Optimising Daily Fantasy Sports Teams with Artificial Intelligence

Beal, Ryan; Norman, Timothy J.; Ramchurn, Sarvapali D.

doi:10.2478/ijcss-2020-0008

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…The participants should focus on finding less popular undervalued players. Beal et al (2020) used linear regression and random forests for forecasting the fantasy points of NFL players. They compare their predictions to predictions available on Yahoo.…”

Section: Related Workmentioning

confidence: 99%

Competing in daily fantasy sports using generative models

Mlčoch,

Hubáček

2023

Int Trans Operational Res

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We introduce a model for competing in daily fantasy sports competitions. Our approach combines modeling of players' fantasy points and subsequent lineup optimization. The distribution of fantasy points was estimated using an ensemble of generative models. We designed a mixed‐integer quadratic program to optimize not only the average fantasy points but also the variance and covariance of players' fantasy points to exploit the top‐heavy payout structures. We tested our approach on real‐world data. Over eight weeks, the model generated a profit of €2439 with an initial investment of €7133, resulting in a return on investment of over 34%.

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Section: Related Workmentioning

confidence: 99%

Competing in daily fantasy sports using generative models

Mlčoch,

Hubáček

2023

Int Trans Operational Res

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“…In reality, ML has been applied to every aspect of sports. ML algorithms are employed in a wide range of applications, including self-awareness [30], the application of medicine [31], player's performance assessment [32], match outcome predictions [33], player's injury estimation [34], tracking of player's fitness [35], smart sports analysis [36], online content generation tactics [37], selecting a perfect team [38], as well as fantasy sports [39].…”

Section: Literature Reviewmentioning

confidence: 99%

Cricket Players Selection for National Team and Franchise League using Machine Learning Algorithms

Md. Robel,

Md. Ashikur Rahman Khan,

Ishtiaq Ahammad

et al. 2023

Cloud Computing and Data Science

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Cricket player selection is a crucial task for both national teams and franchise leagues. Traditionally, selectors rely on their experience and knowledge to evaluate a player's physical fitness, batting, and bowling performance. However, with the advancements in machine learning algorithms, it is possible to automate and improve the selection process. In this research, a Machine Learning (ML)-based approach is proposed for cricket player selection. This approach uses a combination of physical fitness data, batting and bowling statistics, and other relevant metrics to create a comprehensive player profile. Then three ML algorithms-linear regression, support vector regression, and random forests-are employed to identify the most promising players. Data is collected on a large number of cricket players and their performance in national and franchise leagues from the two most prominent cricket websites, espncricinfo, and cricbuzz, to evaluate the proposed approach. Then the proposed approach trained and tested the ML models on this data and compared their accuracy and performance. Based on the performance scores obtained from these models, two squads are selected for the national team and one squad for each franchise league team. The results demonstrate that the proposed approach can significantly improve the selection process and identify players with high potential. Furthermore, results found that the support vector regression algorithm outperformed other ML models in terms of prediction and player selection.

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Are they worth it? – An evaluation of predictions for NBA ‘Fantasy Sports’

Döpke,

Köhler,

Tegtmeier

2023

J Econ Finan

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Abstract‘Fantasy Sports’ - an internet-based game in which participants chose virtual teams of real professional athletes - has recently gained in popularity. Various firms provide projections regarding athletes’ future performance to help participants choose their virtual teams. We evaluate such forecasts based on 1658 projections regarding NBA basketball of four selected projection providers that were collected in February 2022. We calculate standard measures of forecast quality and find that the use of professional forecasts reduces the errors made in naïve forecasts, but only to a moderate extent. Applying regression-based tests of forecast efficiency, we find that the predictions are inefficient and, in some cases, even biased. Third, pairwise comparisons of the accuracy of the providers suggest notable differences among such providers in the short run. We use a simple optimization algorithm to choose a virtual team for each match day and feed it with the forecasts of the providers. Subsequently, we rank the providers according to the score obtained by these teams. We find small, although in one case significant, long-run differences between the providers, among whom each provides better accuracy than that of a naïve projection based on these athletes’ past performances. Finally, we simulate one-on-one competition among various forecast providers to ascertain the long-term profitability of their services. Given the small magnitude of the detected differences, our results, in brief, raise doubts as to whether the forecasts provided are worth the money.

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Optimising Daily Fantasy Sports Teams with Artificial Intelligence

Cited by 6 publications

References 13 publications

Competing in daily fantasy sports using generative models

Competing in daily fantasy sports using generative models

Cricket Players Selection for National Team and Franchise League using Machine Learning Algorithms

Are they worth it? – An evaluation of predictions for NBA ‘Fantasy Sports’

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