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Interpretable Real-Time Win Prediction for Honor of Kings – a Popular Mobile MOBA Esport Zelong Yang Zhufeng Pan Yan Wang Tsinghua-Berkeley Shenzhen Institute University of California, Los Angeles AI Lab Tsinghua University Email: [email protected] Tencent Inc. Email: [email protected] Email: [email protected] Deng Cai Xiaojiang Liu Shuming Shi The Chinese University of Hong Kong AI Lab AI Lab Email: [email protected] Tencent Inc. Tencent Inc. Email: [email protected] Email: [email protected] Shao-Lun Huang Tsinghua-Berkeley Shenzhen Institute Tsinghua University Email: [email protected] Abstract—With the rapid prevalence and explosive devel- opment of MOBA esports (Multiplayer Online Battle Arena electronic sports), much research effort has been devoted to automatically predicting game results (win predictions). While this task has great potential in various applications, such as esports live streaming and game commentator AI systems, previous studies fail to investigate the methods to interpret these win predictions. To mitigate this issue, we collected a large-scale dataset that contains real-time game records with rich input features of the popular MOBA game Honor of Kings. For inter- pretable predictions, we proposed a Two-Stage Spatial-Temporal Fig. 1: Application of a non-interpretable prediction model in Network (TSSTN) that can not only provide accurate real-time win predictions but also attribute the ultimate prediction results the LoL 2019 World Championship live streaming. Due to the to the contributions of different features for interpretability. lack of interpretability, the commentators can merely make Experiment results and applications in real-world live streaming simple comments like “Now the win probability of the Blue scenarios showed that the proposed TSSTN model is effective Team is 80%”. both in prediction accuracy and interpretability. I. INTRODUCTION these works, [1]–[4], [11] primarily focus on pre-game win MOBA esports have become increasingly popular. For ex- predictions using pre-game features such as hero selections ample, one of the world’s three most popular and highest- and roles of heroes. Other works [5]–[10] introduce real- grossing MOBA games, Honor of Kings (HoK, whose inter- arXiv:2008.06313v3 [cs.AI] 16 Apr 2021 time (during match) features such as team-kills and heroes’ national version is named Arena of Valor), is attracting more experience to predict the outcomes of MOBA games. For than 80 million daily active players and 200 million monthly example, Yang et al. [5] use three real-time features (gold, active players1. Other popular MOBA games such as DotA II experience, and death) to predict the winner of DotA II. and LoL also have tens of millions of players worldwide. In the last King Pro League Fall 2019 tournament for HoK, the Although some progress has been made, existing studies on prize pool reached 1.13 million dollars2. Due to the enormous MOBA esports win predictions suffer from a major limitation popularity of MOBA esports, related industries such as es- that they fail to investigate the possibility to interpret these ports live streaming and game commentator AI systems have win predictions. The lack of interpretability dramatically limits emerged and increasingly generate huge amounts of profit. their applications in industry scenarios such as esports live Under these circumstances, much research [1]–[11] has been streaming. As one can see in Figure 1, the non-interpretable LoL done on automatic MOBA esports win predictions. Among prediction model used in the 2019 World Championship can only give the win probabilities of the two teams (80% 1https://en.wikipedia.org/wiki/Honor of Kings and 20%). Since the game commentators are not aware of 2https://liquipedia.net/arenaofvalor/King Pro League/2019/Fall the underlying reasons for the prediction results, they can merely make some simple comments such as “Now the win probability of the Blue Team is 80.0%” based on the win probabilities, which is rather dull and non-informative. In contrast, once we can provide more interpretable predictions such as: fWinProbBlue: 80.0%, Factor 1: towers, Factor 2: team compositionsg, the commentators can be aware that it is the difference in tower-counts and team compositions that results in these win predictions. With this information, more insightful comments can be made such as: “The Blue Team has destroyed three towers in the past five minutes and thus accumulated a huge advantage in terms of vision. Besides, Fig. 2: Intermediate prediction results in an HoK game. This with a more powerful team composition, they now have a very figure shows the importance weights and contributions of six high win probability of 80.0%.” feature groups in the example game at 5 minutes and 15 To address the above problems, we collected a large-scale minutes. dataset that contains abundant real-time game records of the popular MOBA game HoK. A critical characteristic of these game records is that they are played by highly-skilled and its time-variant weight denote the contribution of the human players (top 1% according to the game leaderboard). single feature group to the ultimate win prediction. Since matches with higher skill levels result in fewer random It is worth noting that high importance does not necessarily factors [2], [5], the collected dataset is more predictable and lead to a large contribution. For example, team gold is a high- thus can result in more reliable models. The dataset covers importance feature at most time-points, but its contribution 184,362 real human games, and the real-time game records will still be insignificant if the two teams have approximately are collected every 30 seconds, generating 5,253,661 data- similar amounts of gold at the current time-point. On the other frames in total. For each data-frame, the most discriminative hand, features with relatively low importance could still make and human-perceptible features are included, such as teams’ a non-negligible contribution if the Spatial-model’s distinct accumulated gold, kill-counts, and tower-counts. prediction output is high enough. For instance, in the game With the above collected dataset, we propose a Two-Stage shown in Figure 2, the importance weights at 15.0 minutes Spatial-Temporal Network (TSSTN), which gives not only is the fifth out of six (10.1%), while its contribution to the the accurate win predictions but also human-interpretable current game ranks the third (16.6%). intermediate results. In the first Spatial-stage, features are Figure 2 illustrates the interpretable intermediate results grouped into six distinct feature groups (Gold, Kill, Tower, given by the TSSTN model. As shown, our system gives Wild Resource, Soldier, and Heroes) and projected onto six information about the importance weights and contributions separate representation spaces. These are the six most impor- of the six feature groups to explain the underlying reasons for tant features suggested by senior professional commentators. the ultimate predictions. At 5.0 minutes, the most contributed Upon these spaces, we build six Spatial-models that take feature groups are Gold, Kill, and Tower, as the pure coral individual feature groups as input only and make the respective bars show; at 15.0 minutes, the most contributed features win predictions. Then, after a deep investigation on HoK and change to Gold, Soldier, and Wild Resource, as the striped other MOBA games, we find that the same features are not coral bars show. In this example, the Gold feature’s importance invariantly important throughout the games. For example, in and contribution decrease as the match progresses, while the beginning of the games, the team compositions are the other features’ (such as Tower and Soldier) contributions and most vital features to the win predictions; however, in the importance increase as the match proceeds. middle of the games, gold amount may become the most Experiments show that the TSSTN model gives accurate important feature in terms of win predictions. To better model real-time win predictions as well as human-interpretable inter- this “temporal” characteristic, in the second stage (Temporal- mediate results for the popular MOBA game HoK. Besides, stage) we assign six time-variant weights to the six Spatial- the TSSTN model has already been implemented in a real- models, and the ultimate win prediction is made through the world AI commentator system to provide human-interpretable weighted combination of Spatial-models’ outputs. prediction results. The proposed TSSTN model is interpretable in that it The contributions of our work are summarized as follows: separates the effects of the values and importance of features • A large-scale HoK dataset for real-time MOBA esports to decouple their contributions: 1) The Spatial-models’ out- win prediction is collected and utilized to train reliable puts are independent win predictions based on single feature prediction models. groups, which indicate the two teams’ win-or-lose likelihood in • A Two-Stage Spatial-Temporal Network (TSSTN) is pro- the domains of different feature groups (Spatial-domains); 2) posed to make real-time interpretable win predictions by The six time-variant weights of the Temporal-stage represent attributing the predictions to the contributions of different the relative importance of different feature groups at specific features. time-points; and 3) The product of the Spatial-model’s output • Experiment results and applications in a real-world AI commentator system demonstrate the effectiveness and TABLE I: An example data-frame at time 9.5 minutes. interpretability of the proposed TSSTN model. time: 9.5 win side: ··· middleTowerCnt: 3 II. RELATED WORKS lose side: crystalTowerCnt: 3 Due to the high demand for game result forecasts, much hero id: AD: 52, 96, 56, 23, 31 alive: true research has been conducted on predicting the results of hero: id: 31 games, both for traditional sports and MOBA esports.

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