Complex Network Analysis in Cricket : Community structure, player's role\n and performance index
Bibliographic record
Abstract
This paper describes the applications of network methods for understanding\ninteraction within members of sport teams.We analyze the interaction of batsmen\nin International Cricket matches. We generate batting partnership network (BPN)\nfor different teams and determine the exact values of clustering coefficient,\naverage degree, average shortest path length of the networks and compare them\nwith the Erd\\text{\\"{o}}s-R\\text{\\'{e}}nyi model. We observe that the networks\ndisplay small-world behavior and are disassortative in nature. We find that\nmost connected batsman is not necessarily the most central and most central\nplayers are not necessarily the one with high batting averages. We study the\ncommunity structure of the BPNs and identify each player's role based on\ninter-community and intra-community links. We observe that {\\it Sir DG\nBradman}, regarded as the best batsman in Cricket history does not occupy the\ncentral position in the network $-$ the so-called connector hub. We extend our\nanalysis to quantify the performance, relative importance and effect of\nremoving a player from the team, based on different centrality scores.\n
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".