@book {1225,
	title = {Sar{\`o} Franco - Vita di Franco Turini, executive chef dell{\textquoteright}Universit{\`a} di Pisa},
	year = {2019},
	pages = {20},
	publisher = {Pisa University Press},
	organization = {Pisa University Press},
	address = {Pisa, Italy},
	abstract = {Chi {\`e} Franco Turini? Come molti sanno, uno dei pionieri dell{\textquoteright}informatica italiana. Ma non {\`e} questa la domanda che ci interessa. Quella a cui questo breve saggio si propone di rispondere {\`e} una questione molto pi{\`u} importante: chi avrebbe voluto essere Franco Turini?
In questo scritto, la vita e la carriera di Turini vengono ripercorse alla luce della sua vera, unica e irredimibile passione: la cucina. In un intreccio romanzesco, denso di colpi di scena e assolutamente falso e tendenzioso, il contributo di Franco Turini all{\textquoteright}informatica e all{\textquoteright}intelligenza artificiale si dipana, indissolubilmente intrecciato alla sua passione per i fornelli, attraverso le molte intuizioni geniali che lo hanno colpito mentre cucinava.},
	issn = {978-88-3339-252-3},
	url = {https://store.streetlib.com/it/marco-malvaldi/saro-franco-9788833392523/},
	author = {Marco Malvaldi}
}
@proceedings {770,
	title = {The harsh rule of the goals: data-driven performance indicators for football teams},
	year = {2015},
	abstract = {{\textemdash}Sports analytics in general, and football (soccer in
USA) analytics in particular, have evolved in recent years in an
amazing way, thanks to automated or semi-automated sensing
technologies that provide high-fidelity data streams extracted
from every game. In this paper we propose a data-driven
approach and show that there is a large potential to boost the
understanding of football team performance. From observational
data of football games we extract a set of pass-based performance
indicators and summarize them in the H indicator. We observe a
strong correlation among the proposed indicator and the success
of a team, and therefore perform a simulation on the four major
European championships (78 teams, almost 1500 games). The
outcome of each game in the championship was replaced by a
synthetic outcome (win, loss or draw) based on the performance
indicators computed for each team. We found that the final
rankings in the simulated championships are very close to the
actual rankings in the real championships, and show that teams
with high ranking error show extreme values of a defense/attack
efficiency measure, the Pezzali score. Our results are surprising
given the simplicity of the proposed indicators, suggesting that
a complex systems{\textquoteright} view on football data has the potential of
revealing hidden patterns and behavior of superior quality.},
	url = {https://www.researchgate.net/profile/Luca_Pappalardo/publication/281318318_The_harsh_rule_of_the_goals_data-driven_performance_indicators_for_football_teams/links/561668e308ae37cfe4090a5d.pdf},
	author = {Paolo Cintia and Luca Pappalardo and Dino Pedreschi and Fosca Giannotti and Marco Malvaldi}
}
