bibliography.bib

@book{typo,
  author    = {},
  title     = {Lexique des règles typographiques en usage à l'Imprimerie nationale},
  publisher = {L'Imprimerie nationale},
  year      = {2002},
  address   = {Paris},
  isbn      = {2-7433-0482-0}
}

@online{knuthwebsite,
  author   = {Donald Knuth},
  title    = {Knuth: Computers and Typesetting},
  url      = {http://www-cs-faculty.stanford.edu/~uno/abcde.html},
  addendum = {(accessed: 01.09.2016)},
  keywords = {latex,knuth}
}

@article{einstein,
  author   = {Albert Einstein},
  title    = {{Zur Elektrodynamik bewegter K{\"o}rper}. ({German})
              [{On} the electrodynamics of moving bodies]},
  journal  = {Annalen der Physik},
  volume   = {322},
  number   = {10},
  pages    = {891--921},
  year     = {1905},
  doi      = {http://dx.doi.org/10.1002/andp.19053221004},
  keywords = {physics}
}

@book{dirac,
  title     = {The Principles of Quantum Mechanics},
  author    = {Paul Adrien Maurice Dirac},
  isbn      = {9780198520115},
  series    = {International series of monographs on physics},
  year      = {1981},
  publisher = {Clarendon Press},
  keywords  = {physics}
}

@online{royalAcademyOfEngineering,
  publisher = {The Royal Academy Of Engineering},
  title     = {Formula One Race Strategy},
  url       = {https://www.stem.org.uk/rxstz},
  addendum  = {(accessed: 20.05.2023)},
  note      = {Supported by McLaren Racing Limited},
  year      = {2019}
}

@online{hurryUpAndWeight,
  author   = {Vyssion \& jjn9128},
  title    = {Hurry up and weight},
  year     = {2018},
  month    = {05},
  day      = {29},
  url      = {https://www.f1technical.net/features/21637},
  addendum = {(accessed: 20.05.2023)}
}

@online{parttimeanalyst,
  author   = {"Part time analyst"},
  title    = {F1 Strategy Analysis},
  year     = {2021},
  month    = {09},
  day      = {29},
  url      = {https://theparttimeanalyst.com/2021/09/29/f1-strategy-analysis/},
  addendum = {(accessed: 20.05.2023)}
}

@article{app10124229,
  author         = {Heilmeier, Alexander and Graf, Michael and Betz, Johannes and Lienkamp, Markus},
  title          = {Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport},
  journal        = {Applied Sciences},
  volume         = {10},
  year           = {2020},
  number         = {12},
  article-number = {4229},
  url            = {https://www.mdpi.com/2076-3417/10/12/4229},
  issn           = {2076-3417},
  abstract       = {Applying an optimal race strategy is a decisive factor in achieving the best possible result in a motorsport race. This mainly implies timing the pit stops perfectly and choosing the optimal tire compounds. Strategy engineers use race simulations to assess the effects of different strategic decisions (e.g., early vs. late pit stop) on the race result before and during a race. However, in reality, races rarely run as planned and are often decided by random events, for example, accidents that cause safety car phases. Besides, the course of a race is affected by many smaller probabilistic influences, for example, variability in the lap times. Consequently, these events and influences should be modeled within the race simulation if real races are to be simulated, and a robust race strategy is to be determined. Therefore, this paper presents how state of the art and new approaches can be combined to modeling the most important probabilistic influences on motorsport races—accidents and failures, full course yellow and safety car phases, the drivers’ starting performance, and variability in lap times and pit stop durations. The modeling is done using customized probability distributions as well as a novel “ghost” car approach, which allows the realistic consideration of the effect of safety cars within the race simulation. The interaction of all influences is evaluated based on the Monte Carlo method. The results demonstrate the validity of the models and show how Monte Carlo simulation enables assessing the robustness of race strategies. Knowing the robustness improves the basis for a reasonable determination of race strategies by strategy engineers.},
  doi            = {10.3390/app10124229}
}


@article{app10217805,
  author         = {Heilmeier, Alexander and Thomaser, André and Graf, Michael and Betz, Johannes},
  title          = {Virtual Strategy Engineer: Using Artificial Neural Networks for Making Race Strategy Decisions in Circuit Motorsport},
  journal        = {Applied Sciences},
  volume         = {10},
  year           = {2020},
  number         = {21},
  article-number = {7805},
  url            = {https://www.mdpi.com/2076-3417/10/21/7805},
  issn           = {2076-3417},
  abstract       = {In circuit motorsport, race strategy helps to finish the race in the best possible position by optimally determining the pit stops. Depending on the racing series, pit stops are needed to replace worn-out tires, refuel the car, change drivers, or repair the car. Assuming a race without opponents and considering only tire degradation, the optimal race strategy can be determined by solving a quadratic optimization problem, as shown in the paper. In high-class motorsport, however, this simplified approach is not sufficient. There, comprehensive race simulations are used to evaluate the outcome of different strategic options. The published race simulations require the user to specify the expected strategies of all race participants manually. In such simulations, it is therefore desirable to automate the strategy decisions, for better handling and greater realism. It is against this background that we present a virtual strategy engineer (VSE) based on two artificial neural networks. Since our research is focused on the Formula 1 racing series, the VSE decides whether a driver should make a pit stop and which tire compound to fit. Its training is based on timing data of the six seasons from 2014 to 2019. The results show that the VSE makes reasonable decisions and reacts to the particular race situation. The integration of the VSE into a race simulation is presented, and the effects are analyzed in an example race.},
  doi            = {10.3390/app10217805}
}

@article{doi:10.1089/big.2014.0018,
  author   = {Tulabandhula, Theja and Rudin, Cynthia},
  title    = {Tire Changes, Fresh Air, and Yellow Flags: Challenges in Predictive Analytics for Professional Racing},
  journal  = {Big Data},
  volume   = {2},
  number   = {2},
  pages    = {97-112},
  year     = {2014},
  doi      = {10.1089/big.2014.0018},
  note     = {PMID: 27442303},
  url      = { https://doi.org/10.1089/big.2014.0018},
  eprint   = { https://doi.org/10.1089/big.2014.0018},
  abstract = { Abstract Our goal is to design a prediction and decision system for real-time use during a professional car race. In designing a knowledge discovery process for racing, we faced several challenges that were overcome only when domain knowledge of racing was carefully infused within statistical modeling techniques. In this article, we describe how we leveraged expert knowledge of the domain to produce a real-time decision system for tire changes within a race. Our forecasts have the potential to impact how racing teams can optimize strategy by making tire-change decisions to benefit their rank position. Our work significantly expands previous research on sports analytics, as it is the only work on analytical methods for within-race prediction and decision making for professional car racing. }
}

@phdthesis{phdthesis,
  author = {Choo, Christopher},
  year   = {2015},
  month  = {01},
  pages  = {},
  title  = {Real-time decision making in motorsports : analytics for improving professional car race strategy}
}

@article{LIU2021106781,
  title    = {Formula-E race strategy development using distributed policy gradient reinforcement learning},
  journal  = {Knowledge-Based Systems},
  volume   = {216},
  pages    = {106781},
  year     = {2021},
  issn     = {0950-7051},
  doi      = {https://doi.org/10.1016/j.knosys.2021.106781},
  url      = {https://www.sciencedirect.com/science/article/pii/S0950705121000447},
  author   = {Xuze Liu and Abbas Fotouhi and Daniel J. Auger},
  keywords = {Energy management, Formula-E race strategy, Deep deterministic policy gradient, Reinforcement leaning},
  abstract = {Energy and thermal management is a crucial element in Formula-E race strategy development. In this study, the race-level strategy development is formulated into a Markov decision process (MDP) problem featuring a hybrid-type action space. Deep Deterministic Policy Gradient (DDPG) reinforcement learning is implemented under distributed architecture Ape-X and integrated with the prioritized experience replay and reward shaping techniques to optimize a hybrid-type set of actions of both continuous and discrete components. Soft boundary violation penalties in reward shaping, significantly improves the performance of DDPG and makes it capable of generating faster race finishing solutions. The new proposed method has shown superior performance in comparison to the Monte Carlo Tree Search (MCTS) with policy gradient reinforcement learning, which solves this problem in a fully discrete action space as presented in the literature. The advantages are faster race finishing time and better handling of ambient temperature rise.}
}



@online{scikitLearnRandomForest,
  author   = {scikit-learn},
  title    = {Random Forests},
  url      = {https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#},
  addendum = {(accessed: 01.07.2023)}
}

@online{classweight,
  author   = {scikit-learn},
  title    = {sklearn.utils.class\_weight.compute\_class\_weight},
  url      = {https://scikit-learn.org/stable/modules/generated/sklearn.utils.class_weight.compute_class_weight.html},
  addendum = {(accessed: 26.07.2023)}
}


@online{fastf1documentation,
  author   = {Philipp Schaefer},
  title    = {FastF1},
  url      = {https://docs.fastf1.dev/api.html},
  addendum = {(accessed: 20.07.2023)}
}

@article{lime,
  author    = {Marco Tulio Ribeiro and
               Sameer Singh and
               Carlos Guestrin},
  title     = {"Why Should {I} Trust You?": Explaining the Predictions of Any Classifier},
  booktitle = {Proceedings of the 22nd {ACM} {SIGKDD} International Conference on
               Knowledge Discovery and Data Mining, San Francisco, CA, USA, August
               13-17, 2016},
  pages     = {1135--1144},
  year      = {2016}
}

@online{chollet2015keras,
  title     = {Keras},
  author    = {Chollet, Francois and others},
  year      = {2015},
  publisher = {GitHub},
  url       = {https://github.com/fchollet/keras}
}

@online{rnnExample,
  title     = {Structure of RNN},
  author    = {fdeloche},
  year      = {2017},
  publisher = {WikiMedia},
  url       = {https://commons.wikimedia.org/wiki/File:Recurrent_neural_network_unfold.svg}
}