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A generative model for predicting outcomes in college basketball
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| Zeitschriftentitel: | Journal of Quantitative Analysis in Sports |
|---|---|
| Personen und Körperschaften: | , |
| In: | Journal of Quantitative Analysis in Sports, 11, 2015, 1 |
| Format: | E-Article |
| Sprache: | Unbestimmt |
| veröffentlicht: |
Walter de Gruyter GmbH
|
| Schlagwörter: |
| author_facet |
Ruiz, Francisco J. R. Perez-Cruz, Fernando Ruiz, Francisco J. R. Perez-Cruz, Fernando |
|---|---|
| author |
Ruiz, Francisco J. R. Perez-Cruz, Fernando |
| spellingShingle |
Ruiz, Francisco J. R. Perez-Cruz, Fernando Journal of Quantitative Analysis in Sports A generative model for predicting outcomes in college basketball Decision Sciences (miscellaneous) Social Sciences (miscellaneous) |
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ruiz, francisco j. r. |
| spelling |
Ruiz, Francisco J. R. Perez-Cruz, Fernando 1559-0410 2194-6388 Walter de Gruyter GmbH Decision Sciences (miscellaneous) Social Sciences (miscellaneous) http://dx.doi.org/10.1515/jqas-2014-0055 <jats:title>Abstract</jats:title><jats:p>We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.</jats:p> A generative model for predicting outcomes in college basketball Journal of Quantitative Analysis in Sports |
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Walter de Gruyter GmbH |
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Journal of Quantitative Analysis in Sports |
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A generative model for predicting outcomes in college basketball |
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A generative model for predicting outcomes in college basketball |
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A generative model for predicting outcomes in college basketball |
| title_fullStr |
A generative model for predicting outcomes in college basketball |
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A generative model for predicting outcomes in college basketball |
| title_short |
A generative model for predicting outcomes in college basketball |
| title_sort |
a generative model for predicting outcomes in college basketball |
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Decision Sciences (miscellaneous) Social Sciences (miscellaneous) |
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http://dx.doi.org/10.1515/jqas-2014-0055 |
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2015 |
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<jats:title>Abstract</jats:title><jats:p>We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.</jats:p> |
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| author | Ruiz, Francisco J. R., Perez-Cruz, Fernando |
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| description | <jats:title>Abstract</jats:title><jats:p>We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.</jats:p> |
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| spelling | Ruiz, Francisco J. R. Perez-Cruz, Fernando 1559-0410 2194-6388 Walter de Gruyter GmbH Decision Sciences (miscellaneous) Social Sciences (miscellaneous) http://dx.doi.org/10.1515/jqas-2014-0055 <jats:title>Abstract</jats:title><jats:p>We show that a classical model for soccer can also provide competitive results in predicting basketball outcomes. We modify the classical model in two ways in order to capture both the specific behavior of each National collegiate athletic association (NCAA) conference and different strategies of teams and conferences. Through simulated bets on six online betting houses, we show that this extension leads to better predictive performance in terms of profit we make. We compare our estimates with the probabilities predicted by the winner of the recent Kaggle competition on the 2014 NCAA tournament, and conclude that our model tends to provide results that differ more from the implicit probabilities of the betting houses and, therefore, has the potential to provide higher benefits.</jats:p> A generative model for predicting outcomes in college basketball Journal of Quantitative Analysis in Sports |
| spellingShingle | Ruiz, Francisco J. R., Perez-Cruz, Fernando, Journal of Quantitative Analysis in Sports, A generative model for predicting outcomes in college basketball, Decision Sciences (miscellaneous), Social Sciences (miscellaneous) |
| title | A generative model for predicting outcomes in college basketball |
| title_full | A generative model for predicting outcomes in college basketball |
| title_fullStr | A generative model for predicting outcomes in college basketball |
| title_full_unstemmed | A generative model for predicting outcomes in college basketball |
| title_short | A generative model for predicting outcomes in college basketball |
| title_sort | a generative model for predicting outcomes in college basketball |
| title_unstemmed | A generative model for predicting outcomes in college basketball |
| topic | Decision Sciences (miscellaneous), Social Sciences (miscellaneous) |
| url | http://dx.doi.org/10.1515/jqas-2014-0055 |