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on Sports and Economics |
| By: | Michał Lewandowski (Faculty of Economic Sciences, University of Warsaw); Marcin Chlebus (Faculty of Economic Sciences, University of Warsaw) |
| Abstract: | High-quality football predictive models can be very useful and profitable. Therefore, in this research, we undertook to construct machine learning models to predict football outcomes in games from Spanish LaLiga and then we compared them with historical forecasts extracted from bookmakers, which knowledge is commonly considered to be deep and high-quality. The aim of the paper was to design models with the highest possible predictive performances, get results close to bookmakers or even building better estimators. The work included detailed feature engineering based on previous achievements of this domain and own proposals. A built and selected set of variables was used with four machine learning methods, namely Random Forest, AdaBoost, XGBoost and CatBoost. The algorithms were compared based on: Area Under the Curve (AUC) and Ranked Probability Score (RPS). RPS was used as a benchmark in the comparison of estimated probabilities from trained models and forecasts from bookmakers' odds. For a deeper understanding and explanation of the demonstrated methods, which are considered as black-box approaches, Permutation Feature Importance (PFI) was used to evaluate the impacts of individual variables. Features extracted from bookmakers odds’ occurred the most important in terms of PFI. Furthermore, XGBoost achieved the best results on the validation set (RPS equals 0.1989), which obtained similar predictive power to bookmakers' odds (their RPS between 0.1977 and 0.1984). Results of the trained estimators were promising and this article showed that competition with bookmakers is possible using demonstrated techniques. |
| Keywords: | predicting football outcomes, machine learning, betting, adaboost, random forest, xgboost, catboost, ranked probability score, auc, permutation feature importance |
| JEL: | C13 C51 C52 C53 C61 L83 Z29 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2021-22&r=spo |
| By: | Jean-Marc Bourgeon; José De Sousa; Alexis Noir-Luhalwe |
| Abstract: | We examine the risky choices of pairs of contestants in a popular radio game show in France. At one point during the COVID-19 pandemic the show, held in person, had to switch to an all-remote format. We find that such an exogenous change in social context affected risk-taking behavior. Remotely, pairs take far fewer risks when the stakes are high than in the flesh. This behavioral difference is consistent with prosocial behavior theories, which argue that the nature of social interactions influences risky choices. Our results suggest that working from home may reduce participation in profitable but risky team projects. |
| Keywords: | COVID-19, social distancing, social pressure, decision making, risk |
| JEL: | C93 D81 D91 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10063&r=spo |
| By: | Michał Krawczyk (University of Warsaw, Faculty of Economic Sciences); Maciej Wilamowski (University of Warsaw, Faculty of Economic Sciences) |
| Abstract: | We elicit probability forecasts from amateur contract bridge players. At the end of the auction of each deal in a tournament, the players were asked to make a guess (unobservable to others) about the probability with which the contract will be made. We observe them to be overall poorly calibrated. We also find that incentivizing correct forecasts makes no difference. |
| Keywords: | overconfidence, forecast incentives, gender differences, bridge into professions |
| JEL: | D01 L26 J24 J16 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2022-06&r=spo |
| By: | Michał Krawczyk (University of Warsaw, Faculty of Economic Sciences); Joanna Rachubik (University of Warsaw, Faculty of Economic Sciences,) |
| Abstract: | The representativeness heuristic (RH) proposes that people expect even a small sample to have similar characteristics to its parent population. One domain in which it appears to operate is the preference for combinations of numbers on lottery tickets: most players seem to avoid very characteristic, “unrepresentative” combinations, e.g., only containing very low numbers. Likewise, many players may avoid betting on a recently drawn combination because it would seem particularly improbable to be drawn again. We confirm both of these tendencies in a lab experiment and corroborate their external validity in two field experiments. However, we only find a weak link between these two choices: the same people do not necessarily exhibit the two biases. In this sense, there is little consistent manifestation of the RH across different tasks at the individual level. Nevertheless, there are some links related to rationality across the two choices – people who are willing to forgo a monetary payment to get the preferred ticket in one task are also willing to do it in the other. We find such preferences to be related to the misperception of probabilities and providing intuitive, incorrect answers in the Cognitive Reflection Test. |
| Keywords: | Decision making under risk, Lottery choice, Perception of randomness, Number preferences in lotteries, Representativeness heuristic |
| JEL: | C93 D01 D81 D91 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2021-24&r=spo |