Journal of Sports Analytics - Volume 2, issue 2

Authors: Graham, James | Mayberry, John

Article Type: Research Article

Abstract: Defensive fouls play an important role in elite men’s water polo generating over half of all goals. Despite their importance, little is known about the relationship between foul calling patterns and other game-state variables in the sport. Here we apply a sequence of hierarchical mixed logistic regression models on data from major tournaments in 2012–2014 to study such relationships and find a number of significant biases in foul calling rates. Offensive teams who are winning/tied are about 31% less likely to draw a defensive foul and 32% more likely to get called for an offensive foul than teams who are …losing. The magnitude of losing team bias tends to increase over the course of a game, but is not significantly affected by the size of the lead. A team’s odds of getting called for a foul also increase by about 10% for each consecutive goal scored or foul called in their favor. These biases persist across different offensive and defensive tactical decisions and tournaments suggesting that they are widespread and that it is referees, rather than teams, who are responsible for a lack of independence in water polo foul calling rates. Show more

Keywords: Water polo analytics, referee bias, logistic regression

DOI: 10.3233/JSA-160019

Citation: Journal of Sports Analytics, vol. 2, no. 2, pp. 61-71, 2016

Authors: Trombley, Matthew J.

Article Type: Research Article

Abstract: In this study I present cause for concern that Major League Soccer teams with an artificial grass (AG) home playing surface may possess an advantage over visiting teams used to playing on grass. I develop a theoretical model predicting the outcomes we should observe if teams with AG possess a competitive advantage. Using match-level data from the 2011–2014 Major League Soccer regular seasons, I find that conditional on attendance, distance traveled, and the difference in talent between the two teams, AG is uncorrelated with the final outcome of a match and the number of goals scored by each team. This …suggests that AG does not affect the competitive balance in Major League Soccer. Show more

Keywords: Association football, artificial turf, travel, attendance, match outcomes, restricted generalized Poisson, ordered logit

DOI: 10.3233/JSA-160020

Citation: Journal of Sports Analytics, vol. 2, no. 2, pp. 73-87, 2016

Authors: Dendir, Seife

Article Type: Research Article

Abstract: The peak age for professional soccer players is of significant interest to coaches, managers and executives alike. The evidence so far is predominantly anecdotal and subjective. This paper formally analyzes the peak or optimal age in professional men’s soccer using performance ratings of players in the four major top flight leagues of Europe. WhoScored.com ratings from 2010/11 to 2014/15 are used. The analysis is done for all outfield players, separately by field position. In addition to simple age distribution and bivariate approaches, a player fixed effects model that accounts for potential selection bias is estimated. The results show that the …average professional soccer player peaks between the ages of 25 and 27. In the preferred models, the average forward peaks at 25, whereas the typical defender peaks at 27. For midfielders, the estimated peak age varies by model but still occurs in the 25–27 age band. Defenders experience relatively minimal curvature in the age-performance relationship. Further results show that peak age may vary directly with ability. Show more

Keywords: Age-performance relationship, aging curve, peak age, optimal age, soccer

DOI: 10.3233/JSA-160021

Citation: Journal of Sports Analytics, vol. 2, no. 2, pp. 89-105, 2016

Authors: Bruce, Scott

Article Type: Research Article

Abstract: Player tracking data provides a platform for the creation of new basketball statistics that can dramatically improve the ability to evaluate and compare player performance. However, the increasing size of this new data source presents challenges in how to efficiently analyze the data and interpret findings. A scalable analytical framework is needed that can effectively reduce the dimensionality of the data while retaining the ability to compare player performance. In this paper, Principal Component Analysis (PCA) is used to identify four components accounting for 68% of the variation in player tracking data from the 2013-2014 regular season. The most …influential statistics on these new dimensions are used to construct intuitive, practical interpretations. In this high variance, low dimensional space, comparisons across any or all of the principal components are possible to evaluate characteristics that make players and teams similar or unique. A simple measure of similarity between player or team statistical profiles based on the four principal components is also constructed. The Statistical Diversity Index (SDI) allows for quick and intuitive comparisons using the entirety of the player tracking data. As new statistics emerge, this framework is scalable as it can incorporate existing and new data sources by reconstructing principal component dimensions and SDI for improved comparisons. Using principal component scores and SDI, several use cases are presented for improved personnel management. Team principal component scores are used to quickly profile and evaluate team performance, more specifically how New York’s lack of ball movement negatively impacted success despite high average scoring efficiency as a team. SDI is used to identify players across the NBA with the most similar statistical performances to specific players. All-Star Tony Parker and shooting specialist Anthony Morrow are used as two examples and presented with in-depth comparisons to similar players using principal component scores and player tracking statistics. This approach can be used in salary negotiations, free agency acquisitions and trades, role player replacement, and more. Show more

Keywords: Principal component analysis, NBA player tracking data, statistical diversity index, dimension reduction, personnel management, National Basketball Association

DOI: 10.3233/JSA-160022

Citation: Journal of Sports Analytics, vol. 2, no. 2, pp. 107-119, 2016

Authors: Coleman, B. Jay | DuMond, J. Michael | Lynch, Allen K.

Article Type: Research Article

Abstract: We extend prior research on the at-large bid decisions of the NCAA Men’s Basketball Committee, and estimate an eight-factor probit model that would have correctly identified 178 of 179 at-large teams in-sample over the 2009–2013 seasons, and correctly predicted 68 of 72 bids when used out of-sample for 2014 and 2015. Such performance is found to compare favorably against the projections of upwards of 136 experts and other methodologies over the same time span. Predictors included in the model are all easily computed, and include the RPI ranking (using the former version of the metric), losses below 0.500 in-conference, …wins against the RPI top 25, wins against the RPI second 25, games above 0.500 against the RPI second 25, games above 0.500 against teams ranked 51–100 in RPI, road wins, and being in the Pac-10/12. That Pac-10/12 membership improved model fit and predictive accuracy is consistent with prior literature on bid decisions from 1999–2008. Show more

Keywords: College basketball, probit, group decisions, voting, committees

DOI: 10.3233/JSA-160023

Citation: Journal of Sports Analytics, vol. 2, no. 2, pp. 121-132, 2016

Article Type: Other

Citation: Journal of Sports Analytics, vol. 2, no. 2, pp. 133-133, 2016