Abstract
There seems to be an upper limit to predicting the outcome of matches in (semi-)professional sports. A number of works have proposed that this is due to chance and attempts have been made to simulate the distribution of win percentages to identify the most likely proportion of matches decided by chance. We argue that the approach that has been chosen so far makes some simplifying assumptions that cause its result to be of limited practical value, especially for settings where teams do not play all possible opponents. Instead, we propose to use clustering of statistical team profiles and observed scheduling information to derive limits on the predictive accuracy for particular seasons, which can be used to assess the performance of predictive models on those seasons. Using NCAA basketball data, we show that the resulting simulated distributions are much closer to the observed distributions and give higher assessments of chance and tighter limits on predictive accuracy. We also show similar results for the NBA.
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Notes
- 1.
For details for Weissbock’s work, we direct the reader to [12].
- 2.
The choice of seasons is due to presentation concerns, especially in the case of visualizations. Other/additional seasons exhibit similar phenomena.
- 3.
Other seasons show similar behavior, so we treat 2008 as a representative example.
- 4.
The results for other seasons can be found in Appendix A.
- 5.
Although some might be similar enough to be merged.
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Zimmermann, A. (2023). Chance and the Predictive Limit in Basketball (Both College and Professional). In: Bifet, A., Lorena, A.C., Ribeiro, R.P., Gama, J., Abreu, P.H. (eds) Discovery Science. DS 2023. Lecture Notes in Computer Science(), vol 14276. Springer, Cham. https://doi.org/10.1007/978-3-031-45275-8_40
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