World Cup Cricket Data Analytics: A Comparative Review of T20 and ODI World Cup Prediction Techniques
Bibliographic record
Abstract
This paper focuses on utilizing data analytics to extract meaningful insights from historical T20 World Cup cricket data, aiding in strategic decision-making. By analyzing statistics from past matches, player performances, and team dynamics, the study identifies key trends and patterns at both macro and micro levels. It showcases the potential of data analytics in sports, helping teams develop data-driven strategies to enhance performance in future T20 World Cup tournaments. The project involves building a predictive model using logistic regression, implemented in Python, to forecast outcomes of future events. Leveraging detailed historical data, including information on participating countries, match types, and winning teams, the goal is to predict whether a team is likely to win in the upcoming seasons, specifically for 2027 and 2031. The logistic regression model is trained on the historical data to understand how well country have performed. This model will be used to predict winning probabilities in next years, sorting the 4 teams with probability of success.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".