Are Canadian-born Major League Baseball players more likely to bat left-handed? A partial test of the hockey-influence on batting hypothesis
Bibliographic record
Abstract
It has been suggested that Canadian-born Major League Baseball (MLB) players are more likely to bat left-handed, possibly owing to the fact that they learn to play ice hockey before baseball, and that there is no clear hand-preference when shooting with a hockey stick; approximately half of all ice hockey players shoot left. We constructed a database on active (i.e., October, 2016) MLB players from four countries/regions based on place of birth (Canada, United States of America [USA], Dominican Republic and South Asia [i.e., Japan, Taiwan and South Korea]), including information on which hand they use to bat and throw. We also extracted information on all Canadian-born MLB players, dating back to 1917. Our results confirm that the proportion of left-handed batters born in Canada is higher when compared to the other countries selected; also, since 1917, the proportion of Canadian MLB players who bat left has been consistently higher than the league average. We also compared the proportion of left-handed batters in Canada with players born in states in the USA grouped into high, average and low based on hockey participation. The proportion of MLB players born in states with a high level of hockey participation were more likely to bat left, although the differences were significant at trend level only (p < .10). Lastly, we found that while Canadians were more likely to bat left-handed, this did not correspond with a greater left-hand dominance, as determined by throwing hand. In conclusion, the present study confirms that Canadian-born MLB players are more likely to bat left-handed when compared to American, Dominican Republic and South Asian-born MLB players, providing partial support for the hockey influence on batting hypothesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".