李仁傑,畢業於中國科學技術大學少年班, 美國 University of Rochester 博⼠。現為 Riot Games 數據科學總負責人。致力於通過開發創新的數據產品來提高用戶的體驗,甚至人類的生活。從⽆到有建立了Riot Games Data Science 部⻔, 帶領團隊從《英雄聯盟》的海量數據平台中用大數據分析大規模提升商業決策和運營效率, 並用機器學習算法開發了精細化運營, 精準分析預測, 個性化推薦, 自然語⾔處理等各種全球領先的數據產品。
5. Players & Data
W O R L D W I D E
Statistics released Jan 2014
67+ million
monthly active players
500+ billion
data points per day
26 petabytes
data collected since beta
6. DATA SCIENCE AT RIOT GAMES
EMPOWER RIOTERS TO MAKE BETTER
DATA POWERED PRODUCTS
TEAM MISSION
7.
8. DATA SCIENCE AT RIOT GAMES
PLAYER FOCUSED
DATA INFORMED, NOT DATA DRIVEN
TEAM PHILOSOPHIES
11. DATA SCIENCE AT RIOT GAMES
Data
Science
RiskMarketing
Ecommerce
12. DATA SCIENCE AT RIOT GAMES
Data
Science
Social Play
RiskMarketing
Ecommerce
13. DATA SCIENCE AT RIOT GAMES
Data
Science
Social Play
Risk
Match
Making
Marketing
Ecommerce
14. DATA SCIENCE AT RIOT GAMES
Data
Science
Social Play
Risk
Match
Making
Player
Onboarding
Marketing
Ecommerce
15. DATA SCIENCE AT RIOT GAMES
Data
Science
Social Play
AI
Risk
R & D
Match
Making
Eco System
Player
Support
Player
Onboarding
Game
Balance
Design
Marketing
Ecommerce
19. UNDERSTANDING PLAYERS’ IN-GAME BEHAVIOR
CHAMPION PLAY PATTERNS
TYPE A CHAMPION
TYPE B CHAMPION
TYPE C CHAMPION
TYPE E CHAMPION
TYPE F CHAMPION
TYPE G CHAMPION
TYPE D CHAMPION
7 KEY TYPES OF CHAMPIONS
OUR DESIGN PHILOSOPHY ALIGNS
WITH PLAYERS PLAY STYLES
20. UNDERSTANDING PLAYERS’ IN-GAME BEHAVIOR
0%
10%
20%
30%
40%
50%
60%
Type
A
Champion
Type
B
Champion
Type
C
Champion
Type
D
Champion
Type
E
Champion
Type
F
Champion
Type
G
Champion
Player
1
Player
2
PLAYER SEGMENTATION BASED ON HOW THEY
PLAY EACH TYPE OF CHAMPION
21. UNDERSTANDING PLAYERS’ IN-GAME BEHAVIOR
PLAYER SEGMENTATION BASED ON HOW THEY
PLAY EACH TYPE OF CHAMPION
9 DIFFERENT CHAMPION PLAY BEHAVIOR
22. UNDERSTANDING PLAYERS’ IN-GAME BEHAVIOR
Type
A
Type
B
Type
C
Type
D
Type
E
Type
F
Type
G
IDEAL VS. CURRENT
DISTRIBUTION OF CHAMPION
CLUSTERS
CURRENT
IDEAL
OVERSERVEDUNDERREPRESENTED
23. CASE STUDY #1 TAKEAWAYS
CERTAIN CHAMPION ARCHETYPES MAY BE UNDERREPRESENTED OR OVERSERVED
HOW DIFFERENTLY PLAYERS PLAY OUR CHAMPIONS
WHETHER OUR DESIGN PHILOSOPHY ALIGNS WITH PLAYERS’ PLAY STYLES
CHAMPION PLAY PATTERN MODEL HELPS US BETTER UNDERSTAND:
28. CASE STUDY #2 TAKEAWAYS
PROVIDE INSIGHTS FOR STRATEGIC DECISION MAKING AND PLANNING
QUANTIFY EFFECT OF EVENTS AND CHANGES IN THE GAME
FIND AND UNDERSTAND IMPORTANT FACTORS THAT PLAYERS ARE INTERESTED IN
WEEKLY ENGAGEMENT PREDICTION MODEL HELPS US :
31. PERSONALIZED RECOMMENDATION
MANY ENTERTAINMENT SERVICES AND ECOMMERCE COMPANIES
HAVE MADE RECOMMENDER SYSTEM A PROMINENT PART OF THEIR
WEBSITES
GOOD RECOMMENDATIONS ADD ANOTHER DIMENSION TO THE USER
EXPERIENCE AND BOOST CONTENT ENGAGEMENT
32. PERSONALIZED RECOMMENDATION
GOOGLE NEWS: RECOMMENDATIONS GENERATE 38% MORE CLICKTHROUGH
NETFLIX: 66% OF THE MOVIES WATCHED ARE RECOMMENDED
AMAZON: 35% SALES ARE FROM RECOMMENDATIONS
46. INSTANT FEEDBACK SYSTEM
SUPPORTS 14 DIFFERENT LANGUAGES
INCREASES CHATLOG COVERAGE FROM 10% TO 100%
DECREASES DETECTION TIME FROM WEEKS to 15 MINS
47. DATA SCIENCE AT RIOT GAMES
PLAYER FOCUSED
DATA INFORMED, NOT DATA DRIVEN
TEAM PHILOSOPHIES
EMPOWER RIOTERS TO MAKE BETTER
DATA POWERED PRODUCTS
TEAM MISSION