Outcome prediction of DOTA2 using machine learning methods
Bibliographic record
Abstract
With the wide spreading of network and capital inflows, Electronic Sport (ES) is developing rapidly in recent years and has become a competitive sport that cannot be ignored. Compared with traditional sports, the data of this industry is large in size and has the characteristics of easy-accessing and normalization. Based on these, data mining and machine learning methods can be applied to improve players' skills and help players make strategies. In this paper, a new approach predicting the outcome of an electronic sport DOTA2 was proposed. In earlier studies, the heroes' draft of a team was represented by unit vectors or its evolution, so the complex interactions among heroes were not captured. In our approach, the outcome prediction was performed in two steps. In the first step, Heroes in DOTA2 were quantified from 17 aspects in a more accurate way. In the second step, we proposed a new method to represent a heroes' draft. A priority table of 113 heroes was created based on the prior knowledge to support this method. The evaluation indexes of several machine learning methods on this task have been compared and analyzed in this paper. Experimental results demonstrate that our method was more effective and accurate than previous methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".