CARLO AMBROGIO FAVERO

ABSTRACT
In professional basketball, player evaluation often relies on aggregated box score statistics. The Performance Index Rating (PIR), widely used in leagues like the NBA and EuroLeague, lacks a clear statistical foundation and may not reliably reflect a player’s contribution to team success. This paper revisits WINSCORE, an approach proposed by Berri et al.(2006), which links individual player statistics directly to team outcomes, offering a more interpretable and theoretically grounded measure of player value.

We reinterpret WINSCORE as a model-based procedure and validate its application in measuring team and player performance using data from both the NBA and EuroLeague. In doing so, we highlight its advantages over traditional metrics.

Finally, the model-based interpretation of WINSCORE allows to introduce a new modified  WINSCORE-based method to evaluate player performance in individual games, illustrated with EuroLeague data. This extension provides a useful tool for coaches and analysts seeking game-level insights into player efficiency. 

JEL-Code: Z20, C51, C52 

Keywords: Player Performance Evaluation, Basketball Analytics, EuroLeague, WINSCORE Model, Performance Index Rating (PIR) 

The data-set for  associated to the replication codes made available here  is too large for upload on this webpage please send a mail to carlo.favero@unibocconi.it to receive it. 

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Abstract: This paper investigates the efficiency of time-outs in the EuroLeague, using Play-by-Play (PBP) and Box Score data from the 2021–22 to 2023–24 regular seasons. Our analysis of PBP data indicates that time-outs help mitigate losses during critical moments of a game but do not necessarily change its overall outcome. Additionally, evidence from Box Score data suggests that time-outs do not provide additional explanatory power beyond the standard four-factor model in determining team performance over the course of a season. 

Keywords: Time-Out Efficiency, Euroleague Play-by-Play data, Euroleague Box-Score data. 

The data set associate to the replication codes in R is too long to be uploaded on this webpage please send an e-mail to carlo.favero@unibocconi.it to obtain it. 

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This Revision July 2023

According to no-arbitrage, risk-adjusted returns should be unpredictable. Using several prominent factor models and a large cross-section of anomalies, we uncover a striking fact: past pricing errors predict future risk-adjusted anomaly returns. We show that past pricing errors can be interpreted as deviations of an anomaly price from the mean-variance efficient portfolio. Price deviations constitute an anomaly-specific predictor, endogenous to the given factor model, thus providing direct evidence for conditional misspecification. A zero-cost investment strategy using price deviations generates positive alphas.
     Our findings suggest that cross-sectional models should incorporate information in prices to capture the time-series dynamics of returns.

Keywords: Factor Models, Return Predictability, Conditional Misspecification, Mean-Variance
Portfolio, SDF
JEL codes: C38, G12, G17.

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Version March 2023

This paper studies the link between macroeconomic variables and asset prices. We document two
novel empirical facts. First, the price of well-known systematic (characteristics-based) risk factors,
like SMB and HML, is anchored to macroeconomic trends related to inflation and real economic
activity. Second, factor prices temporarily deviate from their macro trends generating factor risk
premia predictability. Intuitively, future factor returns are low (high) when prices are above (below)
their level warranted by the macroeconomy. We provide evidence for this mechanism for different
factors, both in- and out-of-sample. This predictability translates into economic gains from factor
timing for investors. Finally, our approach leads to an estimated stochastic discount factor that
displays sizable time variation when benchmarked against standard long-run risk or habit models.

Keywords: Factor Models, Macro–Finance, Factor Timing, SDF, Risk Management.
JEL codes: C38, G11, G12, G17.

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ABSTRACT

We provide, calibrate and test a realistic model of the spread of SARS-Cov-2 in an
economy with different risks related to age and sectors. The model considers hospital
congestion and response of individuals adjusting their behavior to the virus' spread.
We measure precisely the size of these effects using real data for Italy on intensive care
capacity and mobility decisions; thus our claim is that the tradeoffs we estimate are
quantitatively, rather than qualitatively, approximately correct.
We characterize the policies of containment of the epidemic that are efficient with
respect to number of fatalities and GDP loss. Prudent policies of gradual return to
work may save many lives with limited economic costs, as long as they differentiate
by age group and risk sector. More careful behavior of individuals induced by the
perceived cost of infection may contribute to further reduce fatalities.

 JEL-Code: I12, I18, D6, H84

 Keywords: Covid-19,SARS-Cov-2 SEIR model, post lockdown policies.
 

Press coverage: Il Foglio; Promarket; Bloomberg;  Liberioltre; Sole24ore; Politico; Corriere della sera; Financecue

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This Version November 2022

ABSTRACT

We study the effects of fiscal consolidations in the United States and their propagation in the production network. We use a narrative approach to identify fiscal adjustments which are exogenous to output fluctuations. Then we apply spatial econometric techniques to separate the total effect of fiscal adjustments into a direct and network component. We find that fiscal adjustments based on increased taxation  are more recessionary than those based on spending cuts. Moreover, one quarter of the difference in their total output effect is explained by the stronger network propagation of taxes relative to government spending.

Keywords: industrial networks, fiscal adjustment plans, output growth, applied spatial econometrics.

JEL codes : E60, E62.

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This revision July 2018

Abstract: We investigate the macroeconomic effects of fiscal consolidations based upon government spending cuts, transfers cuts and tax hikes. We extend a narrative dataset of fiscal consolidations, finding details on over 3500 measures. Government spending and transfer cuts are much less harmful than tax hikes. Standard New Keynesian models match our results when fiscal shocks are persistent. Wealth effects on aggregate demand mitigates the impact of a persistent spending cut. Static distortions caused by persistent tax hikes cause larger shifts in aggregate supply under sticky prices. This channel explains different sizes of multipliers found in fiscal stimuli compared to consolidation plans.

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