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"Rossi, Giambattista"
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Sports agents and labour markets : evidence from world football
Drawing on extensive empirical research into football around the world, this book explains what agents do, how their role has changed, and why this is important for future sport business.
Book
The relative age effect reversal among the National Hockey League elite
Like many sports in adolescence, junior hockey is organized by age groups. Typically, players born after December 31st are placed in the subsequent age cohort and as a result, will have an age advantage over those players born closer to the end of the year. While this relative age effect (RAE) has been well-established in junior hockey and other professional sports, the long-term impact of this phenomenon is not well understood. Using roster data on North American National Hockey League (NHL) players from the 2008-2009 season to the 2015-2016 season, we document a RAE reversal-players born in the last quarter of the year (October-December) score more and command higher salaries than those born in the first quarter of the year. This reversal is even more pronounced among the NHL \"elite.\" We find that among players in the 90th percentile of scoring, those born in the last quarter of the year score about 9 more points per season than those born in the first quarter. Likewise, elite players in the 90th percentile of salary who are born in the last quarter of the year earn 51% more pay than players born at the start of the year. Surprisingly, compared to players at the lower end of the performance distribution, the RAE reversal is about three to four times greater among elite players.
Journal Article
Relative Age Effect on Labor Market Outcomes for High Skilled Workers - Evidence from Soccer
In sport and education contexts, children are divided into age-groups which are arbitrary constructions based on the admission dates. This age-group system is thought to determine differences in maturity between pupils within the same group, that is, relative age (RA). In turn, these within-age-group maturity differences produce performance gaps, that is, relative age effects (RAE), which might persist and affect the labor market outcome. I analyze the RAE on labor market outcomes using a unique dataset providing information on a particular group of high skilled workers: soccer players in the Italian major soccer league. In line with previous studies, evidence on the existence of RAE in terms of representativeness is found, meaning that players born relatively early in the age-group are over-represented, while players born relatively late are under-represented, even accounting for specific population trends. Moreover, players born relatively late in the age-group receive lower gross wages than players born relatively early. This wage gap seems to increase with age and in the quantile of the wage distribution.
Paper
Why Are Migrants Paid More?
In efficient global labour markets for very high wage workers one might expect wage differentials between migrant and domestic workers to reflect differences in labour productivity. However, using panel data on worker-firm matches in a single industry over a seven year period we find a substantial wage penalty for domestic workers which persists within firms and is only partially accounted for by individual labour productivity. We show that the differential partly reflects the superstar status of migrant workers. This superstar effect is also apparent in migrant effects on firm performance. But the wage differential also reflects domestic workers' preferences for working in their home region, an amenity for which they are prepared to take a compensating wage differential, or else are forced to accept in the face of employer monopsony power which does not affect migrant workers.
Paper
Future Smart Grids Control and Optimization: A Reinforcement Learning Tool for Optimal Operation Planning
The smart grids of the future present innovative opportunities for data exchange and real-time operations management. In this context, it is crucial to integrate technological advancements with innovative planning algorithms, particularly those based on artificial intelligence (AI). AI methods offer powerful tools for planning electrical systems, including electrical distribution networks. This study presents a methodology based on reinforcement learning (RL) for evaluating optimal power flow with respect to various cost functions. Additionally, it addresses the control of dynamic constraints, such as voltage fluctuations at network nodes. A key insight is the use of historical real-world data to train the model, enabling its application in real-time scenarios. The algorithms were validated through simulations conducted on the IEEE 118-bus system, which included five case studies. Real datasets were used for both training and testing to enhance the algorithm’s practical relevance. The developed tool is versatile and applicable to power networks of varying sizes and load characteristics. Furthermore, the potential of RL for real-time applications was assessed, demonstrating its adaptability to online grid operations. This research represents a significant advancement in leveraging machine learning to improve the efficiency and stability of modern electrical grids.
Journal Article
Influential users in Twitter: detection and evolution analysis
In this paper, we study how to detect the most influential users in the microblogging social network platform Twitter and their evolution over time. To this aim, we consider the Dynamic Retweet Graph (DRG) proposed in Amati et al. (2016) and partially analyzed in Amati et al. (IADIS Int J Comput Sci Inform Syst, 11(2) 2016), Amati et al. (2016). The model of the evolution of the Twitter social network is based here on the retweet relationship. In a DRGs, the last time a tweet has been retweeted we delete all the edges representing this tweet. In this way we model the decay of tweet life in the social platform. To detect the influential users, we consider the central nodes in the network with respect to the following centrality measures: degree, closeness, betweenness and PageRank-centrality. These measures have been widely studied in the static case and we analyze them on the sequence of DRG temporal graphs with special regard to the distribution of the 75%\\(75\\%\\) most central nodes. We derive the following results: (a) in all cases, applying the closeness measure results into many nodes with high centrality, so it is useless to detect influential users; (b) for all other measures, almost all nodes have null or very low centrality and (c) the number of vertices with significant centrality are often the same; (d) the above observations hold also for the cumulative retweet graph and, (e) central nodes in the sequence of DRG temporal graphs have high centrality in cumulative graph.
Journal Article
Adequacy of Pain Treatment in Radiotherapy Departments: Results of a Multicenter Study on 2104 Patients (Arise)
Aim: The frequent inadequacy of pain management in cancer patients is well known. Moreover, the quality of analgesic treatment in patients treated with radiotherapy (RT) has only been rarely assessed. In order to study the latter topic, we conducted a multicenter, observational and prospective study based on the Pain Management Index (PMI) in RT Italian departments. Methods: We collected data on age, gender, tumor site and stage, performance status, treatment aim, and pain (type: CP—cancer pain, NCP—non-cancer pain, MP—mixed pain; intensity: NRS: Numeric Rating Scale). Furthermore, we analyzed the impact on PMI on these parameters, and we defined a pain score with values from 0 (NRS: 0, no pain) to 3 (NRS: 7–10: intense pain) and an analgesic score from 0 (pain medication not taken) to 3 (strong opioids). By subtracting the pain score from the analgesic score, we obtained the PMI value, considering cases with values < 0 as inadequate analgesic prescriptions. The Ethics Committees of the participating centers approved the study (ARISE-1 study). Results: Two thousand one hundred four non-selected outpatients with cancer and aged 18 years or older were enrolled in 13 RT departments. RT had curative and palliative intent in 62.4% and 37.6% patients, respectively. Tumor stage was non-metastatic in 57.3% and metastatic in 42.7% of subjects, respectively. Pain affected 1417 patients (CP: 49.5%, NCP: 32.0%; MP: 18.5%). PMI was < 0 in 45.0% of patients with pain. At multivariable analysis, inadequate pain management was significantly correlated with curative RT aim, ECOG performance status = 1 (versus both ECOG-PS3 and ECOG- PS4), breast cancer, non-cancer pain, and Central and South Italy RT Departments (versus Northern Italy).Conclusions: Pain management was less adequate in patients with more favorable clinical condition and stage. Educational and organizational strategies are needed in RT departments to reduce the non-negligible percentage of patients with inadequate analgesic therapy.
Journal Article
Adequacy of Pain Management in Patients Referred for Radiation Therapy: A Subanalysis of the Multicenter ARISE-1 Study
Background: Pain is a prevalent symptom among cancer patients, and its management is crucial for improving their quality of life. However, pain management in cancer patients referred to radiotherapy (RT) departments is often inadequate, and limited research has been conducted on this specific population. This study aimed to assess the adequacy and effectiveness of pain management when patients are referred for RT. Moreover, we explored potential predictors of adequate pain management. Methods: This observational, prospective, multicenter cohort study included cancer patients aged 18 years or older who were referred to RT departments. A pain management assessment was conducted using the Pain Management Index (PMI), calculated by subtracting the pain score from the analgesic score (PMI < 0 indicated inadequate pain management). Univariate and multivariate analyses were performed to identify predictors of adequate pain management. Results: A total of 1042 cancer outpatients were included in the study. The analysis revealed that 42.9% of patients with pain did not receive adequate pain management based on PMI values. Among patients with pain or taking analgesics and referred to palliative or curative RT, 72% and 75% had inadequate or ineffective analgesic therapy, respectively. The odds of receiving adequate pain management (PMI ≥ 0) were higher in patients undergoing palliative RT (OR 2.52; p < 0.001), with worse ECOG-PS scores of 2, 3 and 4 (OR 1.63, 2.23, 5.31, respectively; p: 0.017, 0.002, 0.009, respectively) compared to a score of 1 for those with cancer-related pain (OR 0.38; p < 0.001), and treated in northern Italy compared to central and southern of Italy (OR 0.25, 0.42, respectively; p < 0.001). Conclusions: In this study, a substantial proportion of cancer patients referred to RT departments did not receive adequate pain management. Educational and organizational strategies are necessary to address the inadequate pain management observed in this population. Moreover, increasing the attention paid to non-cancer pain and an earlier referral of patients for palliative RT in the course of the disease may improve pain response and treatment outcomes.
Journal Article
Sapling Similarity: a performing and interpretable memory-based tool for recommendation
Many bipartite networks describe systems where an edge represents a relation between a user and an item. Measuring the similarity between either users or items is the basis of memory-based collaborative filtering, a widely used method to build a recommender system with the purpose of proposing items to users. When the edges of the network are unweighted, the popular common neighbors-based approaches, allowing only positive similarity values, neglect the possibility and the effect of two users (or two items) being very dissimilar. Moreover, they underperform with respect to model-based (machine learning) approaches, although providing higher interpretability. Inspired by the functioning of Decision Trees, we propose a method to compute similarity that allows also negative values, the Sapling Similarity. The key idea is to look at how the information that a user is connected to an item influences our prior estimation of the probability that another user is connected to the same item: if it is reduced, then the similarity between the two users will be negative, otherwise, it will be positive. We show that, when used to build memory-based collaborative filtering, Sapling Similarity provides better recommendations than existing similarity metrics. Then we compare the Sapling Similarity Collaborative Filtering (SSCF, a hybrid of the item-based and the user-based) with state-of-the-art models using standard datasets. Even if SSCF depends on only one straightforward hyperparameter, it has comparable or higher recommending accuracy, and outperforms all other models on the Amazon-Book dataset, while retaining the high explainability of memory-based approaches.
Paper