Selection of dream-11 players in T20 cricket by using TOPSIS method
Bibliographic record
Abstract
In terms of competitors, spectators, and media interest, cricket is one of the favorite international sports. At the international level, cricket may be played in three formats: a five-day Test, a one-day International (ODI) for each squad of 50 overs, and a Twenty-Twenty (T20) for each team of 20 overs. Various online games that are based on the aforementioned cricket forms allow players to form a virtual squad of real-life players and score points based on how well they perform in real matches. A user gains better rank on the leaderboard if they get the most points in all of the contests they have participated in. Dream11 is one such online gaming platform which offers free and paid contests and thereby gamers can win some cash rewards. So users can select the eleven players and form a team. These eleven players should be selected from the two teams between which the match will be played. Additionally, these eleven players may be chosen from either one team or a combination of the two teams. To identify the eleven players for the T20 cricket format who can provide the maximum score to get the best rank in competitions, many studies employ Multi Criteria Decision Making (MCDM).In this paper, the TOPSIS method is utilized (weights of the performance factors of players are evaluated by using AHP) to determine the top eleven players.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".