Analysis of Lack of Data in Esports: Important Lessons for the Industry

Esports analysis requires thorough data preparation to accurately assess elements such as patches, meta, rosters, and tournament systems. However, many reports lack basic information, leading to the conclusion that in-depth analysis cannot be performed. This reflects the current reality of the industry, where information providers often only provide vague or insufficient data. In this context, tracking and understanding indicators like win-rate, pick-ban rate, or gold difference in matches becomes more challenging than ever. Analysts must face many risks such as not determining the direction of the meta, not evaluating the benefits for stakeholders, or predicting the consequences of patch changes. This not only affects the quality of analysis but also reduces trust from fans and experts. Let's consider a specific example: when there is no data on the actual server version compared to the practice server, analyses of patches often lead to serious errors. Teams may be affected undesirably if they do not grasp changes in a timely manner. Similarly, in evaluating the tournament system, lack of information about format type, series length, and schedule density can lead to inaccurate assessment of fatigue risks for players. Regional context also plays an important role, but lack of data on international results, talent pool, and ecosystem health makes comparing regions meaningless. Club budgets, sponsorship revenue, and salary expenses cannot be accurately evaluated, leading to unforseeable financial risks. Rules and governance systems also face similar issues, with risks of violating competitive integrity or unmonitored transfer regulations. In risk analysis, lack of data increases uncertainty in competitive, financial, personnel, and public opinion areas. In summary, the entire analysis shows that there is no information to perform a comprehensive evaluation, from competitive value to industry value, to reference value. Recommendations include providing complete data from the initial stage to avoid high risks. Signals to monitor include information source quality and data timeliness. The esports industry needs to improve information provision processes to enhance analysis quality and support investors, fans, and teams. This will help the industry develop more sustainably, avoiding similar mistakes in the future.