Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2309.05783

A New Framework to Estimate Return on Investment for Player Salaries in the National Basketball Association

Jackson P. Lautier
View original source ↗Read source document ↗Export metadata ↓

Abstract

The National Basketball Association (NBA) imposes a player salary cap. It is therefore useful to develop tools to measure the relative realized return of a player's salary given their on court performance. Very few such studies exist, however. We thus present the first known framework to estimate a return on investment (ROI) for NBA player contracts. The framework operates in five parts: (1) decide on a measurement time horizon, such as the standard 82-game NBA regular season; (2) calculate the novel game contribution percentage (GCP) measure we propose, which is a single game summary statistic that sums to unity for each competing team and is comprised of traditional, playtype, hustle, box outs, defensive, tracking, and rebounding per game NBA statistics; (3) estimate the single game value (SGV) of each regular season NBA game using a standard currency conversion calculation; (4) multiply the SGV by the vector of realized GCPs to obtain a series of realized per-player single season cash flows; and (5) use the player salary as an initial investment to perform the traditional ROI calculation. We illustrate our framework by compiling a novel, sharable dataset of per game GCP statistics and salaries for the 2022-2023 NBA regular season. A scatter plot of ROI by salary for all players is presented, including the top and bottom 50 performers. Notably, missed games are treated as defaults because GCP is a per game metric. This allows for break-even calculations between high-performing players with frequent missed games and average performers with few missed games, which we demonstrate with a comparison of the 2023 NBA regular seasons of Anthony Davis and Brook Lopez. We conclude by suggesting uses of our framework, discussing its flexibility through customization, and outlining potential future improvements.

Explore related subjects

q-fin.GNstat.AP

Keep this discovery

BibTeXRIS

Jackson P. Lautier. 2023-09-11. A New Framework to Estimate Return on Investment for Player Salaries in the National Basketball Association. https://doi.org/10.1002/asmb.70020

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

AI for AI: Optimizing Additional Infrastructure Build-out to Power Artificial Intelligence Data Centers

The twenty-first century's transformative technology, artificial intelligence, is increasingly constrained by the twentieth century's transformative technology, the electricity grid. Rapid growth in electricity demand from data centers is leading to higher electricity prices, without a compensating supply-side response. We develop a framework linking data-center load growth, available generation capacity, and market-clearing prices to understand this phenomenon. We first analyze a deterministic model to show how differing estimates of demand and supply growth rates affect prices. We then model the expansion of new data centers and their associated electricity demand, together with build-outs of new electricity supply, as stochastic processes,resulting in probabilistic distributions of supply, demand, and prices rather than a single forecast. Finally, we formulate generation expansion as a stochastic control problem in which a revenue-maximizing investor dynamically chooses the intensity of supply-side investments. The analysis highlights a central challenge of the data-center build-out: even when rapid demand growth increases the need for new generation, the uncertainties related to load forecasts, development execution risks, and value cannibalization from overbuilding capacity may weaken incentives to invest at the pace required to keep electricity prices stable.

q-fin.GN↗
arXiv2026

Measuring DeFi Risk

Decentralized finance (DeFi) lending has grown from nonexistent in 2017 to nearly 40 billion US Dollars in deposited funds in May 2022. Using cryptocurrency as collateral, the platforms match speculative margin trading with yield-seeking depositors lending coins pegged to the dollar (stable coins). Depositors receive claims guaranteed by a basket of collateral, akin to new stable coins. We develop a framework requiring only knowledge of aggregate deposits and borrowings to measure overall system risks to lenders and borrowers. Using evidence from major protocols, the measures identify an increase in system fragility beyond prudent levels around mid 2021, with a potential loss of peg for extreme variations in coin prices. Overall, the model offers an easily implementable aggregate risk metric capturing the perspectives of synthetic investors and offers early warning signals as the industry is moving from deposits guaranteed by collateral to fiat money.

q-fin.GN↗
arXiv2026

Historical Reflections on Interest Rates and the Emergence of the Yield Curve

This text grew out of a historical introduction initially written for a study of interest rates in cryptocurrency markets. The difficulty of defining a term structure for a currency without a conventional bond market led naturally to a more fundamental question: under what historical conditions does a yield curve become observable at all? Credit existed long before modern money, and interest-bearing loans are documented as early as ancient Mesopotamia. For much of history, the surviving evidence lacks the institutional features that facilitate reliable comparisons of interest rates by maturity: standardised debt instruments, sufficiently homogeneous borrowers, regular issuance over a range of maturities, observable market prices, and liquid secondary markets. We trace the gradual emergence of these conditions from ancient Mesopotamia, Greece, and Rome, through medieval and early modern Europe, to the development of modern sovereign debt markets in the nineteenth and twentieth centuries.

q-fin.GN↗