alex Ben Calvin Dell Ben Dell Eric Ben Eric Felix Alex Eric Felix Gary Alex Calvin Gary In this case, Ben logins around 6am, and plays for 30 minutes

8. The Problem of Our Basic Method • The server only returns at most 50 accounts in an query.In this situation, we have to narrow down our query ranges by dividing all the users into different races, professions, and levels. Level: 50+ 30 Level: 40~44 40 Level: 40~49 100 50 Level: 45~49 Level: 30~39 10 20

9. Talk Progress • Overview • Measuring game play traces • How long do gamers play • When do gamers play • Predictability analysis • Conclusion • Further Prediction Work • Current Work

10. Summary of Our Traces • Duration of our traces • Since 2005-12-22 to 2007-10-17 • Totally 665 days • Observed accounts • 34521 accounts • Observed sessions • 1672820 sessions

11. How Long Do Gamers Play • We examine “how long” do gamers play from various scales. • Subscription time • Consecutive gameplay days • Daily gameplay activity • Unsubscription • We define that if a player has not shown up in game for longer than 3 months, than he has “quit” the game. • In our traces, many of the observed accounts are censored. • We do survival analysis for the censored accounts.

12. Survival Analysis • Basic concept • Deal with censored data • Evaluate the failure • Reference • http://www.statsoft.com/textbook/stsurvan.html

13. Subscription Time 60% of users will survive longer than an year after their first visits The game is indeed a very attractive game!

14. Consecutive Game Play Days • The highly addicted gamers tend to play consecutively every day. • ON and OFF periods. • We define an ON period as a group of consecutive days during which a player joins the game everyday, and OFF period is the opposite.

15. Cumulative Distribution of ON/OFF Periods Extremely long ON and OFF periods exist 80% of ON and OFF periods are shorter than 5 days OFF periods are slightly longer than ON periods Players tend to alternate between ON and OFF periods shorter than 5 days

16. Season and Vacation • Some extremely long OFF periods exist. • 3% of OFF periods are longer than 30 days. • Even after a long OFF period, gamers may come back and play game as seriously as before. • We define OFF periods which are longer than 30 days as “vacations”, and the active periods between two vacations as “seasons”.

17. Cumulative Distribution of Seasons/Vacations 20% of vacations are longer than 180 days 50% of seasons are longer than 60 days Even after a long vacation, about 20% of gamers still return

18. Daily Activities • The daily activities are important predictors of users’ subscription time. • Users’ daily behavior includes: • Daily playtime • Daily session count • Session playtime

19. Cumulative Distribution of Daily Activities More than 80% gamers login less than 2 times per day 25% gamers play longer than 5 hours per day Significant knees around 1 hour and 5 hours 75% gamers play longer than 2 hours per day WoW is attractive, and gamers tend to play long after his/her login.

20. Talk Progress • Overview • Measuring game play traces • How long do gamers play • When do gamers play • Predictability analysis • Conclusion • Further Prediction Work • Current Work

21. When Do Gamers Play? • We observe: • Average daily playtime on each day of a week. • Average number of gamers on each hour in a day. • From our intuition • Playtime on weekends would be obviously higher than on weekdays. • Number of gamers at night would be obviously higher than in the morning.

22. When Do Gamers Play (2) Average daily playtime on different day The difference between weekends and weekdays is not significant

23. When Do Gamers Play (3) Average number of gamers at different time The peak hours are from 21pm to 1am The coldest hours are from 4am to 10am Support our intuition!

24. Talk Progress • Overview • Measuring game play traces • How long do gamers play • When do gamers play • Predictability analysis • Conclusion • Further Prediction Work • Current Work

25. Predictability • Can we predict what will happen based on the gameplay history of gamers? • Our analysis • Short-term vs. long-term behavior • Evaluate temporal dependence

26. Predictability of Short-term Behavior • Is short-term behavior a reliable indicator?

27. Predictability of Short-term Behavior • Is short-term behavior a reliable indicator?

28. Predictability of Short-term Behavior • Is short-term behavior a reliable indicator? Long-term behavior is weakly correlated.

29. Players’ Game Hours in Consecutive Periods • Does temporal dependence exist?

30. Players’ Game Hours in Consecutive Periods • Does temporal dependence exist? The strongest correlation

31. Players’ Game Hours in Consecutive Periods • Does temporal dependence exist? The strongest correlation

32. Players’ Game Hours in Consecutive Periods • Does temporal dependence exist? Weaker than weekly playtime Weekly patterns are the most regular for most people

33. Summary of Predictability • The more stars represent the stronger correlated

34. Talk Progress • Overview • Measuring game play traces • How long do gamers play • When do gamers play • Predictability analysis • Conclusion • Further Prediction Work • Current Work

35. Summary of This Paper • We study players’ game hours for WoW, during a 2-year period. • We analyze “when” and “how long” gamers play WoW. • Our results indicate that although short-term prediction is feasible, long-term prediction is much more difficult. • Our goal is to construct a model that can predict whether a player will leave a game.