Outcome prediction of DOTA2 using machine learning methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it