A. M. Petersen, O. Penner
Renormalizing individual performance metrics for cultural heritage management of sports records (pdf)(Pre-print OA Version)
Chaos, Solitons & Fractals 136, 109821 (2020). DOI:10.1016/j.chaos.2020.109821
Abstract
Individual performance metrics are commonly used to compare players from different eras. However, such cross-era comparison is often biased due to significant changes in success factors underlying player achievement rates (e.g. performance enhancing drugs and modern training regimens). Such historical comparison is more than fodder for casual discussion among sports fans, as it is also an issue of critical importance to the multi- billion dollar professional sport industry and the institutions (e.g. Hall of Fame) charged with preserving sports history and the legacy of outstanding players and achievements. To address this cultural heritage management issue, we report an objective statistical method for renormalizing career achievement metrics, one that is particularly tailored for common seasonal performance metrics, which are often aggregated into summary career metrics - despite the fact that many player careers span different eras. Remarkably, we find that the method applied to comprehensive Major League Baseball and National Basketball Association player data preserves the overall functional form of the distribution of career achievement, both at the season and career level. As such, subsequent re-ranking of the top-50 all-time records in MLB and the NBA using renormalized metrics indicates reordering at the local rank level, as opposed to bulk reordering by era. This local order refinement signals time-independent mechanisms underlying seasonal and cumulative achievement in professional sports, meaning that appropriately renormalized achievement metrics can be used to compare players from eras with different season lengths, team strategies, rules - and possibly even different sports.
A. M. Petersen, M. Riccaboni, H. E. Stanley, F. Pammolli.
Persistence and Uncertainty in the Academic Career (pdf)
Proceedings of the National Academy of Sciences USA 109, 5213 - 5218 (2012). DOI: 10.1073/pnas.1121429109
Abstract
Recent shifts in the business structure of universities and a bottleneck
in the supply of tenure track positions are two issues that threaten to change the
longstanding patronage system in academia.
Understanding how institutional changes within academia
may affect the overall potential of science requires a better quantitative understanding of how careers evolve over
time. Since knowledge spillovers, cumulative advantage, and collaboration are distinctive features of the academic
profession, the employment relationship should
be designed to account for these factors. We quantify the impact of these factors in the production n_i(t) of a
given scientist i by analyzing the longitudinal career data of 300 scientists and
compare our results with 21,156 sports careers comprising a non-academic labor force.
The increase in the typical size of scientific collaborations has led to the increasingly difficult task of allocating funding and assigning recognition.
We use measures of the scientific collaboration radius, which can change dramatically over the course of a career, to provide insight into the role of collaboration in productio
n efficiency.
We introduce a model of proportional growth to provide insight into the complex relation between knowledge spillovers,
competition, and uncertainty at the individual scale.
Our model shows that high competition levels can make careers vulnerable to ``sudden death'' termination relatively early in the career as a result of negative production fluctuations and not necessarily due to lack of individual persistence.
- Short-term contracts may hinder young scientists, PNAS Highlight
A. M. Petersen, W-S. Jung, J-S. Yang, H. E. Stanley.
Quantitative and Empirical demonstration of the Matthew Effect in a study of Career Longevity (pdf)
Proceedings of the National Academy of Sciences USA 108, 18-23 (2011). DOI: 10.1073/pnas.1016733108
Abstract
In many competitive systems, there are typically only few "big winners." This largely reflects the everyday fact that
obtaining future opportunities often depends on an individual's record of achievement since employment opportunities are limited to a finite number of competitors.
We solve exactly a longevity model which predicts the distribution of career length P(x) for professions characterized by high selectivity and uncertainty. We confirm the model's prediction for P(x) using extensive empirical data for the careers of both scientists (publishing in high-impact journals such as Nature, Science, etc.) and professional athletes (playing in MLB, NBA, Premier League, and Korean Professional Baseball). This study uncovers a remarkably simple statistical law which describes the frequencies of the extremely short careers of `one-hit wonders' as well as the extremely long careers of the `iron-horses'. Our model highlights the importance of early career development, showing that many careers are stunted by the relative disadvan- tage associated with inexperience.
A. M. Petersen, O. Penner.
A method for the unbiased comparison of MLB and NBA career statistics across era (pdf)
Presented at the MIT Sloan Sports Analytics Conference 2012 (2012).
Abstract
An extension of "Methods for detrending success metrics to account for inflationary and deflationary factors" to National Basketball Association (NBA) career statistics. Includes extensive tables listing re-ranked top-50 achievements for points, rebounds and assists, at the season and career level.
A. M. Petersen, O. Penner, H. E. Stanley.
Methods for detrending success metrics to account for inflationary and deflationary factors (pdf) Eur. Phys. J. B 79, 67-78 (2011). DOI: 10.1140/epjb/e2010-10647-1
Pre-print title: Detrending career statistics in professional Baseball: accounting for the Steroids Era and beyond Abstract
We compare both career and seasonal achievements of 130+ years of baseball players, (e.g., addressing the question of who effectively hit more home runs -- Babe Ruth or Barry Bonds?), using statistical methods to account for time-dependent factors that inflate success measures. We provide non-technical top-50 record tables for career HR, H, RBI, W, K and season HR, H, RBI, K, focussing on the accessible measures found in newspaper box-scores and on the back of baseball cards.
- Complexity Theory and the National Baseball Hall of Fame, the European Physical Journal News Highlights
- New Statistical Method Ranks Sports Players From Different Eras, MIT Technology Review
- Boston University clip, The Daily Free Press
- A Physics Curveball, arts&sciences Fall 2010 Magazine (Annual BU Research Highlight)
- Baseball Greats Reranked, BU Today, April 8, 2011
A. M. Petersen, W-S. Jung, H. E. Stanley.
On the distribution of career longevity and the evolution of home run prowess in professional baseball (pdf)
Europhysics Letters 83, 50010 (2008). DOI: 10.1209/0295-5075/83/50010
Abstract
How is it that 3% of all fielders finish their career with one at-bat and 3% of all pitchers finish their career with less than one inning pitched; Yet, there are also some careers that span more than 10,000 at-bats and 3,000 innings pitched? Analyzing every Major League Baseball player career over the 80-year period 1920-2000, we find a beautiful statistical law which describes both the extremely short careers of `one-hit wonders' as well as the extremely long careers of the `iron-horses'. Furthermore, analyzing home run rates, we find evidence consistent with performance enhancing drugs during the `Steroids Era' of the 1990's and 2000's.
Presentations:
Ascent in competitive arenas: From Fenway Park to Mass Ave (2013) (pdf)
Presented at the "Science of Success" Symposium, Northeastern Univ. & IQSS Harvard University
Beyond the Asterisk* : Adjusting for Performance Inflation in Professional Sports (2012) (pdf)
presented at the "Sabermetrics, Scouting and the Science of Baseball" weekend seminar for the benefit of the Jimmy Fund
Quantifying statistical regularities in the career achievements of scientists and professional athletes (2012) (pdf)
