“What's the score?”: A first look at sports live data feed services
Bibliographic record
Abstract
The significant interest that sports fans show for live game events, coupled with the major relevance that such information has for the online betting industry, singles out the live sports data as “the most important secondary information in the world.” As a result, a set of specialized - sports data feed service - providers, has emerged. In this paper, we devise a methodology to evaluate such services in terms of speed and accuracy, at scale. We provide, to the best of our knowledge, the first measurement study of such services. By obtaining a direct access to the data feed of a leading sports data provider, we manage to assess the impact of different entities present in the data delivery chain. By measuring National Basketball Association (NBA) and English Premier League (EPL) live games from 40 sports websites, associated with 3 different data feed providers, we find that: (i) the direct data feed that we evaluated is systematically faster than a live cable TV broadcast provider, (ii) the variance of delays significantly increases once the feed gets redistributed by sports websites, and (iii) there exists an order-of-magnitude discrepancy in terms of delay, accuracy, and data diversity among different providers.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".